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NEXT GENERATION COMPUTING 

AND BIG DATA ANALYTICS 

JOINT HEARING 

BEFORE THE

SUBCOMMITTEE ON RESEARCH & 

SUBCOMMITTEE ON TECHNOLOGY 

COMMITTEE ON SCIENCE, SPACE, AND 

TECHNOLOGY 

HOUSE OF REPRESENTATIVES 

ONE HUNDRED THIRTEENTH CONGRESS 

FIRST SESSION 

WEDNESDAY, APRIL 24, 2013 

Serial No. 113–22 

Printed for the use of the Committee on Science, Space, and Technology 

( 

Available via the World Wide Web: http://science.house.gov 
(II) 

COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY 

HON. LAMAR S. SMITH, Texas, Chair 

DANA ROHRABACHER, California 

RALPH M. HALL, Texas 

F. JAMES SENSENBRENNER, JR., 

Wisconsin 

FRANK D. LUCAS, Oklahoma 

RANDY NEUGEBAUER, Texas 

MICHAEL T. MCCAUL, Texas 

PAUL C. BROUN, Georgia 

STEVEN M. PALAZZO, Mississippi 

MO BROOKS, Alabama 

RANDY HULTGREN, Illinois 

LARRY BUCSHON, Indiana 

STEVE STOCKMAN, Texas 

BILL POSEY, Florida 

CYNTHIA LUMMIS, Wyoming 

DAVID SCHWEIKERT, Arizona 

THOMAS MASSIE, Kentucky 

KEVIN CRAMER, North Dakota 

JIM BRIDENSTINE, Oklahoma 

RANDY WEBER, Texas 

CHRIS STEWART, Utah 

VACANCY 

EDDIE BERNICE JOHNSON, Texas 

ZOE LOFGREN, California 

DANIEL LIPINSKI, Illinois 

DONNA F. EDWARDS, Maryland 

FREDERICA S. WILSON, Florida 

SUZANNE BONAMICI, Oregon 

ERIC SWALWELL, California 

DAN MAFFEI, New York 

ALAN GRAYSON, Florida 

JOSEPH KENNEDY III, Massachusetts 

SCOTT PETERS, California 

DEREK KILMER, Washington 

AMI BERA, California 

ELIZABETH ESTY, Connecticut 

MARC VEASEY, Texas 

JULIA BROWNLEY, California 

MARK TAKANO, California 

ROBIN KELLY, Illinois 

SUBCOMMITTEE ON RESEARCH

HON. LARRY BUCSHON, Indiana, Chair 

STEVEN M. PALAZZO, Mississippi 

MO BROOKS, Alabama 

STEVE STOCKMAN, Texas 

CYNTHIA LUMMIS, Wyoming 

JIM BRIDENSTINE, Oklahoma 

LAMAR S. SMITH, Texas 

DANIEL LIPINSKI, Illinois 

ZOE LOFGREN, California 

AMI BERA, California 

ELIZABETH ESTY, Connecticut 

EDDIE BERNICE JOHNSON, Texas 

SUBCOMMITTEE ON TECHNOLOGY

HON. THOMAS MASSIE, Kentucky, Chair 

JIM BRIDENSTINE, Oklahoma 

RANDY HULTGREN, Illinois 

DAVID SCHWEIKERT, Arizona 

LAMAR S. SMITH, Texas 

FREDERICA S. WILSON, Florida 

SCOTT PETERS, California 

DEREK KILMER, Washington 

EDDIE BERNICE JOHNSON, Texas 
(III) 

C O N T E N T S 

Wednesday, April 24, 2013 

Page 

Witness List ............................................................................................................. 2 

Hearing Charter ...................................................................................................... 3 

Opening Statements 

Statement by Representative Larry Bucshon, Chairman, Subcommittee on 

Research, Committee on Science, Space, and Technology, U.S. House of 

Representatives .................................................................................................... 8 

Written Statement ............................................................................................ 9 

Statement by Representative Daniel Lipinski, Ranking Minority Member, 

Subcommittee on Research, Committee on Science, Space, and Technology, 

U.S. House of Representatives ............................................................................ 10 

Written Statement ............................................................................................ 11 

Statement by Representative Thomas Massie, Chairman, Subcommittee on 

Technology, Committee on Science, Space, and Technology, U.S. House 

of Representatives ................................................................................................ 12 

Written Statement ............................................................................................ 13 

Statement by Representative Frederica S. Wilson, Ranking Minority Member, 

Subcommittee on Technology, Committee on Science, Space, and Technology, U.S. House of Representatives ............................................................... 13 

Written Statement ............................................................................................ 14 

Witnesses: 

Dr. David McQueeney, Vice President, Technical Strategy and Worldwide 

Operations, IBM Research 

Oral Statement ................................................................................................. 16 

Written Statement ............................................................................................ 18 

Dr. Michael Rappa, Director, Institute for Advanced Analytics, Distinguished 

University Professor, North Carolina State University 

Oral Statement ................................................................................................. 26 

Written Statement ............................................................................................ 28 

Dr. Farnam Jahanian, Assistant Director for the Computer and Information 

Science and Engineering (CISE) Directorate, National Science Foundation 

Oral Statement ................................................................................................. 36 

Written Statement ............................................................................................ 38 

Discussion ................................................................................................................. 55 

Appendix I: Answers to Post-Hearing Questions 

Dr. Michael Rappa, Director, Institute for Advanced Analytics, Distinguished 

University Professor, North Carolina State University .................................... 76 

Dr. Farnam Jahanian, Assistant Director for the Computer and Information 

Science and Engineering (CISE) Directorate, National Science Foundation .. 79 
Page

IV 

Appendix II: Additional Material for the Record 

IDC IVIEW report, The Digital Universe in 2020: Big Data, Bigger Digital 

Shadows, and Biggest Growth in the Far East, submitted by Representative 

Derek Kilmer, Subcommittee on Technology, Committee on Science, Space, 

and Technology, U.S. House of Representatives ............................................... 86 
(1) 

NEXT GENERATION COMPUTING AND BIG 

DATA ANALYTICS 

WEDNESDAY, APRIL 24, 2013 

HOUSE OF REPRESENTATIVES, 

SUBCOMMITTEE ON RESEARCH & 

SUBCOMMITTEE TECHNOLOGY

COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY, 

Washington, D.C. 

The Subcommittees met, pursuant to call, at 10:04 a.m., in Room 

2318 of the Rayburn House Office Building, Hon. Larry Bucshon 

[Chairman of the Subcommittee on Research] presiding. 
2 

LAMAR S. SMITH, Texas 

CHA!RMAN 

Q:ongrEss of thE tlnitro ~tatm 

iRonsc of 'Rcprcsmtatincs 

COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY 

2321 RAYBURN HOUSE OFFICE BUILDING 

WASH!NGTON, D,C 20515-6301 

1202) 225-6371 www.sc.ience.ho~$(l.gov 

EDDIE BERNICE JOHNSON, Texas 

RANKING MEMBER 

Subcommittees on Research and Technology Hearing 

Next Generation Computing and Big Data Analytics 

Wednesday, April 24, 2013 

10:00 a.m. - 12:00 p.m. 

2318 Rayburn House Office Building 

Witnesses 

Dr. David McQueeney, Vice President, Technical Strategy and Worldwide Operations, IBM Research 

Dr. Michael Rappa, Executive Director of the Institute for Advanced Analytics, Distinguished 

University Professor, North Carolina State University 

Dr. Farnam Jahanian, Assistant Director for the Computer and Information Science and Engineering 

(ClSE) Directorate, National Science Foundation (NSF) 
3 

Purpose 

U.S. HOUSE OF REPRESENTATIVES 

COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY 

SUBCOMMITTEES ON RESEARCH AND TECHNOLOGY 

HEARING CHARTER 

Next Generation Computing and Big Data Analytics 

Wednesday, April 24, 2013 

10:00 a.m. - 12:00 p.m. 

2318 Rayburn House Office Building 

On Wednesday, April 24, 2013, the House Committee on Science, Space, and Technology's 

Research and Technology Subcommittees will examine how advancements in information 

technology and data analytics enable private and public sector organizations to utilize mass 

volumes of data to provide greater value to their customers and citizens, spurring new product 

and service innovations. The hearing will focus on innovative data analytics capabilities, 

research and development efforts, management challenges, and workforce development issues 

associated with the "Big Data" phenomenon. 

Wituesses 

• Dr. David McQueeuey, Vice President, Technical Strategy and Worldwide Operations, 

IBM Research 

• Dr. Michael Rappa, Executive Director of the Institute for Advanced Analytics, 

Distinguished University Professor, North Carolina State University 

• Dr. Farnam Jahanian, Assistant Director for the Computer and Information Science and 

Engineering (CISE) Directorate, National Science Foundation (NSF) 

Overview 

Unprecedented volumes of complex and diverse data sets are being generated daily across a 

range of industries and public sector organizations. The term "Big Data" encompasses the 

challenge of collecting, analyzing and disseminating the massive data sets that are currently 

being generated and stored. Private industry and government officials are seeking ways to 

harness, analyze, and exploit these data sets in ways that provide greater value to their customers 

and citizens. While Big Data is a relatively new term, the problem is not. What is changing is 

both the volume of the data and the pressure to find technological solutions to managing, storing, 

and utilizing that data. 

The McKinsey Global Institute estimated that global enterprises stored more than seven exabytes 

of new data on disk drives in 2010, and that consumers stored more than six exabytes on 

personal computers and laptops.! An Exabyte is 1018 bytes or one billion gigabytes. As a frame 

, Big data: The next/rontier for innovation. competition, and productivity, McKinsey Global Institute, May 201 I, 

McKinsey and Company. 
4 

of reference, one exabyte of data is more than 4,000 times the amount of data stored in the U.S. 

Library of Congress. 

Given the evolution of computing power and analytical capabilities, overcoming the challenge of 

data management presents significant need for technological innovation. High performance 

computing can process mass, complex sets of data at a greater rate; mathematicians and 

statisticians are developing new algorithms to analyze data; and data analytics professionals are 

employing new techniques to extract value from data. 

Big Data has profound implications for a range of industries. For example, health care data can 

enable care providers to monitor health trends and evaluate different treatments, energy data can 

inform power distribution creating greater efficiencies, transportation data can be used to 

mitigate traffic congestion, and information technology data can identify potential cyber threats. 

In addition, technological advances allow scientists to both collect and analyze data at a 

significantly faster rate. Examples include advancements in human genome sequencing, digital 

astronomy data, and particle physics. 

Industry and Big Data 

Big Data represents a significant growth area for private industry. In recent years, industry 

spending on data analytics and management has increased approximately 10 percent a year.2 

Companies utilize data analytics to manage supply chains, target marketing based on user 

preferences, provide airline fare prediction services for consumers, and reduce costs by 

identifying operating inefficiencies, among a multitude of other uses. 

Information Communications and Technology (lCT) companies and management consulting 

companies are providing a range of Big Data capabilities, including software, hardware, and 

analytics services. Industry customers of Big Data products and services, including health care, 

transportation, agriculture, and retail companies are identifying ways to increase yields, cut costs, 

and increase customer retention. ICT companies also work closely with government and 

academia to build high performance computers and software systems, which enable cutting edge 

Big Data scientific research and development initiatives. 

Big Data Workforce Development 

McKinsey has projected the United States will need an additional 140,000 to 190,000 

professionals with significant technical depth in data analytics, and the need for an additional 1.5 

million managers and analysts who can work effectively with big data analysis by 2018.3 

To address anticipated workforce demands, colleges and universities are recognizing the value in 

providing students with education and training in Big Data-related disciplines. Such programs 

provide instruction in a broad spectrum of Big Data-related disciplines including data 

management, mathematical and statistical methods for data modeling, and techniques for data 

2 "A special report on managing information: Data, data everywhere," The Economist; February 25, 2010. 

l Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute, May 2011, 

McKinsey and Company. 

2 
5 

visualization in support of business decision making. Although some institutions have initiated 

these types of degree programs, overall they are still relatively rare. 

Federal Big Data Research and Development Initiatives 

On March 29, 2012, the Obama Administration unveiled its "Big Data Research and 

Development Initiative," 4 announcing more than $200 million in new funding to improve the 

tools and techniques needed to access, organize, and glean discoveries from huge volumes of 

digital data. 

Six federal departments and agencies, including the National Science Foundation (NSF), 

National Institutes of Health (NIH), the Department of Defense (DOD) and Defense Advanced 

Research Projects Agency (DARPA), Department of Energy (DOE), and the U.S. Geological 

Survey (USGS) are participating in this initiative. 

National Science Foundation and Big Data 

The NSF Computer and Information Sciences and Engineering Directorate (CISE) supports 

investigator-initiated research in all areas of computer and information science and engineering, 

helps develop and maintain cutting-edge national computing and information infrastructure for 

research and education generally, and contributes to the education and training of the next 

generation of computer scientists and engineers. 

CISE supports Big Data investments in foundational research, cyberinfrastructure, education and 

workforce development needs, and in efforts to support interdisciplinary research. 

Core Techniques and Technologies/or Advancing Big Data Science & Engineering. 

As part of the President's Big Data Initiative, the NSFINIH Core Techniques and Technologies 

for Advancing Big Data Science & Engineering (BlGDATA) is offering research grants to 

accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that 

would not otherwise be possible; encourage the development of new data analytic tools and 

algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase 

understanding of human and social processes and interactions; and promote economic growth 

and improved health and quality of life. 

Yellowstone, Blue Waters, Gordon, and Stampede Supercomputers 

NSF also advances Big Data computational research and development through the Yellowstone, 

Blue Waters, and Stampede Supercomputers, in partnership with the University of Wyoming, the 

University of Illinois, the University of California, San Diego, and the University of Texas at 

Austin, respectively. 

Yellowstone is the petascale computing resource in the National Center for Atmospheric 

Research (NCAR)-Wyoming Supercomputing Center (NWSC), which opened in October 2012. 

4 http://www.whitehouse.gov/blogl2012/03129Ibig-data-big-deal 

3 
6 

(A petascale refers to a computer system capable of reaching performance in excess of one 

petaflops, i.e. one quadrillion floating point operations per second.) The NWSC provides 

advanced computing services to scientists studying a broad range of disciplines, including weather, 

climate, oceanography, air pollution, space weather, computational science, energy production, and 

carbon sequestration. 

The Blue Waters supercomputer provides sustained performance of one petaflop on a range of 

real-world science and engineering applications. Blue Waters enables scientists and engineers 

across the country to tackle a wide range of challenging problems, from predicting the behavior 

of complex biological systems to simulating the evolution of the cosmos. 

The Gordon Compute Cluster is a unique data-intensive supercomputer sponsored by NSF, went 

into production January 1,2012. Large graph problems, data mining, genome assembly, 

database applications, and quantum chemistry are some of the fields of research benefitting from 

Gordon's unique architecture. 

Stampede was officially dedicated in March 2013 at the University of Texas at Austin's 

Advanced Computing Center (TACC). Stampede will have a peak performance of 10 petaflops. 

Research programs already being conducted at Stampede include seismic hazard mapping, ice 

sheet modeling, improving the imaging quality of brain tumors, and carbon dioxide capture and 

conversion. 

Department of Energy Scalable Data Management, Analysis and Visnalization (SDA V) 

Institute 

On March 29, 2012, the DOE announced $25 million to establish the Scalable Data 

Management, Analysis and Visualization (SDA V) Institute to extract knowledge and insights 

from large and complex collections of digital data. Led by the Energy Department's Lawrence 

Berkeley National Laboratory, the SDA V Institute brings together the expertise of six national 

laboratories and seven universities to develop new tools to help scientists manage and visualize 

data on the Department's supercomputers, which will further streamline the processes that lead 

to discoveries made by scientists using the Department's research facilities. 5 

The SDA V Institute helps scientists extract insights from today's increasingly massive research 

datasets by assisting researchers in using state-of-the art software tools for data analysis on these 

supercomputers - ranging from superfast search engines to sophisticated visualization software 

that enables researchers to literally picture and "see" complex relations among data points. The 

Energy Department supports some of the world's fastest supercomputers located at Argonne, 

Oak Ridge, and Lawrence Berkeley National Laboratories, which are used by scientists from a 

wide range of fields. 

National Institute of Standards and Technology (NIST) Big Data Initiatives 

The NIST Information Technology Laboratory conducts a number of activities related to Big 

Data through its Computer Security Division. NIST conducts research on the science behind Big 

5 http://www.sdav-scidac.orglreport.html 

4 
7 

Data, measurement tools to advance Big Data, privacy, and the security of Big Data 

infrastructure. This effort includes convening industry and interested stakeholders together to 

explore the challenges of Big Data, including common terms and taxonomies for use by the field, 

and identification of areas where research is needed. 

Specifically, NIST has worked in areas of coiwergence between cloud computing and data, 

particularly on the interoperability of cloud platforms. Although NIST does not playa visible 

role in the Administration's Big Data Research and Development Initiative, it supports the 

creation of many of the analytical tools to address the challenges of Big Data in both the public 

and private sectors. 

Areas for Examination 

Witnesses have been asked to describe private and public Big Data research and development 

efforts; applications of Big Data initiatives; and management challenges, including workforce 

development issues. 

In addition, the Committee will seek to determine: how federal big data research projects are 

coordinated across participating agencies; how the public and private sectors manage privacy 

concerns as part of Big Data initiatives; how federal privacy laws, such as health privacy laws, 

affect opportunities to gain information from data; and how Congress should prioritize Big Data 

research initiatives in federal research budgets. 

5 
8 

Chairman BUCSHON. All right. This joint hearing of the Subcommittee on Research and the Subcommittee on Technology will 

come to order. 

Good morning, and welcome to today’s joint hearing entitled 

‘‘Next Generation Computing and Big Data Analytics.’’ In front of 

you are packets containing the written testimony, biographies and 

Truth in Testimony disclosures for today’s witnesses. 

Before I get started, since this is a joint hearing involving two 

Subcommittees, I want to explain how we will operate procedurally 

so all Members understand how the question-and-answer period 

will be handled. As always, we will alternate rounds of questioning 

between majority and minority Members. The Chairmen and Ranking Members of the Research and Technology Subcommittees will 

be recognized first. Then we will recognize Members present at the 

gavel in order of seniority on the full Committee and those coming 

in after the gavel will be recognized in order of their arrival. I now 

recognize myself for five minutes for an opening statement. 

Again, I would like to welcome everyone to today’s hearing where 

we will examine how advancements in information technology and 

data analytics enable private and public sector organizations to 

provide greater value to their customers and citizens. Industry, 

academia, and government are all interested in determining how to 

extract value, gain insights, and make better decisions based on 

the wealth of data that is generated today. In recent years, ‘‘big 

data’’ has become the popular term used to encompass this phenomenon. 

TechAmerica, an information technology trade association, defines big data as ‘‘large volumes of high-velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis 

of the information.’’ 

Big data offers a range of opportunities for private industry to 

reduce costs and increase profitability. It can enable scientists to 

make discoveries on a previously unreachable scale. And it can 

allow governments to identify ways to serve its citizens more efficiently. 

The McKinsey Global Institute predicts that effective information 

management can provide $300 billion in annual value to the U.S. 

health care sector alone. TechAmerica released a report last year 

highlighting how big data initiatives can improve the efficiency and 

effectiveness of government services, and through the use of advanced computing power and analytic techniques, universities and 

Federal laboratories can drive new research initiatives that will 

significantly increase our scientific knowledge base. 

There are also various challenges associated with big data that 

the Committee will explore today. McKinsey has estimated that the 

U.S. will face a shortfall of 140,000 to 190,000 professionals with 

significant technical depth in data analytics, and a further shortfall 

of an additional 1.5 million managers and analysts who can work 

effectively with big data analysis by 2018. Committee Members will 

be interested to learn how industry, academia, and government are 

addressing this shortfall. 

While the term ‘‘big data’’ is relatively new, public and private 

organizations have been investing in computing power and data 
9 

analytics for a number of years. In March of last year, the Obama 

Administration announced a Big Data Research and Development 

Initiative, including $200 million in new funding across six different government departments and agencies. I am interested to 

learn how effectively these programs are being coordinated across 

the different Federal agencies to ensure that taxpayer dollars are 

being leveraged effectively. Finally, privacy and security are major 

concerns when private and public organizations are collecting, analyzing, and disseminating massive data sets. 

We have an excellent panel of witnesses ranging across industry, 

academia, and government. I would like to extend my appreciation 

to each of our witnesses for taking the time and effort to appear 

before us today. We look forward to your testimony. 

[The prepared statement of Mr. Bucshon follows:] 

PREPARED STATEMENT OF SUBCOMMITTEE ON RESEARCH CHAIRMAN LARRY BUCSHON

Good morning, I would like to welcome everyone to today’s hearing where we will 

examine how advancements in information technology and data analytics enable 

private and public sector organizations to provide greater value to their customers 

and citizens. 

Industry, academia, and government are all interested in determining how to extract value, gain insights, and make better decisions based on the wealth of data 

that is generated today. In recent years, ‘‘Big Data’’ has become the popular term 

used to encompass this phenomenon. 

TechAmerica, an information technology trade association, defines Big Data as 

‘‘large volumes of high velocity, complex and variable data that require advanced 

techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.’’ 

Big Data offers a range of opportunities for private industry to reduce costs and 

increase profitability. It can enable scientists to make discoveries on a previously 

unreachable scale. And it can allow governments to identify ways to serve its citizens more efficiently. 

The McKinsey Global Institute predicts that effective information management 

can provide $300 billion in annual value to the US health care sector alone. 

TechAmerica released a report last year highlighting how Big Data initiatives can 

improve the efficiency and effectiveness of government services. And, through the 

use of advanced computing power and analytics techniques, universities and federal 

laboratories can drive new research initiatives that will significantly increase our 

scientific knowledge-base. 

There are also various challenges associated with Big Data that the Committee 

will explore today. McKinsey has estimated that the US will face a shortfall of 

140,000 to 190,000 professionals with significant technical depth in data analytics, 

and a further shortfall of an additional 1.5 million managers and analysts who can 

work effectively with big data analysis by 2018. Committee members will be interested to learn how industry, academia, and government are addressing this shortfall. 

While the term Big Data is relatively new, public and private organizations have 

been investing in computing power and data analytics for a number of years. In 

March of last year, the Obama Administration announced a ‘‘Big Data Research and 

Development Initiative,’’ including $200 million in new funding across six different 

federal departments and agencies. I am interested to learn how effectively these 

programs are being coordinated across the different federal agencies to ensure that 

taxpayer dollars are being leveraged effectively. 

Finally, privacy and security are major concerns when private and public organizations are collecting, analyzing, and disseminating massive data sets. We have an 

excellent panel of witnesses ranging across industry, academia and government. I’d 

like to extend my appreciation to each of our witnesses for taking the time and effort to appear before us today. We look forward to your testimony. 

Chairman BUCSHON. I will now yield to Mr. Lipinski for his 

opening statement. 
10 

Mr. LIPINSKI. Thank you. I want to thank you, Chairman 

Bucshon, and I want to thank Chairman Massie for holding this 

hearing. I want to welcome and thank the witnesses for being here. 

Today’s hearing gives us an opportunity to talk about the new 

tools and analytics that are being developed for big data. As Chairman Bucshon stated, big data can be thought of as large volumes 

of complex and diverse types of data that change rapidly with time. 

In basic scientific research in national security as well as in economic sectors ranging from energy to health care, big data challenges are becoming fundamentally important. Effectively dealing 

with big data can impact how we do business and how we think 

about the world. 

As a Member of the Research Subcommittee for several years, I 

have watched as the amount and complexity of data has grown by 

leaps and bounds. The field of astronomy is a great example. When 

the Sloan Digital Sky Survey started work in 2000, its telescope in 

New Mexico collected more data in a few weeks than had been collected in the history of astronomy, and that telescope will be surpassed when the Large Synoptic Survey Telescope begins scientific 

operations in 2020. LSST will photograph the entire sky every few 

days, producing data at a rate almost 100 times greater than the 

Sloan Survey. But data is useless without the means to store and 

analyze it in an efficient manner. 

The types of data are changing as well. Data has gone from being 

mostly numbers entered into Excel spreadsheets to data coming 

from sensors, cell phone cameras and millions of email messages. 

In fact, it is estimated that over 85 percent of data generated today 

are these kinds of unstructured data, data like videos and emails. 

The change in the volume and variety of data as well as how fast 

data is being produced and changed creates almost limitless opportunities. For example, since cybersecurity data is massive, varied, 

and changing quickly, big data technologies have the potential to 

detect and prevent cyber attacks before they happen. I know that 

organizations like IBM are developing technologies to do just that. 

Additionally, big data could be used to establish new business models, create transparency, improve decision-making and reduce inefficiencies within businesses and government. 

But along with the opportunities, there are a number of challenges. We need new tools and software packages to manage, organize, and analyze all these different kinds of data. Additionally, we 

will need an analytic workforce to ensure the gains of big data. 

These challenges necessitate involvement from government, academia and the private sector. That is why I am happy to see all 

those sectors represented here today. 

The government has and will continue to play an instrumental 

role in this area. For instance, the Networking and Information 

Technology Research and Development program, or NITRD, created an interagency big data group that is coordinating Federal efforts in technologies, research, competitions, and workforce development for big data. We had a hearing on the NITRD program 

back in February, and I expect that we will be able to take a broader look at many of the same issues in today’s hearing. 

In some cases, agencies have teamed up to issue joint solicitations. For example, NSF and NIH have a joint big data grant pro-
11 

gram that awarded nearly $15 million of grants to eight teams of 

researchers last year. These first award grants went to projects focused on designing new tools for big data and new data analytic 

approaches. We will be hearing more about these and other interagency activities from Dr. Jahanian in his testimony. We will also 

learn more about specific programs at NSF, one of the leading 

agencies in Federal big data efforts on both the analytics side and 

the computational resources side. 

As I mentioned before, one of the areas being coordinated 

through NITRD is workforce development for big data. Several 

agencies, including NSF, have education activities to support a new 

generation of big data researchers. As we will likely hear from all 

of the witnesses, we face a looming shortage of workers with the 

skills needed to analyze and manage large, complex and high-velocity data sets. There is some overlap with the broader STEM skills 

we so often speak about in this committee, but there are also 

unique skills required to address the big challenges of big data. We 

need to consider how to build those skills into STEM curricula, especially at the undergraduate and graduate levels. I look forward 

to hearing from our witnesses about the current educational efforts 

and what additional initiatives may be necessary. 

And finally, since big data involves different types of data that 

can be produced and transferred quickly, there are concerns over 

privacy. We need to ensure that we strike the right balance between exploring and implementing all of the potential benefits of 

big data while also protecting individuals’ personal information. 

I look forward to hearing the witnesses’ testimony and our discussion today, and I yield back the balance of my time. 

[The prepared statement of Mr. Lipinski follows:] 

PREPARED STATEMENT OF SUBCOMMITTEE ON RESEARCH

RANKING MINORITY MEMBER DANIEL LIPINSKI

Thank you, Chairmen Bucshon and Massie for holding this hearing on examining 

the next generation of computing and big data analytics. I want to welcome and 

thank the witnesses for being here today. 

Today’s hearing gives us an opportunity to talk about the new tools and analytics 

that are being developed for big data. Big data can be thought of as large volumes 

of complex and diverse types of data that are also high velocity—meaning they 

change rapidly with time. 

As a member of the Research Subcommittee for several years now, I have watched 

as the amount and complexity of data has grown by leaps and bounds. The field 

of astronomy is a great example. When the Sloan Digital Sky Survey started work 

in 2000, its telescope in New Mexico collected more data in a few weeks than had 

been collected in the history of astronomy. And that telescope will be surpassed 

when the Large Synoptic Survey Telescope goes online in about 2020. LSST will 

photograph the entire sky every few days. That’s difficult for any of us to wrap our 

heads around. 

The types of data are changing as well. Data has gone from being mostly numbers 

entered in excel spreadsheets to data coming from sensors, cellphone cameras, and 

millions of email messages. In fact, it is estimated that over 85 percent of data generated today are these kinds of unstructured data—data like videos or emails. 

The change in the volume and variety of data as well as how fast data is being 

produced and changed creates almost limitless opportunities. For example, since 

cybersecurity data is massive, varied, and changing quickly, big data technologies 

have the potential to detect and prevent cyber attacks before they even happen. I 

know that organizations like IBM are developing technologies to do just that. Additionally, big data could be used to establish new business models, create transparency, improve decision-making, and reduce inefficiencies within businesses and 

government. 
12 

But along with the opportunities, there are a number of challenges. We need new 

tools and software packages to manage, organize, and analyze all these different 

kinds of data. Additionally, we will need an analytic workforce to ensure the gains 

of big data. These challenges necessitate involvement from government, academia, 

and the private sector. That is why I am happy to see all those sectors represented 

today. 

The government has and will continue to play an instrumental role in this area. 

For instance, the Networking and Information Technology Research and Development—or NITRD—program created an interagency big data group that is coordinating federal efforts in technologies, research, competitions, and workforce development for big data. 

In some cases, agencies have teamed up to issue joint solicitations. For example, 

NSF and NIH have a joint big data grant program that awarded nearly $15 million 

of grants to eight teams of researchers last year. These first awarded grants went 

to projects focused on designing new tools for big data and new data analytic approaches. We will hear more about these and other interagency activities from Dr. 

Jahanian in his testimony. We will also learn more about specific programs at NSF, 

one of the leading agencies in federal big data efforts on both the analytics side and 

the computational resources side. 

As I mentioned before, one of the areas being coordinated through NITRD is the 

workforce development needs for big data. Several agencies, including NSF, have 

education activities to support a new generation of big data researchers. As you will 

likely hear from all of the witnesses, we face a looming shortage of workers with 

the skills needed to analyze and manage large, complex, and high-velocity data sets. 

There is some overlap with the broader STEM skills we often speak of in this committee. But there are also some unique skills required to address the challenges of 

big data. We need to consider how to build those skills into STEM curricula, especially at the undergraduate and graduate levels. I look forward to hearing from our 

witnesses about the current educational efforts and what additional initiatives may 

be necessary. 

Finally, since big data involves different types of data that can be produced and 

transferred quickly, there are concerns over privacy. We need to ensure that we 

strike the right balance between exploring and implementing all of the potential 

benefits of big data while also protecting individuals’ personal information. 

I look forward to hearing the witnesses’ testimonies and to our discussion today. 

Chairman BUCSHON. Thank you, Mr. Lipinski. The Chair now 

recognizes the Chairman of the Subcommittee on Technology, Mr. 

Massie, for five minutes for his opening statement. 

Mr. MASSIE. Thank you, Chairman. 

Good morning. Today we are examining an issue that we hear 

a lot about. ‘‘Big data’’ is a popular new term that can mean a lot 

of different things. The scientific community, though, has generated and used big data before there was the term ‘‘big data.’’ In 

fact, in 1991 this Committee authored the High Performance Computing Act, which organized the Federal agency research, development, and training efforts in support of advanced computing. 

Individual researchers have always been faced with difficult decisions about their data: what to keep, what to toss, what to verify 

with additional experiments. And as our computing power has increased, so has the luxury of storing more data. Incorporating computer power to process more scientific data is transforming laboratories across the country. 

At the same time, the ability to analyze large amounts of data 

across multiple networked platforms is also transforming the private sector. Through big data applications, businesses have not 

only revealed previously hidden efficiency improvements in their 

internal operations, but, more importantly, also uncovered entirely 

new types of businesses built around data that was previously not 

accessible due to its size and complexity. 
13 

Today’s hearing will examine the hype around big data. Is the 

United States the most innovative Nation in big data? Is our regulatory system creating any burdens on businesses? Could publicprivate partnerships with the Federal agencies be improved to 

allow for more data innovations? 

I thank our witnesses today for their participation today and I 

look forward to hearing their testimony. Thank you. I yield back. 

[The prepared statement of Mr. Massie follows:] 

PREPARED STATEMENT OF SUBCOMMITTEE ON TECHNOLOGY

CHAIRMAN THOMAS MASSIE

Good Morning. Today we are examining an issue that we hear a lot about. ‘‘Big 

Data’’ is a popular new term that can mean a lot of different things. 

The scientific community has generated and used Big Data before there was Big 

Data. In fact, in 1991 this Committee authored the High Performance Computing 

Act, which organized the federal agency research, development and training efforts 

in support of advanced computing. 

Individual researchers have always been faced with difficult decisions about their 

data: what to keep, what to toss, what to verify with additional experiments. As our 

computing power has increased, so has the luxury of storing more data. Today, managing this data allows for better-informed experiments, more exact metrics, and perhaps significantly longer doctoral theses. Incorporating computer power to process 

more scientific data is transforming laboratories across the country. 

At the same time, the ability to analyze large amounts of data across multiple 

networked platforms is also transforming the private sector. Through Big Data applications, businesses have not only revealed previously hidden efficiency improvements in their internal operations, but also uncovered entirely new types of business 

built around data that was previously not accessible due to its size and complexity. 

Today’s hearing will examine the hype around Big Data. Is the United States the 

most innovative nation in Big Data? Is our regulatory system creating any burdens 

on businesses? Could public-private partnerships with the federal agencies be improved to allow for more data innovations? 

I thank our witnesses for their participation today and look forward to hearing 

their testimony. 

Chairman BUCSHON. Thank you, Mr. Massie. The Chair now recognizes Ms. Wilson for five minutes for her opening statement. 

Ms. WILSON. First of all, I would like to thank both Chairman 

Bucshon and Chairman Massie for holding this joint hearing, and 

thank you all to our witnesses for being here today. Welcome. 

This morning’s hearing provides us with the opportunity to discuss one of the newest buzzwords in Washington, and you know we 

have many buzzwords here. This one: big data. This buzzword is 

not an exaggeration. A computer that used to take up the space of 

this entire room now fits in the palm of your hand. It is remarkable. 

Just as computers have gotten immensely smaller, they have 

also gotten immensely more powerful. Instead of talking about 

megabytes, we are now talking about petabytes and zettabytes— 

quadrillions and sextillions of units of information. It boggles the 

mind. Collecting and storing this huge volume of data would have 

been impossible just a few years ago. 

I am looking forward to your testimony and learning more about 

the benefits of big data to society. As I understand it, big data has 

the potential to improve nearly all sectors of society. The National 

Cancer Institute is funding a prototype in biological big data that 

could lead to new advances in cancer treatment. Companies and 

agencies are using big data to run controlled experiments that im-
14 

prove decision-making. Scientists at Florida International University in my district are using big data to advance understanding of 

topics including cybersecurity, social networks and cloud computing. 

But there are challenges. In order to reap all the benefits of complex and broadly available data, we need new technologies and software. We also need a workforce, a workforce with the skills necessary to analyze data of such great volume and complexity. A recent study estimates that the United States is in need of 190,000 

additional data scientists. 

In thinking about this hearing on big data, I couldn’t help but 

think about the tragic events last week in Boston. The marathon 

bombings may be one of the most photographed attacks in history. 

The Massachusetts State Police asked the public to share the 

photos and videos taken on that awful day. Now all of this digital 

information has been and is being used by the Boston Police Department and the FBI in their investigation. It appears that this 

data has been instrumental in helping to identify the individuals 

who were involved. 

Examples like this one demonstrate how important it is that we 

develop and attain the tools and the skills people need to analyze 

tremendous amounts of complex data. Big data can not only lead 

to amazing scientific discoveries; it can also save lives. 

As we learn more about these opportunities and challenges 

today, I hope our witnesses will offer recommendations on how the 

Federal Government can help create the new tools, software and 

workforce needed to realize the full potential of big data. 

Chairman Bucshon, Chairman Massie, thank you again for holding this hearing, and I yield back the balance of my time. 

[The prepared statement of Ms. Wilson follows:] 

PREPARED STATEMENT OF SUBCOMMITTEE ON TECHNOLOGY

RANKING MINORITY MEMBER FREDERICA S. WILSON

I’d like to thank both Chairman Bucshon and Chairman Massie for holding this 

joint hearing. And thank you to all of our witnesses for being here today. 

This morning’s hearing provides us with the opportunity to discuss one of the 

newest buzz-words in Washington and around the world—‘‘big data.’’ 

This buzz-word is not an exaggeration: A computer that used to take up the space 

of this entire room now fits in the palm of your hand. It is remarkable. 

Just as computers have gotten immensely smaller, they have also gotten immensely more powerful. Instead of talking about megabytes, we are now talking 

about petabytes and zettabytes—quadrillions and sextillions of units of information. 

It boggles the mind. Collecting and storing this huge volume of data would have 

been impossible just a few years ago. 

I’m looking forward to the testimony of today’s witnesses and learning more about 

the benefits of ‘‘big data’’ to society. 

As I understand it, big data has the potential to improve nearly all sectors of society. The National Cancer Institute is funding a prototype in biological ‘‘big data’’ 

that could lead to new advances in cancer treatment. Companies and agencies are 

using ‘‘big data’’ to run controlled experiments that improve decision-making. Scientists at Florida International University—in my district—are using ‘‘big data’’ to 

advance understanding of topics including cybersecurity, social networks, and cloud 

computing. 

But there are challenges. In order to reap all the benefits of complex and broadly 

available data, we need new technologies and software. We also need a workforce 

with the skills necessary to analyze data of such great volume and complexity. A 

recent study estimates that the United States is in need of 190,000 additional data 

scientists. 
15 

In thinking about this hearing on ‘‘big data,’’ I couldn’t help but think about the 

tragic events last week in Boston. The marathon bombings may be one of the most 

photographed attacks in history. The Massachusetts State Police asked the public 

to share the photos and videos taken on that awful day. Now, all of this digital information has been and is being used by the Boston Police Department and the FBI 

in their investigation. It appears that this data has been instrumental in helping 

to identify the individuals who were involved. 

Examples like this one demonstrate how important it is that we develop and attain the tools and the skilled people needed to analyze tremendous amounts of complex data. Big data can not only lead to amazing scientific discoveries—It can also 

save lives. 

As we learn more about these opportunities and challenges today, I hope our witnesses will offer recommendations on how the federal government can help create 

the new tools, software, and workforce needed to realize the full potential of ‘‘big 

data.’’ 

Chairman BUCSHON. Thank you, Ms. Wilson. 

If there are Members who wish to submit additional opening 

statements, your statements will be added to the record at this 

point. 

It is now time to introduce our panel of witnesses. Our first witness is Dr. David McQueeney, the Vice President of Technical 

Strategy and Worldwide Operations at IBM Research. In this capacity, he is responsible for setting the direction of IBM’s overall 

research strategy across 12 worldwide labs and leading the global 

operations and information systems teams. Dr. McQueeney’s background covers a wide range of disciplines, spending about half of 

his career as a researcher and research executive and half in IBM’s 

customer-focused areas. He holds an M.S. and Ph.D. in solid-state 

physics from Cornell University and an A.B. in physics from Dartmouth College. Welcome. 

Our second witness is Dr. Michael Rappa, the Executive Director 

of the Institute for Advanced Analytics and Faculty Member of the 

Department of Computer Science at North Carolina State University. Dr. Rappa has 25 years of experience as a professor working 

across academic disciplines at the intersection of management and 

computing. He began his teaching career at the University of Minnesota where he earned his doctorate degree. Welcome. 

And our final witness is Dr. Farnam Jahanian, the Assistant Director for the Computer and Information Science and Engineering 

Directorate at the National Science Foundation and a frequent visitor to our Subcommittee. He oversees the CISE’s mission to uphold 

the Nation’s leadership in computer and information science and 

engineering. He also serves as Co-chair of the Networking and Information Technology Research and Development, or NITRD, Subcommittee of the National Science and Technology Council Committee on Technology, providing overall coordination for the activities of 14 government agencies. Dr. Jahanian holds a master’s degree and a Ph.D. in computer science from the University of Texas 

at Austin. Welcome again. 

As our witnesses should know, spoken testimony is limited to 

five minutes each after which Members of the Committee have five 

minutes each to ask questions. Your written testimony will be included in the record of the hearing. 

I now recognize our first witness, Dr. McQueeney, for five minutes for his testimony. 
16 

TESTIMONY OF DR. DAVID MCQUEENEY, VICE PRESIDENT, 

TECHNICAL STRATEGY AND WORLDWIDE OPERATIONS, 

IBM RESEARCH 

Dr. MCQUEENEY. Good morning, Mr. Chairman, Ranking Members, Members of the Subcommittees. Thank you for the opportunity to testify today. My written testimony covers next-generation computing, big data and analytics, workforce development and 

the role of government. In my five minutes, I will focus on areas 

where I can offer critical insights from my personal experience. 

Computing today is undergoing profound change. We are moving 

from computing based on processors that are programmed to follow 

a predesigned sequence of instructions to cognitive computing systems based on massive amounts of data evolving into systems that 

can learn. This new approach will require new strategies in hardware and in software and improved skills to maintain U.S. leadership. Cognitive systems will digest and exploit massive data volumes. Tools such as mobile phones, videos and social networks generate as much data in two days in 2013 as in all of human history 

prior to 2003. 

Advanced analytics can be thought of as tools for infusing all this 

data to make decisions on facts rather than intuition. The challenge is to transform latent data into actionable information to decide what to do next. For example, the Memphis Police Department 

is using data analytics to map crime hotspots and find patterns. As 

a result, they have been able to reduce crime by 30 percent with 

no increase in overall police manpower. 

To run advanced analytics, it is essential to have the most powerful computing systems. However, current supercomputing systems are reaching performance levels that will stagnate without 

significant innovation. We must move to the next generation of 

large-scale computing called exascale computing, a thousand times 

faster than today’s petascale machines. 

The United States needs to invest now in the research and development for exascale systems to maintain strategic and economic 

leadership. Government-funded research on domain skills, especially at our national laboratories, should target systems for modeling, simulation, and analytics on big data. 

Before 2005, the United States had a clear lead in the global 

supercomputing race. Today, we are still ahead but the rest of the 

world is catching up rapidly. To stay ahead will require new skills 

and knowledge and new types of decision-making. Nearly two million IT jobs will be created by 2015 in the United States to support 

big data, and the job candidates with analytic skills will get these 

jobs. 

Industry is developing many collaborative skills programs, as 

enumerated in my testimony. I highlight our announcement today 

with Rensselaer Polytechnic Institute to offer a graduate degree 

program in the fall of 2013, the Master of Science in Business Analytics. 

Privacy must be considered in the design of big data systems. Big 

data does not require the sacrifice of personal privacy. When personal information is used, design-in processes such as IBM’s Privacy By Design can protect privacy. When people understand how 

information is used, they have the ability to set data usage policies 
17 

and enjoy benefits of the analysis, they tend not to have privacy 

concerns. 

The government’s role should focus on research and skills. First, 

Federal research investment in high-performance computing is critical to big data. Industry needs university-based exploratory research into numerous areas including system design, flexible software defined environments, and IT infrastructure. 

Second, IBM strongly supports the reauthorization of the Department of Energy High End Computing Revitalization Act of 2004 to 

be offered by Representative Hultgren. This bill will improve highend computing R&D at the DOE and strengthen government industry partnerships for exascale platforms. IBM has a long history of 

successful partnerships with DOE. This partnership established 

computational simulation as an essential tool for scientific inquiry 

and led to world leadership in the United States in high-performance computing. The challenge ahead is to continue this growth. 

Past Federal investments in HP-related research, particularly at 

DOE’s national labs, have underpinned mission-critical supercomputers at DOD, NASA, NOAA, and in the intelligence agencies. 

Third, the professional science masters program supported by 

NSF is particularly relevant as it provides advanced training in 

science or mathematics and develops workplace skills valued by 

employers. Finally, Congress should reauthorize the Carl D. Perkins Act and the Federal work-study program and restructure 

them to align workforce needs and big data. 

In conclusion, there exists today a tremendous abundance of data 

about our world. New cognitive computing capabilities will help determine which countries and businesses will thrive. The United 

States should support advanced computing and build its workforce 

to seize the future. 

Thank you, and I welcome your questions. 

[The prepared statement of Dr. McQueeney follows:] 
18 

Dr. David F. McQueeney 

Vice President, Technical Strategy and Worldwide Operations 

IBM Research 

House Science Research Subcommittee and 

House Science Technology Subcommittee 

Next Generation Computing and Big Data Analytics Hearing 

April 24, 2013 

Good morning, Chairman Bucshon, Chairman Massie, Ranking Member Lipinski, Ranking Member 

Wilson, and members of the subcommittees. Thank you for the opportunity to speak with you about 

big data, analytics, and the new opportunities they present. 

My name is David McQueeney and I am the Vice President for Technical Strategy and Worldwide 

Operations at IBM Research. I am responsible for setting the direction of IBM's overall research 

strategy and I lead the creation of IBM's Global Technology Outlook. This is an annual effort which 

guides IBM's R&D directions and its acquisition strategies. I have held a variety of senior research 

and business unit leadership roles throughout my twenty-five years at IBM. 

My testimony today draws on this and the wider IBM experience with Big Data and Analytics and 

associated technology and skills requirements. I will focus on next generation computing, the nature 

of big data and analytics, opportunities and issues these technologies present, and how best to 

promote their growth. 

What is Big Data and Analytics? 

We are entering a new era of computing which is causing profound change in the IT industry. We are 

moving from computing based on processors that are programmed to follow a pre-designed sequence 

of instructions, to the cognitive computing era based on massive amounts of data and systems 

evolving into systems that can "learn". Cognitive systems can modify and optimize projections or 

weigh the value of information based on experience and results. This new approach to computing 

requires new strategies and skills to maintain U.S. leadership. 

Cognitive systems will digest and exploit the massive data volumes being generated today. The data is 

coming from the technologies which mark our age: mobile phones, cloud computing, social networks 

and what we call the "internet of things", including everything from your car to your refrigerator to the 

thousands of texts your son or daughter sends each month. Imagine the data volumes generated by 

just two popular sources, the two billion videos watched daily on YouTube and the 293 billion emails 

sent every single day. As much data is generated in two days in 2013 as in all of human history prior 

to 2003. 

Why is Big Data Analytics important? 

Advanced analytics can be thought of as tools for using data to make decisions based on facts rather 

than intuition. These tools are applied to unstructured data from the Web, communications networks, 

governments, businesses, and tens of millions of sensors, to help us understand how the world works 

better than ever before. The challenge for business and government alike is to transform latent data 

into meaningful, actionable information. AnalytiC tools enable predictive inSights and inform decision 

making to prevent bad outcomes and define helpful courses of action. 
19 

For example, the Memphis Police Department is using data analytics to create maps of crime hot 

spots and find patterns that police could not see themselves. They have correlated crimes committed 

at night with presence of pay phones outside convenience stores. As a result, the police suggested 

moving the phones inside the stores. The outcome - crime has fallen. Memphis has increased 

targeted, effective police presence and eliminated random patrolling. The city has seen a 30 percent 

reduction in crime with no increase in overall manpower using new knowledge and skills based on 

data analytics. There are numerous examples around the world from all sectors of society. 

In sum, advanced analytics are tools for using data to improve organizational effectiveness and create 

value. Combining analytics with massive data flows enables organizations to make decisions based 

on facts rather than intuition. This factual, analytiC decision making can revolutionize industries and 

help society successfully address challenges such as energy conservation, health care, and 

transportation, as well as rooting out waste, fraud and abuse. 

Trends in technology development for Next Generation Computing and the Data Explosion 

The most powerful computing systems today are essential in both government and industry. They are 

used in applications ranging managing the nation's nuclear stockpile to automobile, aircraft and 

semiconductor design to the development of new tires and race car aerodynamics to oil exploration 

and oil recovery. However, they are reaching a performance level that will stagnate without significant 

innovation. We must move to the next generation of large-scale computing, exascale computing -

1000 times faster than today's petascale machines. 

An exaflop is a quintillion floating point operations per second. Exascale computers will have 

performance speeds at or above 10 to the 18th power of floating point operations. That's fast. We are 

at a major inflection point in developing next generation, high end, exascale systems. 

The United States must invest now to maintain its economic leadership and competitiveness. 

Government funding and domain skills, especially in our national labs, are required. Investment 

should be targeted at developing innovative and composable systems for modeling, simulation and 

analytics on big data. The new imperative is to design for data, not for processor performance. New 

systems and software concepts must be developed to continue U.S. strategic leadership and to 

support and enhance the economic competitiveness of U.S. Industry. 

To put this in context, before 2005, the U.S. led decidedly in the global supercomputing race, with 

Europe and Japan following. Today, however, and in the future, additional regions are making 

important sovereign strategic investments to compete aggressively. 

The U.S. is still ahead, but others are catching up fast. Japan is continuing its long term effort in high 

performance computing (HPC) as well, with a focus on building "Big Iron" systems. India is making a 

$1 B+ investment to pursue exascale computing with an emphasis on applications and user capability. 

And in Europe, which has been a leader in software and applications for industrial use, we now see 

strong HPC support for small and medium enterprises. There is an aggressive effort to build a 

European capability in HPC with support for companies such as Bull and in complementary 

infrastructure efforts with a number of "Big Science" projects ($1 B+) including Blue Brain, Graphene, 

Robotic, and Health Care. 

In sum, the global race is on. The necessity for leadership in high end computing for a highly 

competitive economy has been recognized around the world and regions are stepping up to the plate. 

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20 

Work Force Development 

Skills are a major inhibitor to the growth of next generation computing and Big Data. Big Data requires 

new skills, knowledge and new types of decision-making. Today's employers are seeking job 

candidates who can analyze and build strategy around Big Data, or the 2.5 quintillion bytes of 

information generated daily. Indeed, the number one barrier to the adoption of business analytics 

technologies is insufficient skills and experience, according to IBM's 2012 Tech Trends Report. 

Much has been written about the urgency of this skills gap. McKinsey Global Institute reports that 

over the next seven years, the need for skilled business intelligence workers in the U.S. alone will 

dramatically exceed the available workforce -- by as much as 60 percent. There is not a job crisis in 

the U.S.; there is a skills crisis. Nearly two million information technology jobs will be created by 2015 

in the U.S. to support Big Data, according to research firm Gartner, Inc. Analytics skills will be a key 

differentiator for candidates seeking to fill those jobs. 

Further, in our global society, applying analytics to Big Data will be a key factor in determining which 

countries pull ahead economically and which ones fall behind. Industry is responding to this situation 

with a variety of programs to build new skills. I will describe several which IBM has created. We are 

addressing skills requirements from grade nine through graduate school and into the working 

population around the globe. 

In fact, just today IBM is announcing a new partnership to prepare business students for the 

expanding scope of careers requiring Big Data analytics skills. IBM and Rensselaer Polytechnic 

Institute are combining forces to offer a new Lally School of Management and Technology graduate 

degree program in fall 2013: the Master of Science in Business Analytics. 

This new Master of Science in Business Analytics is a one year, 3D-credit graduate degree program 

that will provide students and career professionals with the hands-on experience and knowledge 

required to succeed in analytics jobs. It will feature a three-part curriculum comprised of: 

A business core to ensure students understand where Big Data fits into a business' strategy 

and operations, as well as how analytics-driven decisions can impact a business' growth, 

competitive standing and bottom line. 

An analytics core that includes hands-on training in predictive modeling to help businesses 

identify profitable data patterns, focusing also on data management, statistical analysis and 

leading-edge techniques in harneSSing Big Data. 

An experiential core with project-based courses that allow students to apply their newly 

gained skills to real-world problems faced by businesses spanning a range of industries. 

This new collaboration builds on experience gained from our numerous other engagements. Some 

highlights of those are below: 

Early STEM Development -IBM collaborates with the New York City Department of Education, the 

City University of New York, and NYC College of Technology to provide a single public school for 

grades 9-14 and a new public school program to be replicated in other schools. The school, called PTECH (Pathways in Technology Early College High School) is preparing students to fill entry-level jobs 

in technology fields or provide them with the foundation for ongoing learning in a four-year college in 

the STEM disciplines. 

Higher Ed - IBM has 200 academic partnerships focused on Big Data analytics with schools such as 

Yale, Northwestern and Michigan State universities (in addition to our 30,000 academic partnerships 

overall). The mission: develop Big Data analytics curriculums primarily in their business schools. IBM 

3 
21 

works closely with professors in support of curriculum materials and case studies and provides guest 

speakers and faculty awards to accelerate degree program development. A key example of this 

curriculum: Working with Fordham University in New York City, IBM partnered with faculty to create a 

Center for Digital Transformation and two degree programs in analytics: the Masters of Science in 

Business Analytics and Marketing Intelligence. 

Advanced Analytics Center - IBM recently unveiled a first of its kind Advanced Analytics Solutions 

Center in Columbus, Ohio, which aims to create 500 new analytics jobs. The center will serve as an 

innovation hub to advance analytics skills, drawing on the expertise of local businesses, educational 

institutions and industry partners. I BM is partnering with Ohio State University to develop new analytics 

curricula at the undergraduate, graduate and executive education levels. The Columbus center is the 

most recent addition to our existing centers, which include both Dallas and Austin. 

Watson Joins Rensselaer Research Team -IBM is providing a modified version an IBM Watson 

system to Rensselaer Polytechnic Institute, making it the first university to receive such a system. The 

arrival of the Watson system will enable new leading-edge research at Rensselaer, and afford faculty 

and students an opportunity to find new uses for Watson and deepen the systems' cognitive 

capabilities. 

Watson Case Competitions - IBM works with professors and local businesses to create projectfocused case studies for students to gain hands-on experience in Big Data, analytics and cognitive 

computing. For example, the University of Rochester's Simon School of Business launched the firstever Watson academic case competition where MBA students identified critical areas in which Watson 

technology could be beneficial such as crisis management, mining and transportation. This was 

followed by competitions at Cornell University, the University of Connecticut and the University of 

Southern California, where more than 100 business and engineering students combined their skills to 

recommend new uses for Watson, including an innovation that helps doctors identify people who may 

be suffering from Post-Traumatic Stress Disorder. 

Interning with Watson - This past summer, IBM brought students into its labs to learn about and 

develop applications for Watson's ground-breaking analytics technology. IBM Watson interns worked 

directly with clients and IBMers on real-world projects. 

Professional Training - IBM actively trains current workers on Big Data skills. This past year, IBM 

held 1,200 Big Data boot camps at client, partner and university sites and trained over 2,400 IT 

professionals and students on the latest data management techniques; this year IBM launched 

BigDataUniversity.com to help students learn Hadoop, stream computing, and Big Data analytics 

skills. Over 13,000 students have enrolled over the past six months. 

These collaborations between IBM and top U.S. universities are building a workforce of professionals 

and are creating jobs now. Consider North Carolina State University, whose Master of Science in 

Analytics program has generated the ultimate outcome - 90 percent of its graduates receiving job 

offers from data-hungry employers. 

Privacy matters 

Privacy must be considered in the design of Big Data systems. Importantly, realizing the promise of 

Big Data does not require the sacrifice of personal privacy. In many cases in Big Data projects, the 

data being aggregated is non-regulated, de-identified information with no need to re-identify to derive 

value. When personal information is used, organizational processes and technology can protect 

privacy. 

4 
22 

Organizational practices include a systematic way of thinking and acting proactively and responsibly 

about the use of data. When organizations using personal information take privacy into account from 

the start and design in privacy protection practices, they act as better stewards of information and help 

individuals make more informed choices. For example, IBM practices "privacy by design" and has 

included privacy considerations as we have developed our new sense-making analytics technology. 

We welcome the growing ranks of organizations working to adopt this approach. 

Furthermore, IBM and other companies are working with the Future of Privacy Forum on a consumer 

trust seal, authenticated by a third party, to give consumers confidence in smart grids. IBM believes 

that industry codes of conducts should promote transparency with customers. We have been urging 

better communication for a long time - as far back as 1999 we decided to withhold advertising dollars 

from North American websites that did not post their privacy policies. 

Although these considerations are critical as technology is changing rapidly, a major trend in 

consumer expectations is also evolving. Indeed, Americans' views of online privacy are shifting as 

they use multiple social networking tools every day to describe their personal thoughts, actions and 

emotions very publicly, such as through online profiles, tweets, blog posts, and photos. 

Our experience is that when people understand how information is used, have the ability to set data 

usage policies, and enjoy the benefits of the analysis, they tend to see a helpful tool rather than a 

privacy violation. Information - including personal information - is becoming a helpful tool on a grand 

scale. When combined in great quantities and put through sophisticated analysis, general information 

can be used to address some of society's most pressing problems. For example, the Center for 

Medicare and Medicaid Services uses big data in a new program to prevent health care fraud. A 

sophisticated and evolving set of algorithms is used to highlight problem areas and generate fraud 

alerts that allow the agency to direct attention and resources, and take appropriate action. 

Increasingly, CMS will be in a position to interrupt claims that should not be paid. The need to deter 

and detect is great, with health care improper payments totaling in the hundreds of millions of dollars. 

What is the role of government in research? 

Federal research investment in high performance computing is critical to Big Data. Research both 

creates new ideas and insights and trains students with critical skills for later employment. Industry 

needs fundamental, exploratory research as we push the boundaries of programmable systems with 

our high performance systems. We need research into numerous areas including: system design for 

optimized handling of the volume, velocity, and variety of data described earlier; software research to 

understand how to create dynamic and flexible software-defined environments; IT infrastructure 

research to build programmable, optimized and automated environments. New knowledge, practices, 

and infrastructures will enable the discoveries and innovation which are the foundation of U.S. 

competitiveness. 

In addition, IBM strongly supports the reauthorization of the Department of Energy High-End 

Computing Revitalization Act of 2004 to be offered by Representative Hultgren. This bill will improve 

high-end computing research and development at the DoE and strengthen government-industry 

partnerships for integrated research, development and engineering of exascale platforms. IBM has a 

long history of successful partnership with DoE. Through our joint work, computational simulation has 

been established as an essential, broadly available tool for scientific inquiry. World leadership for the 

U.S. in HPC has been grown and sustained, and HPC has become a true engine for innovation. The 

challenge ahead is to continue this growth, recognizing that Big Data is an intrinsic aspect of high 

performance computing. There is not an "either/or" choice between HPC and Big Data. 

The explosion of data is having a significant impact on the focus of HPC. HPC workloads are evolving 

from single "heroic" calculations to complex simulations with different time and space scales involving 

5 
23 

multiple scientific disciplines and several types of computational algorithms. These complex 

simulations interact with each other using mountains of data. Research is needed into systems which 

can integrate computation, data and storage to successfully exploit Big Data to address many 

enormously important scientific, social and commercial workloads. 

Past federal investments in HPC-related research, particularly at DOE's national laboratories in 

partnership with the industry and academia, have underpinned mission critical supercomputers in 

many Federal agencies, including the Department of Energy, for both the Office of Science and the 

National Nuclear Security Administration; the Department of Defense; the National Aeronautics and 

Space Administration; the National Oceanic and Atmospheric Administration; and U.S. intelligence 

agencies. 

What is the role of government in workforce development? 

The Professional Science Masters program supported by National Science Foundation is particularly 

relevant to the new era of computing. A Professional Science Masters (PSM) degree is a new 

graduate degree designed to allow students to pursue advanced training in science or mathematics, 

while simultaneously developing workplace skills valued by employers. According to the Council of 

Graduate Schools, enrollment in PSM degrees increased 22% between 2010 and 2012 and was 

dominated by four fields of study: computer/information sciences (21 %), biotechnology (16%), 

environmental sciences and natural resources (14%), or mathematics and statistics (14%). 

Additionally, Congress should reauthorize the Carl D. Perkins Act and the Federal Work study 

Program to align to labor needs in Big Data. While this is unlikely to fall in the Science Committee 

jurisdiction, it is important for the growth of STEM (science, technology, engineering and math) 

education and jobs. 

In the U.S., community college graduation rates hover at or about 25 percent. At the same time, there 

are 28 million middle skill jobs - those requiring postsecondary degrees - currently available in the 

U.S. which pay close to $40,000 per year on average. Over the next 10 years, 14 million jobs - a 50 

percent increase - will be created for students with "middle skills." 

With so many high school graduates in need of remediation rather than prepared with the skills and 

credentials needed to fill these jobs, we must refocus our efforts on strengthening the education system 

in order to make the U.S. more competitive. 

In the U.S., career and technical education (CTE), once called vocational education, is the core 

program linking school to career. Federal funding under Perkins provides over $1 billion to schools. 

Unfortunately, Perkins is not currently structured to meaningfully address the skills gap that we face. 

Similarly, the federal government invests nearly $1 billion in the Federal Work study Program (FWSP) 

every year, providing nearly 800,000 undergraduates the opportunity to gain work experience while 

earning critical financial aid. To maximize its potential, FWSP should not be viewed solely in the lens 

of college affordability, but also as a way to prepare students to be successful in a career. 

If 10 percent (or $200 million) of the $2 billion invested in these two programs annually were targeted 

in new ways, CTE programs could be reshaped to help significantly bridge the skills gap. For 

example, over a 10 year period, a new skills-based apprenticeship program could belter prepare more 

than 1,000,000 young people for the jobs of the 21 st Century and reinvigorate the American economy. 

IBM supports reauthorizing Perkins funding to align to labor market needs in high-growth industry 

sectors; improve CTE programs with strong collaborations among secondary, postsecondary 

institutions and employers (like the P-TECH example cited earlier); and create accountability 

6 
24 

measures that provide common definitions and clear metrics for performance of CTE programs to 

improve academic outcomes while building technical and employability skills of participants. 

Conclusion 

There exists today an overabundance of data. Leveraging the capabilities presented by this new era 

of cognitive computing presents us with the opportunity to provide benefit in many areas. It will be a 

key factor in determining which countries pull ahead economically and which fall behind, which cities 

attract knowledge workers and business development and which businesses thrive. I believe in the 

value and power of information and technology. The United States should continue and increase its 

support of advanced computing and its investment in building its workforce to seize the value that Big 

Data, Analytics and next generation computing offer. 

Thank you for the opportunity to appear before you today to provide this testimony. I welcome your 

questions. 

7 
25 

Dr. David F. McQueeney 

Vice President, Technical Strategy & Worldwide Operations 

IBM Research 

Yorktown Heights, NY 

davidmcq@us.ibm.com 

Dave McQueeney is Vice President, Technical Strategy & Worldwide Operations at IBM Research. 

Dave is responsible for setting the direction of IBM's overall Research Strategy across twelve 

worldwide labs and leading the global operations and information systems teams. In this 

capacity, Dave leads the annual creation of the Global Technology Outlook, 3 -10 year 

technology projections which guide IBM's R&D directions along with its acquisition strategies. 

Dave's background covers a wide range of disciplines ranging from solid state Physics, to highspeed interconnect design, to distributed software development tools, to participation in a 

startup software company in Scientific Data Analysis, to Government-specific industry solutions. 

Dave has spent about half of his career as a researcher and research executive, and half in IBM's 

customer-facing units including Global Services, Sales and Distribution and Software Group. Dave 

has been the ero for IBM's Federal business, the Global Government Solutions General Manager 

and leader of the IBM Federal Systems Integration services unit. 

Throughout his career, Dave has driven strong connections between IBM Research and IBM's 

clients, as well as the other units of IBM. Dave was recognized by Consulting Magazine as one of 

the top 2S consultants for 2002, citing his work to make the innovations of IBM Research directly 

available to customers via IBM's Global Business Services unit. 

Dave has held a number of other significant positions in IBM Research, including Director of the 

IBM Zurich Research Laboratory, Vice President of Communication Technology, and Vice 

President of Software. 

He joined IBM in the Research Division in 1988. He holds an M.S. and Ph.D. in Solid-State Physics 

from Cornell University, and an A.B. in Physics from Dartmouth College. 

Dave was recognized as one of the "Fed 100" top leaders in the Federal community for 2006 by 

Federal Computer Week magazine. 
26 

Chairman BUCSHON. Thank you, Dr. McQueeney. 

I now recognize Dr. Rappa for five minutes for his testimony. 

TESTIMONY OF DR. MICHAEL RAPPA, DIRECTOR, 

INSTITUTE FOR ADVANCED ANALYTICS, 

DISTINGUISHED UNIVERSITY PROFESSOR, 

NORTH CAROLINA STATE UNIVERSITY 

Dr. RAPPA. Good morning, Chairman Bucshon, Chairman Massie, 

Ranking Member Lipinski, Ranking Member Wilson and other 

Members of the Subcommittee. I appreciate the opportunity to be 

here this morning to speak with you about data analytics and the 

role institutions of higher learning can play in advancing the field. 

I am going to draw this morning’s testimony on my own behalf 

as a professor and director of a research institute, educational institute for over the past 25 years. 

I think it is important to start with the fact that the world is 

changing around data very rapidly and our ability to productively 

use it becomes a very central part of what we do as a society today, 

as has been heard already. A generation ago, data was scarce, expensive, time consuming to collect and difficult to analyze. Today, 

data is everywhere. 

Advances in computer technology and powerful analytic tools 

make it possible not only to collect vast quantities of data but also 

analyze and draw insights from data to solve pressing problems 

from increasing operational efficiency to combating fraud, to better 

health care, to protecting national security. Data is everywhere. 

The question is, how well are we prepared to use it? We have the 

data, the technology, the methods and tools, all of which continue 

to advance. The national challenge, in my view, going forward will 

be our ability to educate a data-savvy workforce that has the analytical skills to put data into action. Estimates of the talent gap as 

we have heard are large and growing. 

This is a dire but solvable problem. As we have shown at NC 

State, working closely with employers and focusing on their needs, 

we can produce the kind of talent that is so desperately needed 

today. We do it quickly in just 10 months with a domestic student 

population ranging from their early 20s to their late 50s, many of 

whom are returning to school. We have done this now for six years 

economically with consistently high student outcomes using a sustainable and scalable business model based on self-financed tuition. 

What it comes down to is creative innovation, how we organize 

graduate education, allowing us to engage with employers more 

productively to yield high-quality results in the skills and readiness 

of our graduates. 

I encourage the Committee to focus its attention on workforce 

needs, to encourage the government to seek out innovation in higher education and to promote new and novel learning models. This 

is a solvable problem. With the proper incentives, focused resources, open collaboration with industry, we can produce the analytics professionals needed to extract value from big data and to 

move the economy forward. As I said, we have done this ourselves 

now for 6 straight years to great effect. We will graduate a class 

in a matter of another week, 80 students in the Master of Sciences 

and Analytics Program, with already 95 percent of them placed in 
27 

jobs. They are literally the most sought after and highest-paid 

graduates of the university. 

So we can do this. It is a solvable problem. Thank you again for 

your time. I will be glad to answer any questions. 

[The prepared statement of Dr. Rappa follows:] 
28 

Testimony of 

Michael Rappa, Ph.D. 

Executive Director and Distinguished University Professor 

Institute for Advanced Analytics 

North Carolina State University 

Before the 

Committee on Science, Space, and Technology 

Subcommittee on Technology 

And the 

Subcommittee on Research 

U.S. House of Representatives 

April 24, 2013 

Next Generation Computing and Big Data Analytics 

Good morning, Chairman Bucshon, Chairman Massie, Ranking Member lipinski, Ranking Member 

Wilson, and members of the subcommittees. Thank you for the opportunity to speak with you about big 

data, analytics, and the important role institutions of high education play in advancing the field. 

My name is Michael Rappa and I am the Executive Director of the Institute for Advanced Analytics and 

Distinguished University Professor in the Department of Computer Science at North Carolina State 

University. I am responsible for overseeing the nation's first graduate degree in Analytics, which was 

founded in 2007 and currently enrolls 80 students each year. I also co-direct a government sponsored 

research project that seeks to advance the science of security in cyberspace. 

My testimony today draws on my 25 years of experience in graduate education and research and, in 

particular, my experience over the past seven years leading the Master of Science in Analytics degree 

program at NC State. I will focus on the role different academic disciplines play in the field of Big Data 

Analytics, the success of the Institute for Advanced Analytics in partnering with industry to produce 

analytics professionals and lessons for other universities, and conclude with recommendations for 

policymakers in their support of Big Data and developing the analytics capabilities of the workforce. 

What is the role of different academic disciplines in Big Data and Analytics? 

Many of the things we do each day as individuals and organizations generate data. How much data? No 

sooner would I give you an estimate than that number would be surpassed. The global use of connected 

digital devices-computers, smart phones, tablets, and the like, that propagate numerical, voice, image 

and text data-is growing just that fast. With all of this data come both necessities and opportunities, 

which have given rise to what today is called "Big Data" and the concomitant need for Analytics; namely, 

the tools, methods and applications for drawing insights from large quantities and varieties of data. Big 

Data is a relative term that suggests a realm of data that pushes the upper limits of existing capabilities 
29 

in computation, storage and analysis. In this sense there always has been and always will be Big Data. 

What's interesting about Big Data today is less about its inherent bigness then its ubiquity. A case can be 

made that Big Data is both a systemic and transformative technological phenomenon-the latest phase 

in a decades long process that began with the rise of modern digital computing in the 1940s. The 

computerization of business processes and later personal computing had the effect of creating first large 

and then highly dispersed stores of data. Subsequently, the rise of the Internet and World Wide Web 

facilitated the movement of data, as well as created huge amounts of data through its use-dramatically 

increasing the amount, immediacy, and the interconnectivity of data flowing from an increasing number 

of users and digital devices that now extends far beyond computing in the conventional sense. Thus, the 

attention to Big Data and Analytics should come as no surprise. It is part of a natural progression that 

has grown out of the digital world we live in today. The ability to analyze and draw insights from data is 

without question important. In the opinion of some observers, it has the potential to become a defining 

factor in how well organizations perform their missions in the future. 

Looking at the academic landscape, certain disciplines playa critical role in pushing forward the research 

frontier: work in computer science, statistics, operations research, and applied mathematics is 

particularly relevant. But it doesn't stop there. Just as Big Data and Analytics will touch almost every 

corner of the economy, so too will it draw in researchers from a wide spectrum of disciplines and subdisciplines like the Internet did a generation earlier. I encourage the Committee to embrace an 

expansive perspective in its support of Big Data research in the academic community. When the hype 

around Big Data eventually dies down, we will nonetheless find ourselves in a world saturated with data. 

Our ability to draw value from this data will be as important as it ever was to every sector of the 

economy. Only through sustained investments in the academic community will we ensure the 

technology and workforce necessary to leverage data to its fullest extent. 

Research is needed to continue to advance our understanding of how to collect, store and process 

extremely large amounts of data, where the definition of "large" is a rapidly expanding quantity that 

knows no bounds. Many areas of computation and data analytics are relevant, including high 

performance computing, databases, networking, machine learning, data mining, algorithms, data 

visualization, natural language processing, optimization, geospatial analytics, remote sensors, data 

privacy and security, among others. Again, I suggest taking a broad view. It's important to recognize that 

the academic landscape is fluid with new disciplines and sub-disciplines emerging over time. Under the 

right conditions these new frontiers of knowledge will become woven into the institutional framework 

of universities through the establishment of centers, institutes and, over time, possibly as new schools 

and colleges. 

How academic disciplines combine and split would matter little were it not for the organizational 

boundaries that are created or dissolved in the process. The domain of Big Data and Analytics is not 

unusual in how it spans disciplines, but we need not be so concerned about crossing disciplinary 

boundaries with respect to research. Many incentives are in place to overcome the barriers, and the 

current generation of faculty has grown up in an environment where multidisciplinary work is not 

uncommon, if not the norm. At NC State, for example, we have a large multidisciplinary project on the 

analytics needed to advance a science of security. There are natural hurdles researchers will encounter 

working across disciplines, certainly, but it is something that can be overcome and is overcome on a 

regular basis. 

More attention should be paid to how the interplay of disciplines can facilitate or inhibit the 

2 
30 

development of educational programs that fulfill the needs of employers and an evolving workforce. 

Academic disciplines and the related degree majors don't always align well with occupational roles. 

There is a need to pull together disparate disciplines in graduate education to produce the kind of 

technical professionals employers want, such as data scientists. This can be difficult because it goes 

beyond faculty collaboration to the larger challenge of reorganizing how universities design and deliver 

degree programs. To overcome the projected scarcity of data scientists, universities need to reduce or 

eliminate organizational boundaries so that students can acquire the bundle of skills they will need to 

succeed in the workplace. 

To address this challenge, North Carolina State University partnered with SAS in 2006 to create the 

Institute for Advanced Analytics and embark on a grand experiment in designing a new graduate degree 

in Analytics, the first of its kind. By every measure, the experiment has been an unambiguous success. 

The program's enrollment has grown to 80 masters students annually, with over 9S-percent selffinanced-allowing the program to be self-sustainable based on tuition revenue. As a group they are 

among the most sought after and highest paid graduates of the university. Students come from a variety 

of disciplines and range in age from their early twenties to their late fifties. The majority of students 

return to school from the workplace, and 80- to 90-percent are U.S. citizens or permanent residents. 

This year the Institute will record its sixth consecutive year in which 90-percent or more of the graduates 

are placed by graduation in positions as analytics professionals-despite the worst economy since the 

Great Depression. Key to this success is close collaboration with industry combined with an innovative 

way of organizing graduate education. 

The Institute for Advanced Analytics 

When the Institute for Advanced Analytics was proposed in April 2006, it was conceived as an 

educational initiative intended to address the growing demand for professionals who can draw insights 

from (what we now call) Big Data. At the cornerstone of the proposed Institute was the idea of creating 

the nation's first Master of Science in Analytics (MSA) degree. The proposal was unusual in that it was 

adopted as a greenfield project that allowed us to start with first principles when creating the degree 

program. Furthermore, the Institute was launched as a university-wide collaboration-organizationally 

independent of the colleges-to give it maximum flexibility to blend together whatever disciplines were 

needed in the education without being anchored to a single department or college by default. 

The MSA curriculum was designed by a large group of faculty from more than a dozen academic 

departments across six colleges, in close consultation with colleagues at SAS and a large industry council. 

We shed conventional thinking about degree programs as much as we possibly could. Instead of starting 

with a menu of core courses and electives-the basic inputs-the design process began with a clear 

focus on desired students outcomes, positioning employers as the core customer and seeking to 

understand what they look for when hiring analytics talent. Analyzing job descriptions and talking with 

employers led to a balanced perspective embodying five core objectives for student outcomes that 

guided the MSA: 

Technical skills 

Teamwork skills 

Communication skills 

Tool skills 

Domain knowledge 

3 
31 

When it comes to analytics professionals, employers want to hire individuals with the requisite technical 

skills to clean, analyze and interpret data, but it doesn't end there. Employers also want people who can 

perform well in multi-functional teams, who have strong verbal and written communication skills, who 

have experience programming with industry standard software tools and programming languages, and 

who have prior knowledge of the business domain whenever possible. 

The MSA curriculum was laid out with all of these objectives in mind, not as a series of courses, but as a 

single integrated learning experience. Faculty members are engaged not by the course, but instead in 

customized lecture streams that align closely with their expertise. This allows us to use a larger number 

of faculty and to integrate subject matter across different disciplines with greater flexibility. The result is 

quite different from conventional programs. The MSA is optimized to achieve the five core objectives by 

leveraging both the content and programmatic structure of the learning experience. While the focus on 

employers and the inclusion of professional skills development aligns the MSA with the larger 

Professional Science Masters (PSM) degree movement, it takes the idea a step further than most, given 

the Institute's flexibility in crafting the curriculum. 

The MSA is a cohort-based 30-credit hour curriculum spanning ten months. It uses the intensity of a 

condensed format to immerse students in the learning process. MSA students pursue the degree fulltime-literally. The program calendar runs 9:00 to 5:00, five days a week for most of the ten months 

(vacation time is more limited than the normal student calendar). When students aren't in class, they 

are typically working on a long-term team project (the practicum) or in study groups that rotate 

membership every five weeks. A premium is placed on learning by doing. The result is a highly 

structured team-based learning experience that has more of the look and feel of a normal workplace. 

The MSA places heavy emphasis on "soft" skills. Professional skill training is woven into the learning 

experience throughout the curriculum. Students receive training in public speaking, technical writing, 

teamwork, leadership, project management, and conflict resolution, among other areas. The soft skills 

are not treated as non-credit add-ons or extra-curricula activities. Instead, these skills are taught as an 

integral component of the curriculum and students are measured and tested against them. 

Teamwork is extremely important in most organizations today. Teams form the basis for most of the 

work students perform in the MSA program. The cohort driven format enables the students to work in 

teams continually across the entire learning experience. The team structure of the curriculum and 

diverse student body maximizes peer-to-peer learning, which is known to be very effective and perhaps 

as important as direct instruction. Students are given guidance on teamwork and undergo frequent 

iterations of peer evaluation and feedback on their performance. The peer evaluation is both structured 

and open-ended and forms the basis for personalized coaching to help students improve their ability to 

function effectively in teams. 

At the core of the MSA curriculum is a practicum-a hands-on learning experience that gives students 

the opportunity to conduct real-world analytics projects using data from sponsoring organizations. 

Students work in teams of 4-5 members each to understand an actual business problem and then clean 

and analyze the data. The Practicum spans seven months and culminates with a report and presentation 

to the sponsor. The teams perform their work under confidentiality agreements and the results are the 

sole property of the respective sponsors. Past projects span virtually every industry segmentadvertising, banking and financial services, consulting, e-commerce, energy, entertainment, healthcare, 

insurance, retail, and transportation-and a number of state and federal agencies. The Institute 

currently conducts 17 practicum projects each year. This year's sponsors included: 

4 
32 

Allscripts (Chicago, Il) 

Caterpillar (Peoria, Il) 

Central Intelligence Agency (Mclean, VA) 

Federal Communications Commission (Washington, DC) 

GE Energy (Atlanta, GA) 

Global Knowledge (Cary, NC) 

GlaxoSmithKline (Research Triangle Park, NC) 

Hanesbrands (Winston-Salem, NC) 

Houston Astros (Houston, TX) 

Inmar (Winston-Salem, NC) 

lowe's Home Improvement (Charlotte, NC) 

M& T Bank (Buffalo, NY) 

Monsanto (St. louis, MO) 

North Carolina Department of State Treasurer (Raleigh, NC) 

Procter & Gamble (Cincinnati, OH) 

U.S. Postal Service (Washington, DC) 

The Institute's open solicitation of practicum proposals keeps the curriculum grounded in the kind of 

problems facing industry, which continue to evolve in character and complexity. Dozens of proposals are 

received each year from an equally large number of organizations representing almost every industry 

segment. The proposals inform decisions made about the kinds of methods and tools we choose to 

emphasize in the curriculum, given the limits of time in a 10-month program cycle. 

In both the practicum and class instruction, students learn analytics by using industry standard software 

tools. The Institute's collaboration with SAS is particularly important in this regard. Enterprise class 

analytics tools are a challenge to deploy and maintain in university settings, given the lack of 

standardized platforms and operating systems. Through the generous ongoing technical support of SAS, 

not only do students have the opportunity to use an industry leading tool set, they can (and frequently 

do) complete numerous SAS certifications in route to their degree. The significant market value of such 

industry certifications is well documented and further underscores the readiness of MSA graduates 

when they land on the job. 

The MSA program was designed from top to bottom with the intention of producing the kind of 

analytics talent employers seek to hire. Since the beginning, the focus remained on how to achieve 

successful student outcomes by understanding employer needs. It should be no surprise that no sooner 

did we start to produce graduates from the program than those graduates attracted the strong interest 

of employers. The Institute has achieved over gO-percent job placement by graduation each year since 

the first MSA cohort entered in 2007. The current class of 81 students, which will graduate in a few 

weeks, will enter new positions with over 30 different employers in 12 states and the District of 

Columbia-at record high salaries. Nearly three-quarters of the class had two or more job offers, and 

over half had three or more offers of employment. Five-year benchmark studies put MSA student 

outcomes, in terms of placement and salary, on par with some of the country's most prestigious 

universities. 

Six years later there are now at least two-dozen graduate degree programs in the U.S. that focus 

explicitly on Big Data and Analytics, and new ones are announced with regular frequency. Some 

programs, like one at louisiana State University, have sought to closely replicate our model, while other 

5 
33 

programs, like one at Northwestern, have adopted similar components like the practicum and industry 

standard tools. It's common for schools to work with one or more supportive companies like SAS, IBM 

and others, to help them deploy industry standard tools or share business problems and data with their 

students. While many programs are situated in business schools, it is yet to be seen whether 

organizationally this is the best approach in balancing the disciplinary knowledge needed to produce the 

kind of data scientists required by industry. 

Conclusion 

The value of big data lies in our ability to extract insights and make better decisions. The acute shortage 

of analytics professionals and data-savvy managers will be addressed most successfully through creative 

partnerships between industry, government and universities. There are several efforts underway, but 

we must intensify and accelerate the national investment in proven models. 

I had the pleasure of serving as academic co-chair on the TechAmerica Foundation's Big Data 

Commission, along with many representatives of the Foundation's member companies. Among the 

Commission's findings was a recommendation to continue to invest in research and development of 

advanced computing technologies that can effectively process not only the vast amounts of data being 

continually generated but also the various types. Those investments should focus on key government 

priorities such as education, fraud and abuse, cybersecurity, healthcare and public safety. 

The Commission also recommended a strong focus on skill development in the workforce. Public-private 

partnerships should be strengthened and expanded to invest in skills-building initiatives for the 

workforce in the area of Big Data. While many of the Commission's recommendations were directed 

specifically to the federal government's own needs, it also encouraged the development of dataintensive degree programs and scholarships to prepare a new generation of data scientists. 

The Institute for Advanced Analytics has a proven track record over the past six years with the Master of 

Science in Analytics degree program that shows we can succeed in educating a new generation of data 

savvy professionals to satisfy the needs of employers. We can do it quickly with an intensive and highly 

targeted educational format that yields consistent student outcomes. The program attracts a diverse, 

high quality, domestic student population and yet runs with a sustainable, self-financed business model. 

PSM programs like ours show that graduate education can be designed effectively in collaboration with 

employers to fill the skills gap in critically important areas like data science and cybersecurity. 

Thank you for the opportunity to appear before you today to provide this testimony. I welcome your 

questions. 

6 
34 

Biographical Sketch 

MICHAEL RAPPA 

Michael Rappa is Executive Director of the Institute for Advanced Analytics and a member of the faculty 

in the Department of Computer Science at North Carolina State University. As head of the Institute, he 

leads the nation's first and preeminent Master of Science in Analytics as its founder and principal 

architect. Before joining NC State as Distinguished University Professor in 1998, for nine years he was a 

professor at the Massachusetts Institute of Technology. 

Dr. Rappa has 2S years of experience as a professor working across academic disciplines at the 

intersection of management and computing. An accomplished researcher and instructor, his passion is 

to bring an entrepreneurial and forward-thinking mindset to innovation in higher learning. His current 

role is to prepare a new generation of data savvy professionals for leadership in a digital world. 

In addition to his duties as director, Rappa is co-principal investigator of the Science of Security lablet, a 

large multidisciplinary research project sponsored by the U.S. National Security Agency. The project is 

run in parallel with sister labs at Carnegie Mellon University and the University of Illinois at UrbanaChampaign. 

A study published in 2006 in the British journal R&D Management identified Rappa as a leading scholar 

in the field of technology management, ranking him in the 99th percentile among over 9,000 authors in 

terms of research productivity in top journals over the past 50 years. His research has been cited on 

three occasions as an outstanding contribution to the field, and his early work on business models is one 

of the most often cited and widely read publications on the subject. 

Rappa is perhaps best known as the creator of Managing the Digital Enterprise, an innovative and 

award-winning educational Web site devoted to the study of management in the digital world. 

launched in 1999, originally as the foundation for a course he taught, the site is a valued resource used 

by several million learners and hundreds of university instructors from around the world. In 2010, Rappa 

presided as general co-chair at the 19th International World Wide Web Conference, the premiere annual 

gathering of the Web research community. 

NC State has recognized Rappa on several occasions for his contributions to teaching and service. He is 

the recipient of the Outstanding Extension Service Award, the Award for Graduate Teaching Excellence, 

and the Gertrude Cox Award for Innovative Excellence in Teaching and learning with Technology. He is 

also winner of the MERLOT Award for Exemplary Online learning; a four-time recipient of the IBM 

Faculty Award; and twice a finalist for the Epton Prize. 

Rappa began his teaching career at the University of Minnesota, where he earned his doctorate in 1987. 

7 
35 

MICHAEL RAPPA 

Biographical Statement 

Michael Rappa is Executive Director of the 

Institute for Advanced Analytics and a 

member of the faculty in the Department of 

Computer Science at North Carolina State 

University. As head of the Institute, he leads 

the nation's first and preeminent Master of 

Science in Analytics as its' founder and 

principal architect. Before joining NC State 

as Distinguished University Professor in 

1998, for nine years he was a professor at 

the Massachusetts Institute of Technology. 

Dr. Rappa has 25 years of experience as a 

professor working across academic 

disciplines at the intersection of management and computing. An accomplished researcher and 

instructor, his passion is to bring an entrepreneurial and forward-thinking mindset to innovation 

in higher learning. His current role is to prepare a new generation of data savvy professionals for 

leadership in a digital world. 

In addition to his duties as director, Rappa is co-principal investigator with Dr. Laurie Williams of 

the Science of Security Lablet, a large multidisciplinary research project sponsored by the U.S. 

National Security Agency. The project is run in parallel with sister labs at Carnegie Mellon 

University and the University of Illinois at Urbana-Champaign. 

A study published in 2006 in the British journal R&D Management identified Rappa as a leading 

scholar in the field of technology management, ranking him in the 99th percentile among over 

9,000 authors in terms of research productivity in top journals over the past 50 years. His 

research has been cited on three occasions as an outstanding contribution to the field, and his 

early work on business models is one of the most often cited and widely read publications on 

the subject. 

Rappa is perhaps best known as the creator of Managing the Digital Enterprise, an innovative 

and award-winning educational Web site devoted to the study of management in the digital 

world. Launched in 1999, originally as the foundation for a course he taught, the site is a valued 

resource used by several million learners and hundreds of university instructors from around the 

world. In 2010, Rappa presided as general co-chair at the 19th International World Wide Web 

Conference, the premiere annual gathering of the Web research community. 

NC State has recognized Rappa on several occasions for his contributions to teaching and 

service. He is the recipient of the Outstanding Extension Service Award, the Award for Graduate 

Teaching Excellence, and the Gertrude Cox Award for Innovative Excellence in Teaching and 

Learning with Technology. He is also winner of the MERLOT Award for Exemplary Online 

Learning; a four-time recipient of the IBM Faculty Award; and twice a finalist for the Epton Prize. 

Rappa began his teaching career at the University of Minnesota, where he earned his doctorate 

in 1987. 
36 

Chairman BUCSHON. Thank you for your testimony. 

I now recognize our final witness, Dr. Jahanian, for five minutes 

for his testimony. 

TESTIMONY OF DR. FARNAM JAHANIAN, 

ASSISTANT DIRECTOR FOR THE COMPUTER AND 

INFORMATION SCIENCE AND ENGINEERING (CISE) 

DIRECTORATE, NATIONAL SCIENCE FOUNDATION 

Dr. JAHANIAN. Good morning, Chairman Massie, Chairman 

Bucshon, Ranking Members Wilson and Lipinski, and Members of 

the Subcommittee. It is my pleasure to be back here to discuss the 

next generation of computing and big data analytics. 

Today we live in an era of data and information enabled by advanced technologies that surround us. Data is generated by modern 

experimental methods, scientific instruments such as telescopes 

and particle accelerators, large-scale simulators, Internet transactions, email, video images, clickstreams, and widespread deployment of sensors everywhere. Approximately 90 percent of the data 

in the world today were created in the last two years alone. However, when we talk about big data, it is important to emphasize not 

only the enormous volume of data being generated but also the velocity, heterogeneity and complexity of data that now confronts us. 

Why is big data important? Several others have alluded to this 

already. Data represents a transformative new currency. Big data 

is increasingly important to all facets of our Nation’s discovery and 

innovation ecosystem. First, insights and more accurate predictions 

from large and complex collections of data are creating opportunities in new markets, driving the creation of IT products and services and boosting the productivity of businesses. Second, advances 

in our ability to store, integrate, and extract meaning and information from data are accelerating the pace of discovery in almost 

every science and engineering discipline. Third, big data has the 

potential to solve many of the Nation’s most pressing challenges 

from health care and education to cybersecurity and public safety, 

yielding enormous societal benefits and ensuring sustained U.S. 

competitiveness. 

Let me share with you just a few examples of the promise of big 

data. These are all grounded in research that is funded by the Federal Government or by the private sector, the work that is done in 

the private sector. By integrating biomedical, clinical and scientific 

data, we can predict the onset of diseases and identify unwanted 

drug interactions. By coupling roadway sensors, traffic cameras, individual GPS devices, we can reduce traffic congestion and generate significant savings in time and fuel. By accurately predicting 

natural disasters such as hurricanes and tornadoes, we can employ 

lifesaving and preventative measures that mitigate their potential 

impact. By correlating disparate data streams through text mining, 

image analysis and face recognition, we can enhance public safety 

and public security. By integrating emerging technologies such as 

MOOCs and inverted classrooms with knowledge from research 

about how people learn, we can transform formal and informal education. 

What does this mean for scientific discovery? Data-driven discovery, also called the fourth paradigm, is revolutionizing scientific 
37 

exploration and engineering innovations. It enables extraction of 

new knowledge, provides novel approaches to driving discovery and 

decision-making, yields increasingly accurate predictions and provides deeper understanding of causal relationship based on advanced data analysis. 

What is government doing to ensure we harness this potential? 

As it was mentioned already, in 2011 U.S. Networking and Information Technology Research and Development Program, also called 

NITRD, formed a big data senior steering group to identify, initiate 

and coordinate big data research and development activities across 

the government to ensure that Federal agencies, the scientific research enterprise, and public maximally benefit from data-driven 

discovery. In March 2012, the National Big Data R&D Initiative 

was launched, focusing the steering committee group’s focus on the 

tools, technologies and human capital needed to move from data to 

knowledge to action. We see exciting new partnership opportunities 

with the private sector, state and local governments, academia and 

nonprofits. 

At NSF, we have identified four major investment areas that address current challenges and promise to serve as the foundation of 

comprehensive long-term agenda: first, investment in foundational 

research to advance big data techniques and technologies; second, 

support for building new interdisciplinary research communities; 

third, investment in education and workforce development; and finally, development and deployment of cyber infrastructure to capture, manage, and analyze and share digital data. 

I should add that NSF’s investment in cyber infrastructure includes advanced computational resources that support data-enabled 

science. In particular, the newly dedicated Blue Waters, Stampede 

and Yellowstone supercomputers will expand our Nation’s computational capabilities significantly. 

In summary, big data represents enormous opportunities for our 

Nation. Investments in big data research and education will advance the frontier of knowledge, further fostering innovation, creating new economic opportunities, and yielding new approaches to 

addressing national priorities. 

Thank you again for this opportunity. I would be happy to answer any questions. 

[The prepared statement of Dr. Jahanian follows:] 
38 

Testimony of 

Farnam Jahanian, Ph.D. 

Assistant Director 

Computer and Information Science and Engineering Directorate 

National Science Foundation 

Before the 

Committee on Science, Space, and Technology 

Subcommittee on Technology 

And the 

Subcommittee on Research 

U.S. House of Representatives 

April 24, 2013 

Next Generation Computing and Big Data Analytics 

Good morning, Chairman Massie and Chairman Bucshon, Ranking Members Wilson and lipinski, and 

members of the Subcommittees. My name is Farnam Jahanian and I am the Assistant Director of the 

Computer and Information Science and Engineering (CISE) Directorate at the National Science 

Foundation (NSF). I also serve as the co-chair of the interagency Networking Information Technology 

Research and Development (NITRD) program, which provides a framework and mechanisms for 

coordination among 20 Federal agencies that support networking and information technology R&D. 

I welcome this opportunity to highlight U.S. investments in advanced computing infrastructure and Big 

Data research and education - and how they are leading to transformative breakthroughs in all areas of 

science and engineering, as well as providing enormous societal benefit. The goal is to fund Big Data 

research at the frontiers of knowledge, to capitalize on the intellectual capacity of both early and 

experienced investigators in our Nation's academic and research institutions, and to foster partnerships 

across U.S. government agencies, the private sector and international organizations to effectively 

leverage these investments. 
39 

Overview 

Innovative information technologies are transforming the fabric of society, and data represent a 

transformative new currency for science, education, government and cammerce. Data are everywhere; 

they are produced in rapidly increasing volume and variety by virtually all scientific, educational, 

governmental, societal and commercial enterprises.' 

Today we live in an "Era of Data and Information." This era is enabled by modern experimental methods 

and observational studies; large-scale simulations; scientific instruments, such as telescopes and particle 

accelerators; Internet transactions, email, videos, images, and click streams; and the widespread 

deployment of sensors everywhere - in the environment, in our critical infrastructure, such as in bridges 

and smart grids, in our homes, and even on our clothing! Consider this fact: every day, 2.5 quintillion 

bytes of data are generated - so much that 90% of the data in the world today has been created in the 

last two years alone'. 

When we talk about Big Data, however, it is important to note that it is not just the enormous volume of 

data that needs to be emphasized, but also the heterogeneity, velocity, and complexity that collectively 

create the science and engineering challenges we face today. 

In December 2010, the President's Council of Advisors on Science and Technology (PCAST) published a 

report to the President and Congress entitled, Designing a Digital Future: Federally Funded Research 

and Development in Networking and Information Technology3. In that report, PCAST pOinted to the 

research challenges involved in large-scale data management and analysis and the critical role of 

Networking and Information Technology (NIT) in moving from data to knowledge to action, 

underpinning the Nation's future prosperity, health and security. 

Through long-term, sustained investments in foundational computing, communications and 

computational research, and the development and deployment of large-scale facilities and 

cyberinfrastructure, Federal agency R&D investments over the past several decades have both helped 

generate this explosion of data as well as advance our ability to capture, store, analyze and use these 

data for societal benefit. More specifically, we have seen fundamental advances in machine learning, 

knowledge representation, natural language processing, information retrieval and integration, 

network analytics, computer viSion, and data visualization, which together have enabled Big Data 

applications and systems that have the potential to transform all aspects of our lives. 

These investments are already starting to payoff, demonstrating the power of Big Data approaches 

across science, engineering, medicine, commerce, education, and national security, and laying the 

foundations for U. S. competitiveness for many decades to come. let me offer three examples: 

'''Dealing with Data," Science Magazine, Volume 331, February 11, 2011. 

2 See http://www-Ol.ibm.com/software/data/bigdata/. 

3 For example, some new technologies include smartphones, eBook readers, and game consoles; corporate datacenters, cloud services and scientific supercomputers; digital photography and photo editing, MP3 music 

players, streaming media, GPS navigation; robot vacuum cleaners, adaptive cruise control in cars and real-time 

control systems in hybrid vehicles, robot vehicles on and above the battlefield; the Internet and the World Wide 

Web; email, search engines, eCommerce, and social networks; medical imaging, computer·assisted surgery, and 

large·scale data analysis enabling evidence-based healthcare and the new biology; and rapidly improving speech 

recognition. Our world today relies to an astonishing degree on systems, tools, and services that belong to a vast 

and still growing domain known as NIT. 

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40 

• Today's homes account for more than 20 percent of the total energy consumption in the 

Nation: and about half of that energy is consumed for heating and cooling'. Each degree 

cooler a house is kept in the winter or each degree warmer in the summer can mean energy 

savings of 20%, translating to $200 to $300 in lower energy bills per year - not to mention 

fewer power plants built and lower carbon emissions6

• A team of researchers has pioneered 

an intelligent thermostat that uses machine learning to transform home heating and cooling. 

At first, a person may set the thermostat four times in one day - upon getting up, going to 

work, getting back from work, and going to bed. The thermostat uses those settings daily, but 

then adapts to further changes. If a person is out of town every other Monday on business, for 

instance, the thermostat's sensors coupled with machine learning algorithms detect the lack 

of activity and switch to an "auto away" setting for lower energy use. 

• Collectively, Americans spent nearly 630,000 years - 5.52 billion hours - stuck in traffic in 

2011, at a cost of $121.2 billion in fuel, maintenance, and lost productivity'. A number of 

regional ventures - in Los Angeles, the Bay Area, northern New Jersey, and here in the 

Washington, DC, region - are integrating heterogeneous data sources such as road sensors, 

traffic cameras, individuals' GPS devices, etc., to develop principles and methods that go 

beyond real-time traffic data and allow us to do inference over entire cities··"!o. The aim is to 

identify hotspots and traffic-sensitive directions to drivers well before a potential traffic jam 

materializes. In Los Angeles, for example, city planners have synchronized every one of the 

4,500 traffic signals across 469 square miles of downtown, and they use a system of sensors in 

the road measuring traffic flow, live traffic cameras, and a centralized computing platform 

leveraging data mining and machine learning to make constant, automated adjustments and 

keep cars running as smoothly as possible". Under this system, the average speed of traffic 

across the city has increased by 16%, with delays at major intersections down 12%11. 

• Breast cancer is the most common cancer among American women, except for skin cancers, 

and nearly 40,000 women die from the disease each year". By extending image 

analysis techniques to hundreds of breast cancer biopsy images, researchers were able to 

identify a small subset of cellularfeatures - out of over 6,000 possible features - that was 

predictive of survival time among breast cancer patients13. This feature set was 

unique: pathologists had not previously identified it as relevant to cancer prognosis, and the 

information and insight was above and beyond that of many existing standard measures of 

cancer severity, such as grade, protein markers, tumor size, and lymph node status. The 

4 U.S. Energy Information Administration (EIA). Annual Energy Outlook 2013: Market Trends - U.s. Energy 

Demand. See http://www.eia.gov/forecasts/aeo/MT energydemand.cfm#indus comm. 

5 See http://www.energystar.gov/index.cfm?c=heat cool.Dr hvac. 

6 See http://www.nest.com!saving-energy/. 

7 "2012 Urban Mobility Report," Texas A&M Transportation Institute, December 2012, 

http://d2dtlSnnlpfrOr.cloudfront.net/tti.tamu.edu/documents!mobility-report-2012.pdf. 

8 See http://www-03.ibm.com/press/us/en/pressrelease/34261.wss. 

9 "Trapping 'Big Data' to Fill Potholes: Start-Ups Help States and Municipalities Track Effects of Car Speeds, Other 

Variables on Traffic," The Wall Street Journal, June 12, 2012. 

10 See http://www.cattlab.umd.edu/?portfolio=ritis. 

11 "To Fight Gridlock, Los Angeles Synchronizes Every Red Light," The New York Times, April 1, 2013. 

12 See http://www.cancer.org!cancer/breastcancer/detailedguide/breast-cancer-key-statistics. 

13 Beck, A.H., Sangoi, A.R., Leung, S., Marinelli, R.J., Nielsen, T.O., van de Vijver, M., West, R.B., van de Rijn, M., and 

Koller, D. 2011. Systematic analysis of breast cancer morphology uncovers stromal features associated with 

survival. Sci. Trans/. Med. 3: 108ra113. 

3 
41 

feature set also resulted in an unexpected finding: the features that were the best predictors 

of patient survival were not from the cancer tissue itself, but rather from adjacent tissuesomething that had gone undetected by pathologists and clinicians. These new discoveries 

will allow clinicians to better understand the genesis and morphology of breast 

cancer, enabling personalized treatments that aim to improve survival times among patients. 

These kinds of breakthroughs are catalyzing a profound transformation in the culture and conduct of 

scientific research, requiring new methods to derive knowledge from the data; new infrastructure to 

manage, curate and serve data to communities; new approaches to education and training; and, 

finally, new types of collaborations of multi-disciplinary teams and communities that have the 

potential to solve today's most complex science and engineering challenges. Through the NITRD 

program and its member agencies, the U.S. Government has responded to this Big Data revolution 

with a bold, sustainable, and comprehensive approach. These agencies support the development of 

cutting-edge tools and algorithms for all aspects of the data lifecycle, all with the aim of helping 

scientists to transcend the logistics of data handling and to focus on scientific discovery. Many of 

these advances leverage capabilities in high performance computing, which shares many of the same 

technology challenges as data-intensive science and has become a critical tool in analyzing and 

interpreting scientific data. As part of this overall effort, NSF will also address data access policies to 

better enable science communities to work together to address science challenges. 

Why is Big Data Important? 

Big Data is important to all facets of the discovery and innovation ecosystem, including the Nation's 

academic, government, industrial, entrepreneurial, and investment communities. 

First, insights and more accurate predictions from large and complex collections of data have important 

implications for the economy. Access to information is transforming traditional businesses and is 

creating opportunities in new markets. Further, Big Data is driving the creation of new IT products and 

services based on business intelligence and data analytics, and is boosting the productivity of firms that 

use it to make better decisions and identify new business trends. 

Second, advances in our ability to store, integrate, and extract meaning and information from data are 

critical to accelerate the pace of discovery in almost every science and engineering discipline. From new 

insights about protein structure, biomedical research and clinical deCision-making, and climate 

modeling, to new ways to mitigate and respond to natural disasters, and develop new strategies for 

effective learning and education - there are enormous opportunities for data-driven discovery. 

Third, Big Data will be a key component to solving the Nation's most pressing challenges - in education, 

healthcare, medicine, energy, transportation, commerce, disaster prevention and mitigation, and cyber 

and national security - yielding enormous societal benefit and laying the foundations for U.S. 

competitiveness. 

There are enormous opportunities to harness the increasingly large-scale and diverse data sets, to 

extract knowledge from them, to provide powerful new approaches to drive discovery and decisionmaking, and to make increasingly accurate predictions and move toward deeper understandings of 

causal relationships based on advanced data analysis. These advances witt lead to new innovations, 

job creation, and long-term economic development and prosperity. 

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42 

New Era of Data and Information 

We are now in a new era of observation as well as a new era of data and information14• Today, our 

scientific tools provide an unprecedented sophistication, resolution and scope. Within the NSFsupported research context, these tools can reach the outer edges of the universe as well as dig deep 

into the tiniest phenomenon. They can transcend all scales, from the molecular and genetic to the 

organismal and social. Our ability to gain new knowledge would be impossible without these 

capabilities. 

At the one extreme, advanced research infrastructure -large-scale facilities, experimental tools, and 

cyberinfrastructure - enables new knowledge at the far reaches of the cosmos. For example, the Large 

Synoptic Survey Telescope (LSST), jointly funded by NSF and the Department of Energy (DOE), is 

expected to enter full operations for a 10-year survey beginning in January 2022. This telescope will 

probe mysterious dark matter and dark energy, map small objects in the solar system, particularly nearEarth asteroids, and detect transient optical events such as novae or supernovae - generating 30 

terabytes of data each night that span billions of light years. 

At the other end of the spectrum, we see research that explores phenomena at nano, pico, and femto 

scales. NSF is supporting IceCube, a particle detector drilling three kilometers deep into the Antarctic in 

search of interactions of a nearly massless subatomic particle called a neutrino, which could reveal the 

new physical processes associated with the enigmatic origin of the highest energy particles in nature. 

Likewise, we can explore the properties of a single neuron in the brain, in response to different stimuli 

and stresses - and then integrate data about millions of such neurons to begin to understand not just 

the underlying biology, but also the interconnections of the brain that give rise to the psychology of the 

human mind. 

We have new opportunities with technology as well. For example, with the advent of the Internet and 

mobile devices, "citizen science" is increasingly leading to new knowledge. Take the iPad, for example. 

One can envision a middle-school child anywhere in the world accessing data in real time that comes out 

of a hundred-million-dollar facility funded by NSF. The child could participate in an experiment in which 

he or she actually gathers data where he or she lives. In another example, last year, citizens distributed 

across the U.S. using a software application, Foldlt, together resolved the detailed molecular structure 

of an enzyme that is believed to playa critical role in the spread of the AIDS virus - a breakthrough that 

had confounded scientists for decades15. 

These advances are not only experimental in nature, but also, combined with advances in computational 

hardware and software to capture and make sense of the data, they are equally computational and 

theoretical. 

14 "Vision from the National Science Foundation," A presentation by Subra Suresh, Director, at the National 

Academy of Sciences Symposium on Science, Innovation, and Partnerships for Sustainability Solutions, 

Washington, DC, May 16, 2012: http://www.nsf.gov!news!speeches!suresh!12!sS120516 nas symposium.isp. 

15 See http://cosmiclog.nbcnews.com!news!2011!09!1617802623-gamers-solve-molecular-puzzle-that-baffledscientists. 

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What Does this Mean for Scientific Discoverv? 

Data are motivating a profound transformation in the culture and conduct of scientific research. Datadriven discovery is revolutionizing scientific exploration and engineering innovations. This approach has 

been called the "fourth paradigm," in contrast to the three earlier approaches to scientific research: 

empirical observation and experimentation; analvtical/theoretical approaches; and computational 

science and simulation. Such a data-enabled approach to science complements these other earlier 

approaches, but has the promise to revolutionize science even further. 

Indeed, the fourth paradigm has led to improved hypotheses and faster inSights. From new knowledge 

about protein structure paving the way to advances in biomedical research and clinical decision-making, 

to new ways to mitigate and respond to natural disasters, to new strategies for effective learning and 

education, there are enormous opportunities for this new form of scientific discovery called "datadriven discovery"! 

Data access and analysis are already having enormous impacts. The opportunities for the future are 

immense. Imagine, if you can: 

• Complete health/disease/genome/environmental knowledge bases that enable biomedical 

discovery and patient-centered therapy. The data can be mined to spot unwanted drug 

interactions or to predict onset of diseases. 

Companies that, by linking together finance, human resources, supplV chain, customer 

management systems, can use data mining techniques to get a complete picture of their 

operations - to identify new business trends, operate more efficiently, and improve forecasting. 

• Accurate high-resolution models that support forecasting and management of increasingly 

stressed watersheds and ecosystems. 

• Consumers that all have the information they need to make optimal energy consumption 

decisions in their homes and cars. 

• Accurate predictions of natural disasters, such as earthquakes, hurricanes, and tornadoes, that 

enable life-saving and cost-saving preventative actions. 

• A cyber-enabled world that is safe, secure, and private, enabling assured use of our critical 

infrastructure and on-line commerce. 

• Students and researchers who have access to intuitive tools to view, understand, and learn from 

publicly-available, large scientific data sets on everything from genome sequences to 

astronomical star surveys, from public health databases to particle accelerator simulations, and 

teachers and educators who use student performance analytics to improve learning and 

enhance assessment. 

Many R&D challenges remain. Below is a list of Big Data hard problems that the research community is 

addressing: 

• Many data sets are too poorly organized to be usable. Research must come up with new 

techniques to better organize and retrieve data. 

• Many data sets ore comprised of unstructured data. Research must develop new data mining 

tools and/or machine learning techniques to make these data usable. Opening government data 

to all is one of the first steps to spur innovation. 

• Many data sets are heterogeneous in type, structure, semantics, organization, granularity, and 

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44 

accessibility. Research must find novel ways to integrate and customize access to federated 

data; research must find ways to make heterogeneous data more interoperable and usable. 

The utility of data is limited by our ability to interpret and use it. Research must find better 

usability techniques to extract and visualize actionable information. Research needs to discover 

new techniques for evaluating and showing results. 

• More data are being collected than we can store. With the right data infrastructure, 

practitioners could analyze data as it becomes available; they could immediately decide what to 

archive and what to discard. 

• Many data sets are too large to download or send over today's Internet. With the right data 

infrastructure, practitioners could analyze the data wherever it resides, instead of sending it to 

data centers. 

Large and linked datasets may be exploited to identify individuals. Research on privacy 

protection and Big Data is critical; new techniques and analysis could have "built-in" privacy 

preserving characteristics. 

The landscape of open research and development challenges is vast. American scientists must rise to the 

occasion and seize the opportunities afforded by this new, data-driven revolution. The work we do 

today will lay the groundwork for new enterprises and long-term economic prosperity. 

U.S. Government Response to Big Data R&D Challenges 

The December 2010 PCAST report - Designing a Digital Future: Federally Funded Research and 

Development in Networking and Information Technology'6 - recommended several actions to take 

advantage of Big Data opportunities. 

The potential impacts and outcomes for the Nation are huge - on the economy, the pace of discovery in 

science and engineering, national security, healthcare, education, energy efficiency, real-time labor 

market information, to name a few national priorities. The Office of Science and Technology Policy 

(OSTP) in the Executive Office of the President (EOP) responded to these recommendations, in part, by 

chartering a Big Data Senior Steering Group (BDSSG) that would focus on Big Data R&D under the NITRO 

umbrella. Currently, NSF and the National Institutes of Health (NIH) co-chair the SSG, and membership is 

comprised of representatives from the science agencies, such as NSF, NIH, the National Institute of 

Standards and Technology (NIST), DOE Office of Science, and National Aeronautic and Space Agency 

(NASA), as well as Departments of Defense, Health and Human Services, Treasury, and Commerce 

(National Oceanic and Atmospheric Administration). 

Over the course of the year following its establishment, the BDSSG inventoried existing Big Data 

programs and projects across the agencies and began coordinating their efforts in four main areas: 

investments in Big Data core techniques and technologies; education and workforce; domain 

cyberinfrastructure; and challenges and competitions.17 Other critical areas for Big Data were also 

identified, including privacy issues, open access to government data, and partnerships with industry and 

not-for-profits. 

'6 President's Council of Advisors on Science and Technology (PCAST). Report to the President and Cangress: 

Designing a Digital Future: Federally Funding Research and Development in Networking and Information 

Technology. December 2010. Executive Office ofthe President. 

http://www.whitehouse.gov(sites(default(files(microsites(ostp(pcast-nitrd-report-2010.pdf. 

17 See http:(/www.nitrd.gov/nitrdgroups/index.php?title=Big_Data_(BD_SSG)#title. 

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On March 29, 2012, the Administration launched the National Big Data Research & Development 

Initiative. Led by OSTP, this initiative seeks to greatly improve the tools and techniques used for Big Data 

analysis and the human capital needed to move data to knowledge to action. 

Examples of agency efforts that are well aligned with this initiative include's: 

The Department of Defense (DOD) launched "Data to Decisions," a series of programs that are 

harnessing and utilizing massive data in new ways, and bringing together sensing, perception, 

and decision support to (a) make truly autonomous systems that can maneuver and make 

decisions on their own, and (b) improve situational awareness to help warfighters and analysts 

and provide increased support to operations. 

• Defense Advanced Research Projects Agency (DARPA) announced the XDATA program to 

develop computational techniques and software tools for analyzing large volumes of data, both 

semi-structured (tabular, relational, categorical, and meta-data) and unstructured (text 

documents, message traffic), particularly in the context of targeted defense applications. 

• NIH made available 200 terabytes of data from the 1000 Genomes Project in the cloud 

through Amazon Web SelVices (AWS), constituting the world's largest set of data on human 

genetic variation and enabling genome-wide association studies to understand the genetic 

contribution to disease. 

• Through its Scientific Discovery Through Advanced Computing (SciDAC) program, the DOE Office 

of Science unveiled a $25 million Scalable Data Management, Analysis, and Visualization 

Institute, spanning six national laboratories and seven universities, that is developing "new and 

improved tools to help scientists manage and visualize data" and supporting the scientists in 

their use. 

• The U.S. Geological SUlVey (USGS) John Wesley Powell Centerfor Analysis and Synthesis issued 

awards focused on improving our understanding of earth system science through Big Data, 

including "species response to climate change, earthquake recurrence rates, and the next 

generation of ecological indicators." 

In addition, a number of other agencies are participating, including the Office of the Director of National 

Intelligence (ODNI) through its Intelligence Advanced Research Projects Activity (I ARPA), the 

Department of Homeland Security (DHS), Department of Veterans Affairs (VA), Food and Drug 

Administration (FDA), National Archives and Records Administration (NARA), and National Security 

Agency (NSA)'9. 

Anchoring this coordinated effort, NSF and NIH released a joint solicitation, "Core Techniques and 

Technologies for Advancing Big Data Science & Engineering," or "BIGDATA." This program aims to 

extract and use knowledge from collections of large data sets in order to accelerate progress in science 

and engineering research. Foundational research advances in data management, analysis and 

collaboration promise to change paradigms of research and education, and develop new approaches to 

addressing national priorities. The goal is new capabilities for data-intensive and data-enabled science to 

create actionable information that leads to timely and more informed decisions and actions. It will both 

help to accelerate discovery and innovation in all sciences, engineering and education, as well as 

support their transition into practice to benefit society. 

18 See http://www.whitehouse.gov/sites/default/files/microsites/ostp/big data press release final 2.pdf. 

19 See http://www.whilehouse.gov!siles/defaull/files/microsiles/OSlp/big data fact sheet final 2.pdf. 

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As we enter the second year of the Big Data Initiative, the BDSSG is encouraging multiple stakeholders, 

including federal agencies, private industry, academia, state and local government, non-profits and 

foundations, to develop and participate in Big Data innovation projects across the country. The BDSSG is 

planning an event to announce these new projects and to emphasize the importance of building multistakeholder partnerships in all areas of Big Data science and engineering across the country. 

Collectively, these coordinated activities led PCAST to conclude in a January 2013 update to its 2010 

report on Networking and Information Technology R&D, "Federal agencies have made significant 

progress in supporting R&D for data collection, storage, management, and automated large-scale 

analysis ('big data,),,20. PCAST found that Big Data remains a "critical focal point" in 2012 and beyond, 

and recommended continued emphasis and coordination. 

Coordination of Federal Big Data Investments 

NSF coordinates its Big Data R&D activities with other Federal agencies, including the NIH, NASA, DOE 

Office of Science, DARPA, and many others, through the following "mission-bridging" mechanisms: 

The National Science and Technology Council's NITRD Sub-Committee, of which I am co-chair, 

has played a prominent role in the coordination of the Federal government's Big Data research 

investments. 

• Under the NITRD umbrella, the BDSSG coordinates Big Data R&D across the member agencies by 

1) promoting new science and accelerating the progress of discovery through large, 

heterogeneous data; 2) exploiting the value of Big Data to address areas of national needs, 

agency missions and societal and economic importance; 3) supporting responsible stewardship 

and sustainability of Big Data resulting from federally-funded research; and 4) developing and 

sustaining the infrastructure needed to advance data science. 

Under the auspices of the NITRD program and the BDSSG, various participating agencies 

collectively sponsor workshops, develop joint programs, and invest in other activities that 

leverage their complementary missions. 

Most multi-disciplinary, cross-agency fields of NIT inquiry in which NSF makes investments are managed 

in a similar way (cybersecurity, cyberphysical systems, etc.). 

A Framework for NSF Investments 

At NSF, it is expected that improvements in access, manipulation, data mining, management, analysis, 

sharing and storing of Big Data will provide new insights, change paradigms of research and education, 

and create new approaches to addressing national priorities. NSF has identified four major investment 

areas that address these challenges and promise to serve as the foundations of a comprehensive, 

long-term agenda. They are: 

1. Foundational Research in all Areas of Science and Engineering: Advance the core scientific and 

technological means of managing, analyzing, visualizing, and extracting useful information from 

large, diverse, distributed and heterogeneous data sets. Facilitate the development of new data 

analytic tools and algorithms; scalable, accessible, and sustainable data infrastructure; and large20 See http://www.whitehouse.gov!sites!default!files!microsites!ostp!pcast-nitrd2013.pdf. 

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47 

scale integrated statistical modeling. This research aims to, among other things, advance our 

knowledge and understanding of mathematical and physical systems, the science of learning, and 

human and social processes and interactions. 

2. Cyberinfrastructure: Provide science, engineering, and education with a comprehensive data 

infrastructure that will enable the capture, management, curation, analysis, interpretation, archiving 

and sharing of data of unprecedented scale, parallelism, and complexity in a manner that will 

stimulate discovery in all areas of inquiry, and from all instruments and facilities, ranging from 

campus- to national-level investments. 

3. Education and Workforce Development: Ensure that the future, diverse workforce of scientists, 

engineers, and educators is equipped with the skills to make use of, and build upon, the next 

generation of data analytics, modeling, and cyberinfrastructure. Support new approaches to K-16 

teaching and learning that takes advantage of new cyberinfrastructure and data-driven 

approaches, leading to a national learning laboratory. 

4. Scientific Community Building ond Governance: Support transformative interdisciplinary and 

collaborative research in areas of inquiry stimulated by data through the development of robust, 

shared resources and partnerships across diverse communities. This development must 

acknowledge the new challenges surrounding reproducibility, storage, curation, and open 

dissemination of scientific data in all its forms, and recognize its importance for accelerating 

fundamental discovery, interdisciplinary research, and innovation in society. Open and shared data 

can enable new approaches for communities to address complex problems in science and 

engineering. 

NSF is developing a bold and comprehensive approach for this new data-centric world - from 

fundamental mathematical, statistical and computational approaches needed to understand the data, to 

infrastructure at a national and international level needed to support and serve our communities, to 

policy enabling rapid dissemination and sharing of knowledge. Together, these activities will accelerate 

scientific progress, create new possibilities for education, enhance innovation in society and be a driver 

for job creation. Everyone will benefit from these activities. 

Big Data Research: NSF continues to cast a wide net and let the best ideas surface, rather than pursuing 

a prescriptive research agenda. It engages the Big Data research community in developing new 

fundamental ideas, which are then evaluated by the best researchers through the peer review process. 

This process, which supports the vast majority of unclassified researchers in the United States, has led to 

innovative and transformative results. Indeed, NSF investments today leverage a long history of 

Foundation-wide support for data analytics and computational science. 

In October 2012, just six months after the Big Data Initiative launch, NSF and NIH announced nearly $15 

million in new Big Data fundamental research projects, the first step toward realizing the goals to 

advance the foundational science and engineering of Big Data. We received over 450 proposals in 

response to the joint solicitation, spanning a broad spectrum of R&D activities from new scientific 

techniques for Big Data management, to new data analytic approaches, to e-science collaborations with 

possible future applications in a variety of fields, such as mediCine, physics and economics. 

As an example of the new awards that we made, consider the work of Eli Upfal of Brown UniverSity, who 

is leading a project on data analytics. Dr. Upfal and his team plan to develop mathematically well10 
48 

founded algorithmic and statistical techniques for analyzing large-scale, heterogeneous and so-called 

"noisy" data. The resultant algorithms will be tested on extensive cancer genome data, contributing to 

better health and the development of new health information technology. 

A second example is an award led by Christos Faloutsos of Carnegie Mellon University and Nikolaos 

Sidiropoulos of the University of Minnesota, aiming to develop theory and algorithms to tackle the 

complexity of language processing and to develop methods that approximate how the human brain 

works in processing language. The research also promises better algorithms for search engines, new 

approaches for understanding brain activity, and better recommendation systems for the retail sector. 

NSF also funds center-scale activities. One project announced at the Big Data Initiative launch in March 

2012 was a $10 million award to researchers at the University of California, Berkeley, under the NSF 

Expeditions in Computing program. The research team will integrate algorithms, machines, and people 

(AMP) to turn data into knowledge and insight. The objective is to develop new scalable machinelearning algorithms and data management tools that can handle large-scale and heterogeneous datasets 

(spanning data generated by computers, sensors and scientific instruments; media such as images and 

video; and free-form tweets, text messages, blogs and documents), novel datacenter-friendly 

programming models, and an improved computational infrastructure. The team is focusing on key 

applications of societal importance, including cancer genomics and personalized medicine; large-scale 

sensing for traffic prediction, environmental monitoring, and urban planning; and network security. 

Although the project is only in its first year, it has already led to significant contributions, including open 

source high performance machine learning software, called Spark, that was featured on Siliconangle's 

list ofTop 5 Open Source Projects in Big Data". 

Aside from its investments in fundamental research, NSF also supports development activities beyond 

the stage of research prototypes through its Small Business Innovative Research (SBIR) and Small 

Business Technology Transfer (STTR) programs, as well as the Innovation Corps Teams Program (1-

Corps), which identifies NSF-funded researchers who will receive additional support - in the form of 

mentoring and funding - to accelerate innovation that can attract subsequent third-party funding. 

Big Data Educotion and Workforce Development: A report by the McKinsey Globallnstitute22 estimated, 

"By 2018 the United States alone faces a shortage of 140,000 to 190,000 people with analytical 

expertise and 1.5 million managers and analysts with the skills to understand and make deciSions based 

on the analysis of big data." 

At NSF, investments in Big Data research are accompanied by investments in Big Data education and 

workforce development. Research undertaken in academia not only engages some of our nation's best 

and brightest researchers, but because these researchers are also teachers, new generations of students 

are exposed to the latest thinking from the people who understand it best. And when these students 

graduate and move into the workplace, they are able to take this knowledge and understanding with 

them. Moreover, faculty members in this dual role of researchers and teachers have incentives to write 

textbooks and develop other learning materials that allow dissemination of their work to a wide 

audience, including teachers and students nationwide. 

21 See http://siliconangle.com/blog/2013/02/04/top-5-open-source-projects-in-big-data-breaking-analysis/. 

Z2 Manyika, J., Chui, M., Brown, B" Bughin, J., Dobbs, R., Roxburgh, c., and Byers, A.H. 2011. Big data: The next 

frontier for innovation, competition, and productivity, McKinsey Global Institute: 

http:Uwww.mckinsey.com/insights/business technology/big data the next frontier for innovation. 

11 
49 

Over the years, NSF has supplemented its investments in Big Data by giving additional funding to 

researchers who were willing to bring undergraduates into their labs through the Research Experiences 

for Undergraduates (REU) program. This program gives many undergraduate students their first handson experiences with real science and engineering research projects. In addition, NSF funds up and 

coming young investigators through the prestigious CAREER program that offers awards in support of 

junior faculty who are exemplary teacher-scholars. These awardees conduct outstanding research, 

develop and implement excellent education plans, and integrate education and research within the 

context ofthe mission of their organizations. 

More recently, NSF used its Integrative Graduate Education and Research Traineeship (IGERT), 

mechanism to educate and train researchers in data-enabled science and engineering, including 1) core 

techniques and technologies for advancing big data science and engineering; 2) analyzing and dealing 

with challenging computational and data-enabled science and engineering (CDS&E) problems; and 3) 

researching, providing, and using the cyberinfrastructure that makes cutting-edge CDS&E research 

possible in any and all disciplines. 

Finally, the move from face-to-face to online and blended learning, which allows for learning anywhere, 

anytime, and by anyone, is rapidly transforming education into a data-rich domain. By collecting, 

analyzing, sharing, and managing the data collected through monitoring learners' use of technology, we 

can begin to understand how people learn. The result is an ability to advance understanding of how to 

use technologies and integrate them into new learning environments so that their potential is fulfilled. 

An anticipated cross-disciplinary effort is participation in an Ideas lab to explore ways to use Big Data to 

enhance teaching and learning effectiveness". 

Computational and Data Cyberinfrastructure: NSF has been an international leader in high-performance 

computing (HPC) deployment, application, research, and education for almost four decades. With the 

accelerating pace of advances in computing and related technologies, coupled with the exponential 

growth and complexity of data for the science, engineering, and education enterprise, new approaches 

are needed to advance and support a comprehensive advanced computing infrastructure that facilitates 

transformational ideas using new paradigms and approaches. The goal is a complementary, 

comprehensive, and balanced portfolio of advanced computing infrastructure and programs for 

research and education to support multidisciplinary computational and data-enabled science and 

engineering that in turn support the entire scientific, engineering, and education community. 

Below, I give a few examples, illustrating the range and scope oftoday's computational and data 

cyberinfrastructure. 

Advanced Computational Infrastructure. last October, NSF inaugurated "Yellowstone," one of the 

world's most powerful supercomputers, based at the National Center for Atmospheric Research (NCAR) 

in Cheyenne, WY, and this past March, NSF dedicated two advanced computational facilities, "Blue 

Waters," located at the National Center for Supercomputing Applications at the University of Illinois at 

Urbana-Champaign, and "Stampede," headquartered at the Texas Advanced Computing Center at the 

University of Texas at Austin. 

The three systems will provide the nation's research community with unprecedented computational 

23 See http://www.nsLgov/pubs/2012/nsf12060/nsi12060.jsp. 

12 
50 

capabilities, further enhancing the already potent union between technology and the human mind, 

offering the opportunity to better test and advance great scientific ideas. Each strengthens the other. 

Consider, for instance, the extraordinary capability of just one of these systems, Blue Waters. This 

computing system is equipped with more than 1.5 petabytes of memory, enough to store 300 million 

images from your digital camera; more than 25 petabytes of disk storage, enough to store all of the 

printed documents in all ofthe world's libraries; and up to 500 petabytes of tape storage, enough to 

store 10% of all the words spoken in the existence of humankind. If you could multiply two numbers 

together every second, it would take you 32 million years to do what Blue Waters does each second. 

By all measures, these computers, with their high speed and storage capacity, and the ability to produce 

high-resolution simulations, will have a significant impact on the pace of scientific progress. They will 

expand the range of data-intensive computationally-challenging science and engineering applications 

that can be tackled with current national resources. They will allow today's scientists to better 

understand the workings of Earth and beyond, for example, by helping to trace the evolution of distant 

galaxies, by providing data that contribute to the design of new materials, and by supporting 

researchers trying to forecast tornadoes, hurricanes and other severe storms, and even space weather, 

such as solar eruptions. 

Sloan Digital Sky Survey. The Sloan Digital Sky Survey (SDSS) - one of the most ambitious and influential 

surveys in the history of astronomy - was launched in 2000. It collected more data in its first few weeks 

of operation than had been amassed in the entire previous history of astronomy. Within a decade, over 

140 terabytes of information were collected, representing 35% of the sky. The final dataset includes 230 

million celestial objects detected in 8,400 square degrees of imaging, and spectra of 930,000 galaXies, 

120,000 quasars, and 225,000 stars. 

NSF and NASA jointly support this project, together with the Alfred P. Sloan Foundation, DOE, and 

international partners in Japan and Germany. The protocols developed in this cyberinfrastructure 

underpin astronomical archives the world over, including the Panoramic Survey Telescope and Rapid 

Response System project, now about to issue its first data release, and the planned lSST, which will 

produce approximately the same amount of data as the first decade of SDSS, every single night of its 

operation. A recent survey of literature citations has listed SDSS as the most influential, most cited 

observatory . 

iPlant. ;Plant, a plant science cyberinfrastructure collaborative led by the University of Arizona, utilizes 

new computer, computational science and cyberinfrastructure solutions to address an evolving array of 

grand challenges in the plant sciences. This center is a community-driven effort, involving plant 

biologists, computer and information scientists and engineers, as well as experts from other diSciplines, 

all working in integrated teams. 

An important grand challenge that iPlant is attempting to address (i.e., bridging the divide between 

genotype and phenotype) involves integrating many types of data, including DNA sequences, trait data, 

and geographical occurrence information. The latter is particularly useful, as a large proportion of 

variation in phenotype is due to environmental influences. iPlant's computational capabilities have 

enabled species range models for over 88,000 species across North and South America; this will help set 

the baseline for biodiversity. Catalyzed in part by iPlant efforts, large agricultural datasets have been 

released from Monsanto and Syngenta for use in modeling crop performance under existing and 

predicted climate regimes. 

13 
51 

NEON. A Major Research Equipment and Facilities Construction (MREFC) project, the National Ecological 

Observatory Network (NEON) is a continental-scale observatory designed to gather and provide 30 years 

of ecological data". By making all its data freely available, NEON is providing infrastructure to facilitate 

hypothesis-driven basic biological and ecological research, enabling the development of a predictive 

understanding of the direct effects and feedbacks between environmental change and biological 

processes. 

NEON is unique in its continental reach and longitudinal data collection over several decades, delivering 

and curating a multimodal stream of never-before-available regional and continental scale ecological 

datasets to the scientific community and the Nation. Just as NEON researchers will benefit from access 

to data from federal agency networks, federal agencies will benefit from the techniques, sensors and 

knowledge gained through NEON-enabled activities. NEON's systems engineering-guided deSign, 

construction and operations plans, and formalized transition to operations are defining a new standard 

for research infrastructure deployment and operations. Other Federal Agencies (e.g., US Department of 

Agriculture, NASA) and international groups (e.g., European Union, Australian Terrestrial Observing 

Network) are emulating the standards established by NEON, Inc. 

Big Data Community Building and Partnerships: NSF seeks to enable research communities to develop 

new visions, teams, and capabilities dedicated to creating new, large-scale, next-generation data 

resources and relevant analytic techniques to advance fundamental research across all areas of science 

and engineering as well as to transition discoveries into practice. 

An example of successful community building is EarthCube, which focuses on the development of 

community-guided cyberinfrastructure to integrate big data across geosciences and ultimately change 

how geosciences research is conducted. Integrating data from disparate locations and sources with 

eclectic structures and formats that has been stored as well as captured in real time will expedite the 

delivery of geoscience knowledge. 

In 2013 EarthCube released a solicitation to engage all stakeholders, from geoscientists to computer 

scientists, industry, academia and government, to build on the momentum and enthusiasm generated in 

the past year. The different components of the call will allow these stakeholders to participate in 

developing the next stage of EarthCube. This includes coordination networks to help geoscientists 

develop standards and policies, demonstrations of promising technologies for integrating across 

geosciences data, and activities to plan innovative architectures across the whole enterprise. 

In recent years, the transition of Big Data research results to the private sector has helped bring 

innovative Big Data solutions and technologies to the marketplace, fuel job growth, and promote 

economic growth and improved health and quality of life. Some of the examples I noted earlier in this 

testimony speak to this transition of discoveries into practice. By promoting strong connections between 

academia and industry, NSF further enhances its research portfolio in Big Data with foundational 

concepts and new ideas that are directly relevant to the commercial sector. 

Data Access Policy 

As investments in research, education, and cyberinfrastructure further Big Data science and engineering, 

there is also recognition of the importance of enabling rapid dissemination and sharing of new 

24 See http://www.neoninc.org/. 

14 
52 

knowledge, tools, and expertise. In February 2013, OSTP issued a memo directing Federal agencies to 

develop plans to support increased access to results from federally-funded research". The memo 

focuses on two particular elements. First, peer-reviewed publications should be stored for "long-term 

preservation and publicly acceSSible to search, retrieve, and analyze in ways that maximize the impact 

and accountability of the Federal research investment." Second, digitally formatted scientific data 

resulting from unclassified research "should be stored and publicly accessible to search, retrieve and 

analyze." 

Summary 

In my testimony today, I have tried to illustrate how we find ourselves in the midst of a new era of data 

and information, driven by innovative information technologies that are at the center of an ongoing 

societal transformation in terms of how we live, work, learn, play, and communicate. I have outlined the 

enormous volume, velocity, heterogeneity, and complexity of data being generated through modern 

experimental methods and observational studies, large-scale simulations, Internet transactions, and the 

pervasive use of sensor-based technologies. I have indicated how the U.S. government has responded to 

this new era through a coordinated, multi-agency National Big Data Research & Development Initiative, 

and, in particular, described NSF's role in support of Big Data research, education, and 

cyberinfrastructure. Finally, I have shared with you how these investments are starting to payoff; much 

progress has been made and, in turn, the power of Big Data approaches is evident in nearly all sectors of 

society and across all national priority areas. With robust sustained support for fundamental research, 

education, and infrastructure in the area of Big Data in both the executive and legislative branches of 

our government, there is a unique and enormous opportunity to position the Nation at the forefront of 

advances in science and engineering, job creation, and economic development for decades to come. 

This concludes my remarks. I appreciate the opportunity to have this dialogue with members of the 

Subcommittees on these very important topics, and I would be happy to answer any questions at this 

time. 

25 See http://www.whitehouse.gov/sitesfdefaultffilesfmicrositesfostpfostp public access memo 20B.pdf. 

15 
53 

Biographical Sketch 

FARNAM JAHANIAN 

Farnam Jahanian is the NSF Assistant Director for the Computer and Information Science and 

Engineering (CISE) Directorate. Dr. Jahanian also serves as Co-Chair of the NITRO Subcommittee of the 

NSTC Committee on Technology, providing overall coordination for the NIT activities of 20 Federal 

agencies. 

At NSF, Dr. Jahanian guides the CISE Directorate in its mission to uphold the Nation's leadership in 

computer and information science and engineering through its support for foundational and 

transformative advances that are key drivers of economic competitiveness and critical in achieving our 

national priorities. CISE supports ambitious long-term research and innovation, the creation and 

provisioning of cutting-edge cyberinfrastructure and tools, broad interdisciplinary collaborations, and 

education and training of the next generation of scientists and information technology professionals 

with skills essential to success in the increasingly competitive, global market. 

Dr. Jahanian is on leave from the University of Michigan, where he holds the Edward S. Davidson 

Collegiate Professorship in Electrical Engineering and Computer Science. Previously, he served as Chair 

for Computer Science and Engineering from 2007 - 2011 and as Director of the Software Systems 

Laboratory from 1997 - 2000. Over the last two decades at the University of Michigan, Dr. Jahanian led 

several large-scale research projects that studied the growth and scalability of the Internet 

infrastructure and which ultimately transformed how cyber threats are addressed by Internet Service 

Providers. His work on Internet routing stability and convergence has been highly influential within both 

the network research and the Internet operational communities. This work was recognized with an ACM 

SIGCOMM Test ofTime Award in 2008. His research on Internet infrastructure security formed the basis 

for the successful Internet security services company Arbor Networks, which he co-founded in 2001. He 

served as Chairman of Arbor Networks until its acquisition in 2010. 

The author of over 100 published research papers, Dr. Jahanian has served on dozens of national 

advisory boards and government panels. He has received numerous awards for his research, teaching, 

and technology commercialization activities. He has been an active advocate for economic development 

efforts overthe last decade, working with entrepreneurs, and frequently lecturing on how basic 

research can be uniquely central to an innovation ecosystem that drives economic growth and global 

competitiveness. In 2009, he was named Distinguished University Innovator at the University of 

Michigan. 

Dr. Jahanian holds a master's degree and a Ph.D. in Computer Science from the University of Texas at 

Austin. He is a Fellow of the American Associatian far the Advancement of Science {AAAS}, the 

Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers 

(IEEE). 

16 
54 

Dr. Farnam Jahanian, Assistant Director, 

Computer and Information Science and Engineering 

(elSE) 

Dr. Farnam lahanian serves as assistant director for the Computer 

and Information Science and Engineering (CISE) Directorate at 

the National Science Foundation. He holds the Edwill'd S. 

Davidson Collegiate Professorship at the University of Michigan, 

where he served as chair for computer science and engineering 

from 2007 to 2011. 

Over the last two decades. Jahanian has led several large-scale 

research projccts, studying the growth and scalability of the 

Internet infm~tructure that have ultimately transformed how cyber 

threats ill'e addressed by Internet service providers. His work on 

Internet routing stability and convergence has been highly 

influential within both the network research and the Internet 

operational communities. This work was recently recognized with an ACM SIGCOMM Test of 

Time Award in 2008. His research on Internet infrastructure security formed the basis for the 

successful Internet secmity services company Arbor Networks, which Jahanian co-founded in 

2001. .Tahanian served as chairman of Arbor Networks until its acquisition by Tektronix 

Communications, a division of Danaher Corporation, in 2010. 

The author of over 90 published research papers, .Tahanian has served on dozens of national 

advisory boards and government panels. He has received numerous awards for his reseill'ch, 

teaChing, and technology commercialization activities, including a National Science 

Foundation Faculty Early Career Development (CAREER) Program aWill'd, the Amoco 

Teaching Award, an IBM Outstanding Technical Innovation Award and the governor's 

University Award for Commercialization Excellence. He has been an active advocate for 

regional economic development cfforts over the la~t decade. working with entreprcncurs, 

frequently lecturing on the topic, and serving on numerous advisory boards. In 2009, he was 

named Distinguished University Innovator at the University of Michigan. Jahaniun hulds a 

master's degree and a Ph.D. in computer science from the University of Texas at Austin. He is 

a fellow of the American Association for the Advancement of Science (AAAS), the 

A'Isociation for Computing Machinery (ACM) and the Institute of Electrical and Electronic 

Engineers (IEEE). 

Credit: University of Michigan 
55 

Chairman BUCSHON. Thank you for your testimony. I would like 

to thank all the witnesses for their testimony. I am reminding 

Members that Committee rules limit questioning to five minutes, 

and the Chair at this point will recognize himself for five minutes 

to start the questions. 

First, Dr. Jahanian, the Administration announced their Big 

Data Research and Development Initiative in March 2012 including $200 million in new commitments for big data research initiatives. However, the National Science Foundation, Department of 

Defense, Department of Energy, and other agencies have had significant research programs and data analytics that predated the 

initiative. How has the Administration’s initiative changed the 

ways these agency research programs are coordinated and are we 

effectively managing and leveraging our research investments 

across agencies? 

Dr. JAHANIAN. Thank you for your question. You are absolutely 

right that it is not that suddenly last March we woke up and said 

boy, data is really important, we need to do something about it. 

There has been significant investment by the Federal sector and 

private sector in areas having to do with data. The challenges we 

face are many—stewardship of digital data and software, for example. Many data sets, as was mentioned, are too poorly organized or 

also unstructured. Many data sets are heterogeneous. The utility of 

data is also limited by our ability to interpret them. Many data are 

being collected at a scale that we can’t even store them, let alone 

analyze them. Also, large and linked data sets may be exploited to 

identify individuals and so there are also the privacy issues. So 

there are enormous challenges that we face. 

As you alluded to, on March 29, 2012, OSTP in concert with a 

number of Federal agencies launched the national Big Data Research Initiative. It expands the scope of our activities in several 

directions, for example, state-of-the-art core technologies that we 

need to collect, store, preserve, manage and analyze data, harnessing these technologies to accelerate pace of discovery, supporting responsible stewardship, for example, and sustainable business models for big data. 

There are a number of cross-coordination efforts taking place 

under NITRD. Let me start with NSF. All NSF directorates, for example, are participating in this. A multidisciplinary panel of experts are making a recommendation on funding of this. Furthermore, big data is being coordinated through a senior steering group 

that reports to the assistant directors at NSF for all the coordination because it involves every science and engineering discipline. 

As far as the Federal Government is concerned, the Big Data 

R&D Initiative is coordinated through the NITRD Subcommittee. 

As you know, I Chair the Subcommittee. There is a senior steering 

group that regularly meets to coordinate the activities on many of 

the fronts that I alluded to. There are also enormous opportunities 

not only in terms of joint solicitations but there are a number of 

workshops that we are holding jointly with other agencies including NIH, NIST, DOE, DOD to advance the frontiers of knowledge 

and exploration in big data. 

I should also mention that when it comes to this initiative, we 

can’t forget that the private sector plays a significant role. When 
56 

we think about innovation and discovery ecosystems, not only are 

we talking about universities, we are talking about scientists and 

engineers, you know, a rich, talented labor force, investments in research and education, and of course, a vibrant private sector. So 

there are a number of programs that we have at NSF that attempt 

to connect the dots when it comes to transfer of knowledge. 

Chairman BUCSHON. Thank you. I am glad to hear there is quite 

a bit of coordination at the Federal level because I think all of us 

are concerned about that, and again, investing the taxpayer dollar 

wisely. 

Dr. Rappa, I also serve on the Education and Workforce Committee, and I have got children age 9 through 20, four of them, and 

I have a really strong interest in how we get young people interested in different fields of study, and obviously we have a tremendous challenge not only with this area but many others, and do you 

think that—what are your ideas on how we engage young people 

in understanding what opportunities there are in this area and 

what the jobs of the future might hold? I mean, how do we do that? 

Because, you know, when you go to a high-school class, and I talk 

to a lot of high-school class, people say, you know, not many people 

come up when you ask them what they want to be, you know, they 

want to analyze big data. So how do you do that? What is your recommendation? 

Dr. RAPPA. Well, thank you very much for your question, and I 

understand exactly what you are saying, and I think that things 

are changing. You know, I think it is exactly true that your average 8-year-old doesn’t say they want to grow up, for example, to be 

a statistician. It is not common, unless they are really interested 

in sports. Then you see a sort of nexus there because of the relationship. But I think what is changing is that it is really about producing education, in my case, at the graduate level, reaching further into the pipeline down into undergraduate education and even 

touching upon high school where people begin—where students 

begin to understand how data is really used in action. So it is really about creating, not just sort of creating knowledge or understanding but also applying that knowledge. And when our students—our whole education is driven around the application of that 

knowledge, and so students really understand, and increasingly undergraduates understand that this kind of graduate education is 

going to lead them to a very interesting, compelling professional 

life. 

Chairman BUCSHON. Well, thank you, because I think that we 

do—you know, we do need to have this type of information gravitate down, even to middle-school kids to get them interested, and 

there is a program in Indianapolis called Project Lead the Way who 

I know very well that is beginning to do that at the high-school 

level, and it is showing some success. 

But my time is expired, so I would love to talk more about that 

but at this point I am going to yield to Ms. Wilson for five minutes 

for her questions. 

Ms. WILSON. Thank you, Mr. Chair. 

Along those lines, can you tell me either one of you what skills 

are necessary for the big data workforce? I heard you say something about an analytical something. And also as you are speaking, 
57 

I would like to hear from you what role can community colleges 

play in preparing the next-generation workforce for big data. 

Dr. RAPPA. Thank you very much for your question. I would like 

to try my hand at that. So what is sort of interesting and novel 

about what we have done around the education, we really started 

from scratch in building an entire new graduate degree program, 

and we really wanted to address this question of what skills were 

needed, and we focused ourselves really looking at the employer as 

the customer in a sense, the person, the individuals who buy our 

product and the students and really tried to understand the skills 

that they need, and really where that brings you is that there is 

these technical skills which are important in programming, in 

math and statistics, but employers really want much more than 

that. They want individuals who can work well in teams, who can 

communicate these insights to decision makers, who can actually 

use the tools and apply the knowledge in an organizational context, 

and so we have structured the whole education to build a very balanced set of skills as opposed to what I think is really the conventional approach in graduate education and to some extent undergraduate education to focus on the technical skills almost exclusively. And so really what we need to do is sort of approach the 

whole student. Now, I think community colleges can play a very 

important role because you can really begin to channel pipelines 

where students can go and get the prerequisite knowledge that 

they need, the early levels of math and statistics, before they go on 

to graduate education. Thank you. 

Dr. MCQUEENEY. I would just like to comment that a lot of the 

focus in the past has been on the graduate level of education, as 

Dr. Rappa just talked about, and while we continue to have a 

strong need for Ph.D.’s and computer science and electric engineering and mathematics, the biggest skill gap that we see is at the 

masters level, quite frankly, of people who may not have the mathematical skills to create an entire new type of analysis of data but 

who have more than basic IT skills who actually can understand 

the implications of using different analytical techniques given a 

problem, given a data set with certain statistical properties, what 

would be the appropriate analytical technique to use, and when 

you apply that technique, how could you be sure that the results 

would be reliable and proper, and so a lot of our focus has been 

on creating an intermediate level of skill that has the basic understanding of how to use these tools even if it would fall on someone 

with more of a Ph.D. level of training to create new analytical approaches. 

Dr. JAHANIAN. Representative Wilson, I want to echo something 

that has been said. If you think about big data, let us just step 

back. There are three related problems that go beyond big data. It 

includes all of our IT workforce, computer science, computational 

science and so on. These problems have to do with underproduction, which everybody recognizes, underrepresentation and then 

pipeline issues. Chairman Bucshon already alluded to this, that we 

need to worry about our high schools, we need to worry about the 

pipeline. I have three kids, and I know where we lose our kids, it 

is not in masters or Ph.D., we lose the interest of our kids in high 

schools and middle schools, so that has to be fixed, and there are 
58 

a number of programs that we have initiated, pilot programs that 

try to address that issue. 

Let me share with you one anecdotal sort of evidence that provides data on this. Annualized Bureau of Labor Statistics data predicts that each year we need about 140,000 job openings. We will 

have 140,000 job openings in computing and broadly speaking ITrelated jobs but we are only producing about 100,000 qualified individuals including masters, Ph.D., undergraduate and community 

colleges. In fact, many of these jobs would be available to individuals who have two year or four year degrees. 

Another data point that I want to share with you is that 62 percent of all newly created STEM job openings between 2010 and 

2020 will be in computing and IT. Let us not forget that. And that 

includes data, that includes computational skills and many of the 

other skills that the other witnesses alluded to. Thank you. 

Ms. WILSON. Just in my 16—oh, 10, 9, 8—what would you suggest that we begin to—how do we begin to get children interested 

in these sort of skills? I know every little child has an iPad. They 

can work these computers better than adults. What do you think 

we can do to stimulate that all the way from K–12 and into the 

community colleges so we will have more IT graduates? Do you 

suggest we buy each one—we outfit classrooms with iPads, or what 

do you think? 

Dr. MCQUEENEY. I think there is an intrinsic curiosity in younger folks about a lot of the tools they use to communicate with each 

other that have tremendously greater scalability than the tools that 

I use to communicate with my friends. 

Ms. WILSON. Right. 

Dr. MCQUEENEY. So the essence of what is a large community’s 

opinion on a topic of interest could involve the opinions of thousands or millions of people and so I think a lot of the young folks 

I talk to when I visit K–12 programs or, you know, in programs 

like eWeek, they have an intrinsic sense not only of the device and 

the technology but they have a sense of the reach of that device 

and technology which is the beginning of an appreciation of really 

what we are talking about with big data, that there are trends that 

they can reach with that device, and I think that fires their imagination in a very powerful way. 

Chairman BUCSHON. Thank you. I will now recognize Mr. 

Massie, Chairman Massie, for his questioning. 

Mr. MASSIE. Thank you, Chairman. 

So one of the questions that I have as we deal with the interface 

between government and private industry here is, are you aware 

of any government data sets that we need to get more into the public domain for usage? For instance, I think we have done a pretty 

good job about getting some of the mapping stuff out there but 

some of that map information is old, goes back to the 1940s and 

1950s, and I know the government has been paying for LIDAR 

mapping, which is a high-resolution terrain mapping, and I am 

kind of concerned that that is not getting out there. Are you aware 

of that, and are there any other data sets that would be useful to 

the public that the public has paid for that we might want to work 

on getting out to the public? 
59 

Dr. MCQUEENEY. I think the government has done an excellent 

job and had many initiatives that were very focused on getting that 

valuable data out so it could be used. You mentioned LIDAR. I 

know that one of the uses that is very promising for LIDAR is to 

do something like an inventory of the forests in the country, to actually be able to conduct a definitive inventory. Right now, the 

agencies that are responsible for that use a statistical sampling 

technique but in a world where you can take LIDAR images and 

process that enormous data volume, you are able to move then 

from a statistical sampling basis, which is all we could do before, 

to a more definitive approach to get a very, very good picture of one 

of the more valuable natural resources that needs tremendous 

amounts of stewardship. So I think that is an example of a data 

set that could be extremely valuable. But I think in general, the 

government is very well and properly focused on getting those valuable data sources out. Weather would be another—basic weather 

data would be another good example that can be built on to add 

extra value. 

Mr. MASSIE. Are the other witnesses aware of any data sets that 

we need to promote more? 

Dr. JAHANIAN. I want to highlight a couple of things. I am sure 

you are aware of data.gov, which is a Web site that makes a lot 

of government data sets available, and the goal here is to increase 

public access to high-value machine readable data sets that are 

generated by the government. Hopefully it will create new economic values. There are also a number of activities in encouraging 

the private sector, entrepreneurs to develop applications on top of 

that data. It is not just making the data available but also making 

the data valuable so there are a number of essential activities related to that. 

There was a recent Wall Street Journal article actually that 

highlighted at least a dozen different kind of government data sets 

that have been made available from labor and health violations to 

flu incidents, energy prize, offshore activities, solar information, 

and so on and so on that are interesting. From the National 

Science Foundation’s point of view, I should mention that as you 

may know, we have a number of large facilities—LSST was mentioned, Neon, which is another facility that collects a lot of data, 

will be collecting a lot of data. The science and engineering community needs that data, and many Federal agencies are working very 

hard to make that data available. There are a number of issues 

having to do with open access that go beyond the scope of this 

question. 

Mr. MASSIE. Let me ask a follow-up question to that. So big data 

like any other data could be misused, altered, hacked, illegally 

accessed, and sometimes it may just be an honest mistake. We 

share data that we probably shouldn’t have, for instance, where 

some farm data that got out there and it could really compromise 

our food safety if people know where all the food sources are. How 

do we balance the desire for privacy, actually the constitutional 

right to privacy, with sharing all of this data now that everybody 

is under a microscope? 

Dr. RAPPA. I thank you for your question, and I would like to sort 

of just turn it a little bit because we do work—each year we work 
60 

with about 16, 17 organizations that share data under a confidentiality agreement including three government agencies in which we 

put teams of students working on very complex analytics projects, 

and so while I applaud, and I think it is very important and I do 

think the government is doing a good job at sharing data openly, 

it is a very important thing to do, I think there is also an opportunity to engage the academic community in other ways to help understand that data, which might mitigate some of these issues 

around the privacy element. 

Mr. MASSIE. Dr. McQueeney? 

Dr. MCQUEENEY. Yes, that is an excellent question. Thank you 

for that. One of the things that we can do is to get data about the 

data. We call it metadata. So we analyze the data and we don’t just 

look at what information we can get from the data but we describe 

the data perhaps in terms of its sensitivity—is this more or less 

sensitive from a point of view of privacy or security or secrecy—and 

we can then tag those data sets with metadata that describes the 

implications of using that data and then we can build into the systems that handle the data policies that look not only at the data 

but the metadata that describes what are the contents and what 

are the implications of sharing and combining that data and so we 

can actually build into the foundation of big data systems the ability to interpret policies that we have set in a very conscious and 

clear-eyed way and as they process the data they can be respectful 

of that metadata. The medical community has actually done a lot 

of very good work around patient confidentiality while still getting 

very good pattern analysis of different kinds of outcomes. 

Mr. MASSIE. Thank you very much. My time expired. I appreciate 

your answer and concern for that question, Mr. Chairman. 

Mr. BUCSHON. Thank you, Mr. Massie. I now recognize Dr. Bera 

for five minutes for his questions. 

Mr. BERA. Thank you, Mr. Chairman, and thank you for the series of hearings that we have had on the Subcommittee. It has 

been great. 

You know, big data is incredibly important and how we manage 

data and with the rapidity of how the world is changing. I mean, 

when I think back to being a high-school student, and for me it was 

going and looking at the index cards, walking down and looking in 

the encyclopedia. Now, when my daughter, you know, she has vast 

access, or when I do rounds in the hospital, we would have to race 

down to the library to get information but now before we are even 

finished presenting, the medical students or the residents can just 

look at the latest data on, you know, a device like this and get access to the most accurate and timely information. So it is incredibly 

important that we make these investments to not only manage the 

data, to sort that data and then to make sure it is accessible. It 

is a critical priority that we have that workforce both at the professional level but then also at the management level and I think the 

number that I read was we need about 1.5 million managers. So 

there is a huge need but also a huge opportunity. 

When I think back to the talent that has been impacted in the 

last four years in the recession, you know, there are a large number of extremely intelligent and talented individuals in their 30s 

and 40s who have been hit hard. These are folks like myself that 
61 

were trained for a 20th-century workforce but now we find ourselves in a 21st-century economy. 

Dr. Rappa, are there some best practices—and these aren’t individuals that need to get a graduate degree, you know, they are talented individuals—where we could take them and quickly train 

them for this new economy? Are there examples? 

Dr. RAPPA. Right. So we do offer it as a graduate degree but we 

do this in 10 months, and indeed, a good, fairly substantial, larger 

portion of our population are people who are returning from—or 

coming from the workforce to go through this and some of them are 

in exactly the position that you say. They were transitioning, their 

companies were faltering. And so the key really with this is short 

duration. Ten months is actually a very reasonably good time because you could build the skills that you need. If it is too short, you 

can’t accumulate the skills but the key thing is that you have really demonstrated ROI on that education because that person who is 

coming in to do that has to know that they have a very high probability of getting a job when they leave and at a particular salary 

rate so that they can justify the investment and time, and that is 

really what we have done. 

Mr. BERA. Dr. McQueeney, are there potentially any examples— 

you know, again, a lot of these folks are also paying their mortgage, they have to continue to foot their bills—of possibly even 

doing an advanced work-study type of program where you recruit 

this talent and they are getting on-the-job training as opposed to 

a traditional school model? 

Dr. MCQUEENEY. Yes. In fact, there is a related topic here that 

I think is quite interesting, which is the application of big data and 

analytics back on to the educational process itself. You have seen 

the great upsurge in videos that attempt to replace traditional 

brick-and-mortar classroom attendance, coursework. You have seen 

a number of startup companies formed in this space. If you look at 

the education process, each of us really learns quite differently. 

Some of us may learn more from hearing or from seeing or from 

working problems, and great teachers, great professors are sensitive to how their different students learn and are capable of presenting material in alternate ways to make sure they reach all the 

students. With electronic delivery of course materials and monitoring of student progress, we generate digital exhaust, if you will, 

that describes how that student is learning, how that student responds to the instruction, and for the parts of the instruction that 

are delivered electronically, we actually have the ability to do analytics and to do an optimization process so that each of us on the 

panel might not get the same length of lecture on five different topics. It might be adjusted to our historical learning patterns. 

So we have worked with a number of universities and other, you 

know, non-traditional educational institutions to apply the big data 

and analytics techniques to the education and training process 

itself. 

Mr. BERA. Great. In my last 30 seconds, so we have access to 

data. I think one element that we should also be conscious of is the 

quality of the data because there certainly is very good-quality data 

but at the same time there is very poor-quality data that is out 
62 

there and, you know, any of you who want to comment on how we 

monitor quality? 

Dr. RAPPA. I think most data starts off as bad data, for the most 

part, unless it is being collected in a highly careful way. And so it 

is, you know—I think just as we hear about big data today, we are 

going to hear about bad data in the future. Most projects start out 

where you have enormous front end to them to really understanding cleaning and cultivating that data to make it useful, and 

that is an important part of the educational process. 

Dr. JAHANIAN. I would just add that there are a number of techniques that have been developed and are in development dealing 

with data exploration, data cleaning and so on. Furthermore, when 

we talk about large-scale data sets, there are statistical techniques 

that are being applied that really take care of the noise, take care 

of some of these inconsistencies, and that is one of the attractions 

of big data. 

Mr. BERA. Great. Thank you. 

Chairman MASSIE. [Presiding] Thank you, Mr. Bera. I now recognize Mr. Schweikert from Arizona for five minutes. 

Mr. SCHWEIKERT. Thank you, Mr. Chairman. 

This is one of those types of conversations, you know, we could 

all sit around and buy you some well-caffeinated coffee and talk for 

hours and still have no idea if we made any progress. 

Doctor, is it McQueeney? 

Dr. MCQUEENEY. Yes. 

Mr. SCHWEIKERT. First, you are with IBM? 

Dr. MCQUEENEY. Yes. 

Mr. SCHWEIKERT. In your testimony, help me do a little ferreting 

out here. Hardware technology or IT talent, what is your biggest 

bottleneck right now? 

Dr. MCQUEENEY. There are bottlenecks in a number of areas. If 

I looked at the hardware itself, the biggest challenge getting from 

the petascale to the exascale is actually the power dissipation of 

the systems. The new technology work that we are doing is to get 

the computations more efficient in terms of floating point operations per watt so that if you assembled a system thousand times 

bigger than today’s supercomputers you could house it and cool it. 

Mr. SCHWEIKERT. You don’t want to take down the power grid? 

Dr. MCQUEENEY. The power grid may not in fact be able to supply enough power if we didn’t make some innovations. That is a 

good point. 

Mr. SCHWEIKERT. But hasn’t your company actually been one of 

the leaders at producing some of those breakthroughs? 

Dr. MCQUEENEY. In fact, we have, and in fact, a lot of that history goes back to work that started with the Department of Energy 

many years ago, and this bears on an interesting historical point. 

In a time when we are concerned about making investments efficiently, if I go back to the beginning of the ASCII program with the 

Department of Energy to do the nuclear weapons stockpile stewardship program, the Department of Energy scientists did a very 

careful analysis of what were the core algorithms, the core analytics, if you will, in today’s language, that needed to be done at 

a certain level to provide the mission that they needed to provide, 

and they found that the current path at that time of supercom-
63 

puting was going to take five years to produce a machine that they 

needed in 1 or two years. The analysis they did was thorough 

enough to reveal that there weren’t bottlenecks everywhere but at 

that time there were bottlenecks mostly in the inner process or 

communication. So they made a very thoughtful, very surgical investment in accelerating just the piece that was needed to close 

their mission gap, which was the beginning of a very long run of 

government-industry collaboration. 

Mr. SCHWEIKERT. But you are in some ways heading towards 

where my question is. So if that bottleneck, in today’s world, do I 

find the technology if I went out to the private sector around the 

world that is competing and producing high-end supercomputing or 

is it coming out of a government lab? And I know the pop culture 

terminology is ‘‘public-private partnership’’ but the reality, they do 

operate in pretty substantially different silos. 

Dr. MCQUEENEY. The real forcing function for a breakthrough is 

a critical mission need. So in the case of high-performance computing, it has often been a government agency with a critical mission that—— 

Mr. SCHWEIKERT. But they were doing a specific request for how 

they wanted to manage their data? 

Dr. MCQUEENEY. That is correct, and once that technology is 

available, it can be consumed very rapidly in lots of other applications that could take great advantage of it but didn’t have a compelling enough need to get over that hurdle. That is when the disbursal of technology starts. 

Mr. SCHWEIKERT. Just as an aside, only because I had some acquaintances who were—I used to be an old SQL programmer so I 

am way out of date now. IBM was actually running a fascinating 

large data project where they were doing sweeping data sets 

through the world’s social media and gathering it and looking for 

trends. Can you in 30 seconds or so tell me your knowledge on 

that? 

Dr. MCQUEENEY. Yeah, we have analyzed the public social media 

sources with several of our customers and we can gain a lot of insights. For example, you know, retailers can gain insights about 

trends and their clients. Transportation agencies can gain insights 

about likely traffic congestion. There are many sources of public 

data, both social media and other forms that can be analyzed to reveal patterns about how people conduct their daily activities that 

are very useful for optimizing the public infrastructure. 

Mr. SCHWEIKERT. Forgive me, I am blind as a bat without these. 

Is it Dr. Rappa? 

Dr. RAPPA. Yes. 

Mr. SCHWEIKERT. Isn’t my single biggest problem in big data 

right now is noise that when I put data set after data set after data 

set and build on it, that just small incremental errors actually create really bad decisions on the end? 

Dr. RAPPA. Well, I think part of the education around handling 

big data deals very squarely with the quality of the data and how 

to clean it and cultivate it to reduce the noise, to—— 

Mr. SCHWEIKERT. But you and I can go over a long series of public policies, both state, national, you know, military, others, where 

we built it on really gigantic analyzed data sets and it was wrong. 
64 

Dr. RAPPA. Well, I think that, you know, the challenge here is 

education. So as I alluded to earlier, we have teams of students—— 

Mr. SCHWEIKERT. Is it education or developing educational skepticism? 

Dr. RAPPA. It is developing the education around how to squarely 

understand the inherent challenges in data. Data is not born clean. 

It isn’t born ready to be analyzed. 

Mr. SCHWEIKERT. And when you and I build our model, the way 

we wait, you know, because we start plugging in human factors 

that, you know, you and I bring our biases and we—— 

Dr. RAPPA. And this is why we really need a focused education 

squarely around how do you draw insights from data because there 

are these inherent problems in data, especially as you scale them 

up, as you combine different data sets, as you combine different 

types of data. 

Mr. SCHWEIKERT. Thank you, Doctor, and Mr. Chairman, thank 

you for tolerating. It is just one of my great fears. And look, I am 

a data freak. I mean, you have got to see the servers and stuff I 

have at home. But I have learned when we make big-time public 

policy on something we all know is right, we keep making huge, 

very costly mistakes. 

Chairman MASSIE. Thank you, Mr. Schweikert. I now recognize 

Mr. Hultgren from Illinois for five minutes. 

Mr. HULTGREN. Thank you, Mr. Chairman. Thank you all for 

being here. First of all, I just want to thank Dr. McQueeney too. 

I appreciate your mention and your support for the exascale computing bill I am currently authoring. I am very excited about the 

potential there and see some huge shift in our national computing 

capabilities and I am very excited about that, so I appreciate your 

mention and support of that. 

I do have a few questions, and first I guess I would address this 

one to Dr. McQueeney and also Dr. Jahanian. Is that right? I am 

sorry. I wonder if you could comment briefly on where the United 

States stands in your opinion in worldwide computing leadership? 

I know the metric of the fastest supercomputer is one metric but 

what do you use as a metric for big data to determine which countries are using it most effectively? 

Dr. MCQUEENEY. The common thing that is cited in these discussions is the top 500 supercomputers list. That is something that is 

compiled twice a year, as you well know, and we have usually been 

at the top of that list. We have continued to be the majority of the 

systems on that list but other countries have noticed the success 

that we had in, you know, government leading the way on highperformance computing breakthroughs. Once those systems are 

built, they find hundreds and thousands of other applications, each 

with a client that might not have been able to fund that breakthrough themselves but can certainly utilize it. Other countries 

have popped up on the top of that list because they are interested 

in emulating the success we have had in leading the way with innovation and then seeing that innovation used broadly across the 

commercial sector. So the top 500 list is a very technical, perhaps 

very geeky measure of who is on top, and I would say that we are 

still in a leadership position there but it has been stronger in the 

past than it is today. 
65 

If you turn to more of a business view, you would want to look 

at the companies that were taking the best advantage of data 

sources, either to drive value in their companies or to provide benefits such as public safety or health benefits, and there again I think 

we are in a good position but it is a very different kind of skill, a 

conversation we didn’t quite finish before about the skill to build 

these large systems is a very focused, very large-scale, very capitalintensive activity but the skills to use these systems are more focused on creativity and are actually better done by large groups of 

small teams. In fact, you know, the NSF has been a leader in fostering that kind of innovation where thousands and thousands of 

groups can build innovative applications and take advantage of 

these systems. 

Mr. HULTGREN. Thanks. Dr. Jahanian? 

Dr. JAHANIAN. Yes, just a couple of quick comments. There is no 

question that we continue to maintain our leadership worldwide in 

this area, and there is no doubt that continued investment in this 

area is extremely important to the future of the country. As I mentioned just a few minutes ago, NSF’s investment in Blue Waters, 

Stampede, as well as the Yellowstone supercomputing centers represent a range of investments that we make in high-performance 

computing, addressing the needs of not only the top five percent of 

application that have exceptionally high computational needs but 

also a broad spectrum of researchers across the country in science 

and engineering who would need computational resources. 

A couple of comments. Just look at Blue Waters, for example, 

which is at University of Illinois. A couple of data points about it. 

It has—if you could—just the computing power of it, if you could 

multiply two numbers together every second, it would take 32 million years to do what Blue Waters does in one second. That is astonishing power, for example, of Blue Waters. In terms of storage 

capacity, memory capacity and so on, there is a similar kind of 

scale. 

The second point that I want to make is, we view computation 

and data to be two sides of the same coin. You really need to address both. So when we talk about computational capabilities, we 

also have to worry about cyber infrastructure to manage, to curate, 

to serve data to science and engineering community, and the investment in cyber infrastructure has to be balanced between the 

computation side of it as well as management and curation of data. 

Mr. HULTGREN. Let me have—my time is running out but I have 

a follow-up question to the two of you as well if you could both comment in the time I have. It seems to me that exascale computing 

is focused on solving discrete problems that necessitate massive 

computing power and speed. Are these different problems than 

those we are addressing through big data analytical tools and how 

do these two terms, how are they different, how are they similar? 

Dr. MCQUEENEY. Historically, we have tended to talk about them 

differently, but as we project how the exascale systems will be built 

and how they will be used and we look at the growing importance 

of big data analytic systems, we see that the platforms on which 

these systems will both depend will be much more common than 

separate, and in fact, we see that there is no conflict between investments in classically what we have called HPC and what we are 
66 

now calling big data analytics, and both are changing actually. The 

way we use an exascale system will not be the same way that we 

use a petascale system. There isn’t time here to go into it, but it 

actually morphs into a direction that is much more common with 

what we will do in big data and analytics. 

Dr. JAHANIAN. I would just add that many of the problems that 

the business community needs, the science and engineering community needs are being addressed today through different kind of 

computational architectures that doesn’t necessarily require today 

to have exascale computing including weather modeling, a number 

of other applications that have been mentioned. So it is really important to consider the investment in exascale computing in the 

spectrum of investment that we make to support computational 

and data needs of the entire science and engineering community 

and of course the private sector. 

Mr. HULTGREN. Thank you so much. Chairman, thank you. I 

yield back. 

Chairman MASSIE. I now recognize Mr. Lipinski from Illinois for 

five minutes. 

Mr. LIPINSKI. Thank you, Mr. Chairman. I am glad that Dr. 

Jahanian mentioned Blue Waters there. We were just there not 

that long ago, but since you covered that, I can move on to a different area. 

Dr. McQueeney, in your testimony you talk about how the Federal Government needs to invest in big data if the U.S. is going to 

maintain its leadership and competitive edge in this area. The 

needs and potential benefits of big data for the Federal Government align closely with those of private industry in a number of 

areas. If that is the case, how can the Federal Government more 

effectively partner with industry to achieve common goals and do 

you believe that industry has sufficient input in the Federal Government’s research agenda as it relates to big data? 

Dr. MCQUEENEY. I do think we have sufficient input. I think we 

have excellent dialogs with the relevant agencies and national laboratories, and I think the roles are complementary. I go back to the 

story about the early days of the ASCII program where through a 

collaboration we realized that the key piece of a supercomputing 

system that needed to be accelerated was not the entire investment. We could ride on the commercial investments for most of the 

components of the supercomputing systems at that time except for 

one, which was the high-bandwidth switching between processors. 

And so that kind of thoughtful connection between the leaders in 

commercial computing and the leaders on the government side has 

been able historically to identify which areas are critical to attain 

government mission imperatives and where we can leverage commercial technology and where we need to accelerate that in a surgical fashion. So it has, in our view, been a very good partnership 

based on very high-bandwidth technical communications, understanding of applications and knowing when the government should 

be leveraging commercial investments and when they need to accelerate parts of that investment to attain unique mission goals, and 

again, as I have said before, once those barriers are crossed in 

terms of either the scalability of the system or the internal bandwidth of the system, it opens up thousands of new applications 
67 

where there were ready problems to be analyzed but those applications weren’t large enough to drive that breakthrough. So that is 

how the effect works of the leadership coming from some of the 

government agencies and then being realized broadly across industry. That is the essence of where this leadership has come from so 

successfully over the years. 

Mr. LIPINSKI. I want to follow up with Dr. Rappa on that. Dr. 

Rappa, you discussed the importance of public-private partnerships 

to realizing the benefits of big data and stated specifically that we 

must intensify and accelerate the national investment in proven 

models. What characteristics make a public-private partnership 

successful and what models should we be investing in? What were 

you referring to there? 

Dr. RAPPA. Well, I think first of all, we have been doing this now 

for six years and so I think we do have a fairly interesting, novel 

model for producing talent in this field with a kind of proven track 

record based on data, based on market value of the graduates, but 

I think it comes really, you know, partly from the university community, partly from the academic community. Obviously we have 

a set of missions to educate students but we need to also, I think, 

do that by trying to really understand the employer, what are they 

looking for when they hire talent, what are the kinds of skills that 

they need in order to be effective on the job, and I think employers 

need to sort of be open to working with the academic community. 

You know, there is a certain amount of dissidence that naturally 

occurs because there are two different worlds with different missions but I think it is really—I think we have shown that it is possible with organizational innovation, with a focused effort, with a 

sense of openness to engage the private sector in a very positive 

way, not just at NC State but at other universities. There are 

many, many examples now that I hope we are providing some leadership on but that other universities are working with our model 

but also pursuing other creative models to do this. There are probably about two dozen around the country already. 

Mr. LIPINSKI. Thank you. Dr. Jahanian, anything you want to 

add about public-private partnerships? 

Dr. JAHANIAN. Yes, indeed. There is no question that when we 

think about the innovation ecosystem in this country, it includes 

academia, it includes the private sector, it includes government investment and a talent-rich workforce. The private sector is investing heavily in cloud computing, as you know. It is investing heavily 

in making computational resources also available. I think there are 

opportunities for the Federal investment to leverage that and make 

some of that available. Of course that is commercially available 

today to our researchers, to our scientists and engineers who could 

rely on those systems. We have announced a number of partnerships, one with IBM and Google, another one with Microsoft that 

make some of those resources available to the research community. 

Dr. McQueeney already mentioned this, that there is high-bandwidth communication between the private sector and various Federal agencies. I can tell you from NSF’s perspective, it is a very, 

very rich collaboration. On my advisory committee, I have a number of the senior leader from the private sector who serve on my 

advisory committee advising us on our portfolio, on our invest-
68 

ments in addition to academics who serve on my advisory committee. 

The final comment that I want to make is, there are a number 

of programs at NSF, and I know you are familiar with all of them, 

including SBIR, including I–Corps and so on that focus on transfer 

of knowledge from lab to practice. Federal Government invests 

heavily in advancing frontiers of knowledge. For us to accelerate 

those programs such as I–Corps, SBIR and so on serves a tremendous purpose, and here again, there are opportunities to engage the 

private sector and accelerate the transfer of knowledge to practice 

to benefit the Nation. Thank you. 

Mr. LIPINSKI. Thank you. 

Chairman MASSIE. Thank you, Mr. Lipinski. I now recognize Mr. 

Bridenstine from Oklahoma for five minutes. 

Mr. BRIDENSTINE. Thank you, Mr. Chairman. 

I also serve on the House Armed Services Committee, and I am 

aware that the Department of Defense is moving towards cloudbased computing solutions, and this of course creates some consternation about security issues, cyber hacking, other cyber crimes, 

and I was wondering if any of your organizations are involved in 

helping the Department of Defense work through these issues and 

what those solutions might be, if you could share with us on that? 

Dr. MCQUEENEY. Sure, if I could start? You are quite right to 

raise the concern about security for any systems used by the Defense Department especially, although it would be true for all Federal agencies. And when you move to a cloud computing model, 

there is an extra imperative to be concerned about security, and if 

you think of it in terms of the DOD might think of it, if that environment should be compromised by an enemy, it is a bigger piece 

of resource than an individual machine so it requires special vigilance. Now, the good news technically is, the way we handle 

virtualization, which is the foundation of how cloud computing is 

delivered from a compute virtualization point of view, there are actually sophisticated techniques that can provide additional security 

in a virtualized environment that we can provide even when using 

things running on bare metal. We have additional abilities to instrument the operation of that cloud and to very rapidly detect any 

kind of pattern or behavior that is indicative of a threat. 

We did a project a number of years ago with the U.S. Air Force 

and they graciously let us write a short press release on it where 

we built a cloud computing environment that was at the cutting 

edge a few years ago. We instrumented it very thoroughly with 

watching the package flowing on the interconnected network that 

built the cloud in question and we very carefully isolated it from 

the rest of the world, introduced known cyber attacks into it and 

were able to show that if we knew the patterns of command and 

control, as the defense folks might say, of these cyber attacks, we 

could actually spot them assembling themselves and interrupt 

them before they had a chance to launch. So having tremendous 

control over the environment out of which we were getting compute 

resources gave us abilities to do additional security and additional 

monitoring, even if we assumed the security was not perfect and 

could be breached, could we essentially in real time detect that 

breach and interrupt it before it stopped. I thought that was a very 
69 

forward-looking piece of work that was driven by the Air Force 

CIO’s office. 

Mr. BRIDENSTINE. Excellent. Go ahead. 

Dr. JAHANIAN. As you alluded to, these new environments, 

whether it is mobile platforms or cloud computing, are introducing 

new challenges, and we recognize that attackers and defenders are 

coevolving and there are enormous challenges to protecting our 

critical infrastructure and our cyber infrastructure. 

I wanted to mention NSF’s Secure and Trustworthy Cyberspace 

program, which is a research program addressing many of the challenges that we alluded to, and this is a research program that addresses not only the technology issues but also transition to practice. Furthermore, the NITRD research and development subcommittee has a working group that focuses on coordination of activity across various agencies on cybersecurity and there is rich dialog involving various agencies on that issue. 

Mr. BRIDENSTINE. Excellent. Are there any other things that the 

Department of Defense could do to help you guys with the objective 

of securing cloud computing for the Department of Defense? 

Dr. RAPPA. So I am currently co-directing a project with a colleague at NC State, which is the science of security project that is 

done in collaboration with Carnegie-Mellon University and University of Illinois, and we are trying to bring together large groups, 

multidisciplinary groups of faculty to really try to understand the 

underpinning of the security problem and how to produce science 

around it. It is a very long-term challenge but it is one which I 

think has to start with getting the faculty across different disciplines focused on it and certainly I think it has been a tremendous opportunity and I look forward to moving into the future. 

Dr. MCQUEENEY. Yeah, Dr. Rappa makes a very interesting 

point, to close the loop here. The cybersecurity problem is itself a 

big data and fast-data problem, and in fact, with some of the advanced persistent threats that we see today, which depend on 

breaching an infrastructure and then laying dormant for several 

months, what the attacker is trying to do is to wait out how long 

you keep your log file data so that when they launch themselves, 

it is difficult to do forensics, and so what we have learned is that 

these log files are actually the essence of the big data you need to 

do pattern analysis, pattern discovery on forensics, you know, 

should any attack occur. So in fact, most of the science behind big 

data including data at rest and large-scale computation and fastdata that are eating very high-speed streams is directly relevant to 

the subject of cyber defense. 

Mr. BRIDENSTINE. Thank you. 

Chairman MASSIE. Thank you, Mr. Bridenstine. If the Ranking 

Member is amenable to this, I think we will do another round of 

questions? 

Ms. WILSON. Yes. 

Chairman MASSIE. Did you have something to introduce into the 

record? 

Ms. WILSON. I do. Thank you, Mr. Chair. Mr. Kilmer has lots of 

conflicts. As we saw him come to the meeting, he had to leave, and 

I want to ask unanimous consent on behalf of Mr. Kilmer to intro-
70 

duce a report on big data from IDC into the record, and then I have 

a question. 

Chairman MASSIE. Without objection, so ordered. It will be set 

into the record. 

[The information appears in Appendix II] 

Ms. WILSON. Thank you. This question is for everyone. 

We have all had several discussions lately about the value of 

NSF-funded research to society and how we might certify that 

value based on the grant proposal. I think we might use big data 

instructively here. It is an incredibly interdisciplinary field where 

tools are developed in the pursuit of one narrow research question, 

let us say in the social sciences might have profound applications 

across many fields of science and even in many sectors of the economy that can’t possibly be anticipated at the time of the proposal. 

What is the potential for data analytics being developed in one little seemingly irrelevant corner having unintended benefits to other 

fields and societal applications? And if you have concrete examples, 

that would be even better for us to understand. Thank you. 

Dr. JAHANIAN. Okay. I guess I will start. There is no question 

there are all sorts of explorations that we are doing in the area of 

big data that we can’t even begin to see the potential impact of it. 

I will give you an example. NSF has been investing and other 

agencies with the private sector in what is known as the area of 

machine learning. These investments have taken place for at least 

20 or 30 years. In fact, IBM has also led efforts in this area. I can 

tell you that it is investments of the last 20 or 30 years that have 

come to fruition such that these machine learning algorithms essentially allow us to look at these large data sets and identify 

trends and be able to adapt. Essentially, they have a broad range 

of applications from weather forecasting to financial modeling to 

biomedical research and so on that have had tremendous, tremendous impact and now we use these techniques as if they are offthe-shelf solutions available that you can buy. These are through 

years of investment that we have made that have come to fruition, 

so that is an example of that. 

We are investing in all sorts of areas in natural language understanding, in information retrieval, in various algorithms and approaches to automated scalable approaches to reasoning that could 

be applied to understanding relationship between gene sequence 

structure and biological functions. These are all essentially the 

kinds of investments that we are making that some of us we could 

see how it comes to fruition. Some of it relies on decades of investment that we have already made in computational techniques and 

data-intensive techniques. 

Dr. MCQUEENEY. If I could offer you an example from the medical world, one of the critical problems in medicine is the loss of 

premature infants due to infections, and physicians have struggled 

for a long time with identifying the onset of an infection at a very 

early point because as these infections can grow exponentially, the 

earlier you can intercept them, the more likely you are to have a 

lifesaving benefit for someone who is very vulnerable such as a premature infant. We have done work with the Toronto Hospital for 

Sick Kids where a physician up there had an idea that all the instrumentation in the NICU that is—you know, you have probably 
71 

been in a hospital room or intensive-care room, all the instruments 

around the bed, someone comes in every half an hour and writes 

down those numbers but the instruments are producing readings 

continuously, and this physician had the idea that if we kept all 

that data and we stored all that data as it came out of the machines in real time, which was a tremendous aggregation from a 

velocity of data point of view and correlated with the eventual 

issues that these premature infants had, we might be able to detect 

patterns using techniques such as machine learning that we were 

just hearing about that would give us an early identification of an 

upcoming infection, the ability to treat it before it got out of control, and her theories were absolutely correct. There were signatures in the data that gave up to 24 hours advance notice of an 

onset of an infection that was time for the doctors to in many cases 

provide some kind of lifesaving therapy. So there is an example of 

very, very deep mathematics, computer science being applied to a 

problem where the data was being produced every day by these instruments and it wasn’t being captured and it wasn’t being looked 

at and it wasn’t being correlated with results to produce a fantastic 

outcome. 

Dr. RAPPA. I would just sum up by saying that really big data 

is part of a decades-long process that really started with computerization in the 1940s and 1950s and eventually got interconnected 

through the Internet in the 1970s, 1980s and 1990s that the world 

that we are turning into, data is going to be everywhere. It is going 

to affect exactly what happens here. It is going to affect hospitals, 

universities, every corner of the economy literally, and so we need 

to take approaches to that to try to develop understanding around 

big data, how it is applied, how the tools of analytics are applied 

across, you know, virtually every sector of the economy, and so I 

would take a very broad view, not looking at it as specifically, you 

know, a realm of computer technology or some other sort of isolated 

realm but looking at it as, you know, unfortunately as the big thing 

it is. 

Dr. JAHANIAN. May I offer another example as I was thinking 

about it? I am reminded of the work by Daphne Koller and her collaborators at Stanford on classifying breast cancer via image analysis. As you know, 40,000 women die from this disease each year. 

By extending essentially image analysis techniques to hundreds of, 

I should say thousands and thousands of biopsy images, they were 

able to identify a subset of cellular features. Out of 6,000 possible 

features, they were able to essentially identify a few of them that 

were predictive of survival time among breast cancer patients. 

What is really surprising is that the feature that they identified, 

it wasn’t just from—the best feature, I should say, that is a predictor of survival, was not from the cancerous tissue itself but it 

was from the surrounding tissue, and that has led to new kinds of 

treatments. It has led to new kinds of diagnosis techniques and 

also a very personalized treatment that could aim to improve survival times in patients. That is a very, very concrete example. 

Another example is the work that Google had done during H1N1 

virus. I will be very brief about this. Before they actually discovered a vaccine, we wanted to track the spread of disease. Google 

engineers used data that had nothing to do with the virus directly 
72 

from billions of essentially web searches from around the world together from publicly available, essentially historic data on flu 

trends, to predict the spread of flu virus down to small regions in 

the country—or across the world, rather. This is a remarkable essentially application of data that one would have never thought 

could be applicable to something like H1N1 virus. 

Ms. WILSON. Thank you very much. 

Chairman MASSIE. Thank you, Ms. Wilson. Thank you for that 

very excellent example of how we can use—a private company can 

find information in the data. 

We got a little bit out of order so the last question is going to 

be mine. I reserve five minutes for myself. And the question I want 

to ask is, we have heard about banks that are too big to fail, and 

we also know that the Internet is now too big to fail. We recently 

in the House passed a CISPA bill which is somewhat controversial 

but some people felt it was necessary to do because the Internet 

was so big and pervasive in our lives. So my question to you is, are 

there any big data sets that are too big to fail? In other words, are 

there ones that are pervasive that we have let through osmosis become—we have become too dependent upon or maybe not too dependent but we are dependent upon these data sets, for instance, 

weather, you know, and early warning systems? Not all of those, 

I imagine, are government systems. Some of them are private but 

possibly the government is relying on these systems and so I would 

be remiss if I didn’t ask this question now before something fails, 

but tell us what is too big to fail right now? What would we bail 

out, and is there sufficient redundancy in the collection, storage 

and access of these data sets? Thank you. 

Dr. MCQUEENEY. Well, first, I would just like to say that we were 

delighted to support that cyber bill, and I congratulate you on such 

broad bipartisan support in the House for getting that acted upon. 

Data sets have the property that they can often be subdivided 

and often be replicated, and so we have a lot of techniques by 

which we can assure the continuity of data if we take the time to 

do it, and if there were very valuable historical records on things 

like long-term weather trends that were only stored in one place, 

that actually could be a concern because that is literally irreplaceable data. But essentially all of the IT techniques needed to take 

those large data sets and segment them and replicate them in different secure places so they could be re-created do exist but I think 

you raise an interesting point, that it is worthwhile to periodically 

check that we are being appropriately vigilant with the digital archives that are so valuable. 

Chairman MASSIE. Dr. Jahanian? 

Dr. JAHANIAN. I don’t have a specific example. What I can tell 

you is that similar to the issue of cybersecurity, as Nation’s critical 

infrastructure and more generally the Internet is playing a vital 

role in integrating the economic, you know, political, societal fabric 

of our society, we are going to become more and more dependent 

on data, and data is going to play an increasingly significant role 

in our day-to-day lives, and for that reason, I think the same sort 

of issues that apply to all sorts of IT solutions that we take for 

granted will increasingly be applied to data. 
73 

From a research and engineering community’s point of view, it 

is not just failure of the data but making that data accessible and 

also making the data accessible to broad community of scientists 

and engineers is an issue that we are quite concerned about. 

Chairman. MASSIE. Thank you very much. I was part of the bipartisan on CISPA, opposing CISPA actually, but that is okay. 

I want to thank the witnesses for their valuable testimony and 

the Members for their questions today. The Members in the Committee may have additional questions for you, and we will ask that 

you respond to those in writing. The record will remain open for 

two weeks for additional comments and written questions from the 

Members. 

The witnesses are excused and this hearing is adjourned. 

[Whereupon, at 11:35 a.m., the Subcommittees were adjourned.] 

(75) 

Appendix I 

ANSWERS TO POST-HEARING QUESTIONS
76 

ANSWERS TO POST-HEARING QUESTIONS

Responses by Dr. Michael Rappa 

QUESTIONS FOR THE RECORD 

THE HONORABLE DEREK KILMER (D-WA) 

U.S. Honse Committee on Science, Space, and Technology 

Next Generation Computing and Big Data Analytics 

Wednesday, April 24, 2013 

There have been a number of big data reports generated recently by a number of industry 

leaders. I'm proud to say that companies, EMC and lsilon, which is headquartered in 

Washington State, have done a lot o.f great work on big data. EMC recently released their latest 

"Digital Universe" study, conducted by IDe. Amazingly, this study projects that the digital 

universe will reach 40 Zettabytes by 2020. 

One of the issues 1 have been passionate about, both in the state legislature and in my first few 

months in Congress, is STEM education. It seems to be that many of these reports make a 

compelling case that there is a dire need for more data scientists. 

1 have two questions: 

I. How are your organizations specifically addreSSing the needfor more data scientists and 

employees with STEM backgrounds? 

The Institute for Advanced Analytics at North Carolina State University has been at the forefront 

of educating a new generation of data savvy professionals to address precisely this need for data 

scientists. Launched in 2007, our Master of Science in Analytics (MSA) degree program is the 

first of its kind in the nation and has become a template for programs at other universities. The 

MSA blends together statistics, mathematics, and computer science and business topics into an 

interdisciplinary curriculum and is classified as a STEM graduate degree. The Institute works 

closely with employers, professional societies, government agencies, and academic institutions 

to enhance the pipeline of data science professionals. 

As I mentioned in my testimony, the Institute has a proven six-year track record that shows we 

can succeed in producing the kind of talent employers need. We can do it quickly with an 

intensive and highly targeted educational format that yields consistent student outcomes. The 

program attracts a diverse, high quality, domestic student population and yet runs with a 

sustainable, cost-effective, and self-financed tuition business model. We have demonstrated the 

ability to produce 80 graduates annually, and I am confident we could produce tenfold with the 

necessary upfront investment in facilities and personnel. 

2. In your testimony, you both discuss how our nation is facing a data scientist shortage. 

What policies would you recommend Congress consider to address that shortage? 

The Institute has shown this is a problem we can solve through close collaboration with the 

private sector. The task now is to scale quickly to meet demand. I believe markets will 

eventually adjust, but we could accelerate the process with a better flow of data. 
77 

Recommendation 1: Provide guidance and incentives to degree granting institutions to 

make public standardized reports of student employment outcomes, such as job placement 

and salary data, by degree majors. 

Accurate and up-to-date information is key to achieving market equilibrium. Prospective 

students who are contemplating the time-consuming and financially burdensome decision 

to invest in their education must have the data needed to calculate the expected return on 

investment. 

There is nothing particularly novel about this recommendation. Already many of the 

nation's top business schools make public each year comprehensive employment reports 

for their MBA programs (as does UW's Foster School of Business). This data is absolutely 

essential to prospective students in making their decision to pursue the MBA degree 

(typically two years of study, and an average tuition cost of $80,000 at the best schools). 

Before making a decision about whether or where to attend, individuals can calculate an 

expected ROJ based on recent placement rates and salaries. 

Similar employment reports should be produced for STEM graduate programs. Once we 

have timely and accurate employment data, the market place will adjust quickly to the 

opportunity in data science. We have seen exactly this kind of reaction in the marketplace 

for our Master of Science in Analytics degree. The Institute has published detailed 

employment reports since the inception of the program. Our applicants clearly make 

decisions based on data driven ROJ calculations of expected employment outcomes. 

We know from employers the demand for data science talent is there and growing. We also 

know there are large numbers of students coming out of school or already in the workforce 

who are underemployed, if not unemployed. There is simply poor alignment between skill 

training and skill needs. The key to achieving a closer alignment is up-to-date and accurate 

degree outcomes data that would allow students to make sound educational investments. 

STEM employment reports need to become as commonplace in universities as they are 

today with MBA programs. 

Recommendation 2: Provide incentives for college and university students to pursue fields 

of study in data science contingent on institutional performance metrics for student 

outcomes that meet threshold levels. 

Congress may wish to create or target existing incentives for students to pursue STEM 

degrees. I recommend tying incentives to institutional performance metrics such as 

graduation rates and employment outcomes. This will ensure students are making sound 

investment decisions in furthering their education based on expected outcomes. 

The current system of subsidized student loans would be on a more solid footing financially 

if students taking loans knew in advance the likelihood of their ability to repay loans. 

Perhaps such data could be included in the loan application process. Again, it comes down 

to more timely and accurate public data about educational outcomes so the marketplace 

can make better decisions. 
78 

Recommendation 3: Provide guidance to Federal agencies to seek-out and take advantage 

of educational opportunities for existing employees to pursue fields of study in data 

science. Furthermore, encourage the creation of job categories within the Federal 

workforce to employ and promote data scientists. 

Existing employee training programs within government agencies should be encouraged to 

take advantage of data science related educational programs and to use institutional 

performance metrics to determine what programs qualify. Sometimes existing program 

classifications within agencies can be slow to change and fail to include in a timely manner 

new areas of studies like Analytics. 

I have worked with one Federal agency to help define a new job category of "data scientist." 

Congress may wish to encourage other agencies to move in a similar direction. By 

providing leadership, the Federal government will also help the private sector as it, too, 

seeks to define job categories for data science. 

Clearly, the educational community needs to accelerate its efforts to increase the supply of 

talent if there is any hope of meeting the demand over the next few years. Working with 

employers and government, I am confident that universities can and will step-up to the 

challenge. 

Once again, thank you for this opportunity. Please do not hesitate to contact me as needed. 

If I can be of any further assistance to the Committee or to the citizens and employers of 

Washington State, I will be glad to help. 

Sincerely, 

Michael Rappa, Ph.D. 

Executive Director of the Institute for Advanced Analytic5 

and Distinguished University Professor 

North CaroHna State UniverSity 

920 Main Campus Drive, SUite 530 

Raleigh, NC 27606 
79 

Responses by Dr. Farnam Jahanian 

QUESTIONS FOR THE RECORD 

THE HONORABLE CYNTHIA LUMMIS (R-WY) 

U.S. House Committee on Science, Space, and Technology 

Next Generation Computing and Big Data Analytics 

Wednesday, April 24, 2013 

1. The massive volumes of data generated daily across a range of industries and public sector 

organizations necessitate new methods to store and manage the data. The National Science 

Foundation (NSF) Computer and lriformation Sciences and Engineering Directorate (C1SE) 

helps develop and maintain cutting-edge national computing and information infrastructure for 

research and education. This data must be analyzed to extract knowledge and promote 

discovery. Often this data resides in scattered locations. 

For the nation to take advantage of the discovery that can be derived from big data, please 

explain how an effective infrastructure can be constructed to connect the entities developing and 

using Big Data to drive discovery. Additionally, please describe how the infrastructure. 

connections, and broadband would be developed to enable the entire community of research 

universities, in particular those like the University of Wyomingfrom EPSCoR states. 

The Division of Advanced Cyberinfrstructure (ACI) in NSF/CISE supports three major programs 

that emphasize the development of computational infrastructure and participation in Big Data 

activities: The first program is Data Infrastructure Building Blocks (DIBBS); the second is 

Campus Cyberinfrastructure - Network Infrastructure and Engineering (CC-NIE); and the third is 

Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21). All three 

programs support research and discovery efforts in data as well as helping campuses to obtain 

the infrastructure connections and facilities required to participate in Big Data. They are 

discussed below. 

The DIBBS Program focuses on how to develop, implement, and support the new methods, 

management structures and technologies to store and manage the diversity, size, and complexity 

of current and future data sets and data streams. DIBBS has three types of awards: 

Conceptualization awards support design specifications for creating a sustainable data 

infrastructure that will be discoverable, searchable, accessible, and usable to the entire research 

and education community; 

Implementation awards support development and implementation of technologies and 

infrastructure that addresses elements of the data preservation and access; and 

Interoperability awards develop frameworks that provide consistency or commonality of 

design across communities and implementation for data acquisition, management, preservation, 

sharing, and dissemination. 

The CC-NIE Program invests in improving and re-engineering networks at the campus level to 

support a range of data transfers supporting computational science and computer networks and 

systems research. CC-NIE has two major types of awards: 
80 

Data Driven Networking and Infrastructure for the Campus and Researcher; and 

Network Integration and Applied Innovation awards. 

The CIF2l effort has participation from every NSF Directorate. CIF21 focuses on foundational 

research, infrastructure support and deployment, and eommunity building. Since CIF21 

supports the entire cyberinfrastructure eco-system, it also supports projects involving data, 

computational science and building research communities. 

NSF EPSCoR supports programs that focus on connectivity and cyberinfrastructure for Big Data. 

These are discussed below, specifically focusing on EPSCoR activities in Wyoming. 

Connectivity: Wyoming is a founding member of the Front Range Gigapop (FRGP) in Denver, 

which provides 10Gbitlsec connectivity between the University of Wyoming and institutions in 

Colorado, including NCAR, as well as connectivity to the Abilene Network and National 

LambdaRail. A significant amount of the nation's long-haul telecommunications fiber transits 

through Wyoming's southern quarter along the mainline of the Union Pacific railroad and 

Interstate 80. Major telecommunications centers as well as the National Center for Atmospheric 

Research (NCAR) - Wyoming Supercomputing Center are located in Cheyenne. Fiber 

connectivity along with the availability of electrical power and favorable climate for data center 

operation is making southeastern Wyoming an important IT hub. 

Managing Big Data: Wyoming has an NSF EPSCoR award that pilots an effective 

cyberinfrastructure that connects EPSCoR entities developing and using Big Data to drive 

discovery. The RII Track-2, CI-Water, allows a consortium of Utah and Wyoming researchers 

to acquire and develop hardware and software cyberinfrastructure to support the development 

and use of large-scale, high-resolution computational water resources models to enable 

comprehensive examination of integrated system behavior through physically-based, data-driven 

simulation. Successful integration requires data, software, hardware, simulation models, tools to 

visualize and disseminate results, and outreach to engage stakeholders and impart science into 

policy, management, and decisions. The computational requirements of stochastic methods to 

consider uncertainties, fine spatial and temporal resolutions to improve accuracy, and 

representation of dynamic processes that include feedbacks among system components demand 

use of state-of-the-art high-performance computing (HPC). CI-WATER is working to develop a 

robust and distributed CI consisting of integrated data services, modeling and visualization tools, 

and a comprehensive education and outreach program that will revolutionize how computer 

models are used to support water resources research in the Intermountain West and beyond. 

2. Within NSF, the Computer and Information Sciences and Engineering Directorate (CISE) 

helps develop and maintain cutting-edge national computing and information infrastructure for 

research and education. NSF has significant investment in computing infrastructure, including 

the NCAR-Wyoming Supercomputing Center, among others. These high peljormance computers 

are capable of processing complex data sets at a greater rate, enabling scientific research and 

discoveries. 
81 

The ability to analyze and utilize information from increasing quantities of data sets is crucial to 

advancing knowledge. Please describe the contributions these facilities are expected to make to 

the development and use of Big Data over the next three to five years. 

ACI supports national efforts in advanced and cutting edge computational facilities including the 

recently announced facilities in Texas (Stampede) and Illinois (Blue Waters). While both of 

these facilities support very high performance and complex data problems, the Blue Waters 

facility has the largest data storage and management system in the world. When these facilities 

are in full production, they will permit investigators across the country to engage in innovative 

research demanding petascale capabilities. 

ACI also supports the XSEDE project, which manages and operates 17 different high 

performance systems across the nation with a common interface to ensure that researchers can 

get what they need without having to contact each site. XSEDE also manages the allocation 

process that provides researchers with the resources they need. Usage of these facilities is done 

via peer review so that the best research is supported. 

The NCAR Wyoming Supercomputing Center (NWSC) provides high-performance CI that will 

enable researchers to perform high-resolution simulations of weather phenomena, global and 

regional climate, coastal oceans, sunspots, subsurface flow, and more. Earth System research and 

education will be transfonned by the NWSC, as the next generation of Earth science researchers 

and computational scientists will be attracted by the importance of the problem and the scale of 

the facilities available to them. Current and planned education, outreach, and training programs 

built around the facility will help to broaden the impact of the NWSC project on both regional 

and national scales. Integration of the NWSC with other NSF high-performance CI will provide 

important linkages with other resource providers and will directly support NSF's vision of a 

transformative national petascale cyberinfrastructure for science and engineering. Finally, the 

NWSC has the potential to contribute to economic development in the State of Wyoming in the 

form of well-paying jobs, workforce training opportunities, and in the transformation of the state 

into a destination of choice for other high-technology enterprises. Through the facility 

partnership with Wyoming, these benefits can be extended to other EPSCoR states as well. 

NCAR aims to improve researchers' abilities to analyze and utilize information via various 

efforts focused on data manipulation and visualization (e.g., Globally Accessible Data 

Environment, GLADE, http://www2.cisLucar.eduiresources/glade; data analysis and 

visualization, http://www2.cisl.ucar.edulresources/software/dav). 
82 

QUESTIONS FOR THE RECORD 

THE HONORABLE DEREK KILMER (D-WA) 

U.S. House Committee on Science, Space, and Technology 

Next Generation Computing and Big Data Analytics 

Wednesday, April 24, 2013 

There have been a number of big data reports generated recently by a number of industry 

leaders. I'm proud to say that companies, EMC and lsi/on, which is headquartered in 

Washington State, have done a lot of great work on big data. EMC recently released their latest 

"Digital Universe" study, conducted by IDe. Amazingly, this study projects that the digital 

universe will reach 40 Zettabytes by 2020. 

One of the issues 1 have been passionate about, both in the state legislature and in my firstfew 

months in Congress, is STEM education. It seems to be that many of these reports make a 

compelling case that there is a dire need for more data scientists. 

1 have two questions: 

1. How are your organizations specifically addressing the need for more data scientists and 

employees with STEM backgrounds? 

NSF has focused for many years on developing the STEM workforce through investments in its 

research and education programs and projects. Increasingly, the development of skills in the 

use of large data sets is a critical part of the training needed for the STEM workforce. 

Collectively, STEM programs support, for example, curriculum development, strategies to 

increase student retention and success in STEM, and student support through scholarships and 

fellowships. As part of the merit review of these projects, they have to show evidence that the 

measures taken will ensure effective learning. 

Many of these programs focus on undergraduate and graduate students in formal and informal 

education settings. In addition, across NSF - in all the science directorates - research projects 

support graduate students as research assistants. Increasingly, these assistantships require 

data-intensive research, often involving large-scale data sets. These hands-on learning 

opportunities are critical in helping to develop a workforce with sophisticated and real 

experience in deploying these skills. 

2. In the FY14 Budget Request, NSF proposes STEM-C Partnerships (Le., STEM with an 

emphasiS on computing) as one of its primary approaches to advance K-12 teacher and 

student development of computational skills. NSF also supports research that develops 

and evolves the knowledge base that informs improvements in the preparation of the 

workforce. (See http://www.nsf.gov/about/budget/fy2014/pdf/25_fy2014.pdf.) 
83 

3. In your testimony, you both discuss how our nation is facing a data scientist shortage. 

What policies would you recommend Congress consider to address that shortage? 

Congress should continue to support STEM education at all levels from kindergarten through 

lifelong learning. In particular, NSF is looking to invest in evidence-based and evidencegenerating approaches to achieve specific educational outcomes. While anecdotal evidence 

may point to a variety of policy options, NSF, working in partnership with private and public 

sector stakeholders, is laying the foundation for policies and programs that are rooted in 

empirical evidence. In particular, retraining efforts, and initiatives that are aligned with the 

changing needs of business and industry,may be promising areas for strategic investment. 

(85) 

Appendix II 

ADDITIONAL MATERIAL FOR THE RECORD
86 

IDC IVIEW, The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and 

Biggest Growth in the Far East, submitted by Representative Derek Kilmer 
87 

• Between 2012 and 2020, emerging markets' share of the expanding digital universe will grow 

from 36% to 62%. 

• A majority of the infonnation in the digital universe, 68% In 2012, is created and consumed by 

consumers - watching digital TV, interacting with social media, sending camera phone images 

and videos between devices and around the Intemet, and so on. Yet enterprises have ttability or 

responsibility for nearly 80% of the information in the digital universe, They deal with issues of 

copyright, privacy, and compliance with regulations even when the data zipping through their 

neworks and server farms is created and consumed by consumers. 

• Only a tiny fraction of the digital universe has been explored for analytic value. IDC estimates that 

by 2020, as much as 33% of the digital universe will contain infOlTl1ation that might be valuable If 

analyzed. 

• By 2020, nearly 40% of the information in the digital unIverse will be ~touched" by cloud 

computIng providers - meantng that a byte will be stored or processed in a cloud somewhere in 

its journey from originator to disposal. 

• The proportion of data tn the digital universe that requires protection is growing faster than the 

digital universe 1tself, from less than a third in 2010 to more than 40% in 2020. 

• The amount of information individuals create themselves - writing documents, taking pictures, 

downloading music. etc. - is far less than the amount of information being created about them in 

the digital universe. 

• Much of the digltal universe is transient - phone calls that are not recorded, digital TV images 

that are watched (or "consumedn

) that are not saved, packets temporarily stored in routers, digital 

sUivalllanc9 images purged from memory when new images come in, and so on. Unused storage 

bits installed throughout the digital universe will grow by a factor of 8 between 2012 and 2020 but 

will still be less than a quarter of the total digital universe in 2020, 

C2012IDC 
88 

Figure 1 

The Digital Universe: 50-fold Growth from the Beginning of 

2010 to the End of 2020 

2Q,000 

\0,000 

2009 2.010 2011 2Q12 2013 2015 2016 2017 2018 2019 2020 

Source: IDC's Digital Universe Study, sponsored by EMC, Dscember 2012 

Within these broad outlines of the digital universe are some singularities worth noting. 

First, while the portion of the digital universe holding potential analytic value is growing, only a tiny 

fraction of territory has been explored. lOG estimates that by 2020, as much as 33% of the digital 

universe will contain information that might be valuable if analyzed, compared with 25% today. This 

untapped va!ue could be found In pattems In social media usage, correlations in scientific data from 

discrete studies, medical information intersected with soclologlcal data, faces in security footage, and 

so on. However, even with a generous estimate, the amount of Information in the digital universe that 

is ~taggedH accounts for only about 3% of the digital universe in 2012, and that which is analyz.ed is 

half a percent of the digital universe. Herein is the promise of "Big Data" technology - the extraction 

of value from the large untapped pools of data in tho digital universe. 
89 

Fl 9 u re .z 

The Impact of Consumers (2012) 

"Enterprlse has some liability or responSibliity 

Source: IDC's Digital Universe Study, sponsored by EMC, December 2012 

• ~nternr'lse "Touch." (2,125 EB)~ 

COnsumer Generated (t,9~4 EBl 

~t Overl~p (1,342. EB) 

Moreover, JDC believes that much of the digital universe is unprotected. Our estimate is that about a 

third of the data in the digital universe requires some type of protection -to protect privacy, adhere 

to regulations, or prevent digital snooping or theft However, currently, only abou120% of the digital 

universe actually has these protections. The level of protection varies by region, with much less 

protection in emerging markets. 

Therefore, Irke our own physical universe, the digital universe is rapidly expanding and incredibly 

diverse, with vast regions that are unexplored and some that are, ~rankly, scary. 

~Z012 IDe 
90 

Figure 3 

The Digital Universe Paradox: 

Falling Costs and Rising Investment 

$8.00 

$6.00 

-Cost per GB 

"OO~-------

$200 ------- ,-----------

'" -" "'" ,.., - ~~* -0 ~::::4._...,.,~~""'" 

2010 2011 2012 2013 20"14 2015 2016 2017 2018 2019 2020 

Source: IDC's DIgital UnIverse Study, sponsored by EMC, December 2012 

However, the digital universe astronauts among us -~. the CIOs. dnta scientists, digital entrepreneurs 

- already know the value that can be found in this ever-expanding collection of digital bits. Hence, 

there is excitemont about ~lg Data technologies, automatic tagging algorithms, real-time analytics, 

social medi~ data mining, and myriad new storaQe technologies. 

The Geography of the Digital Universe 

Although the bits of the dlgital universe may travel at Internet speeds around the globe, it Is possible 

to assign a place of origin to them and chart the map of the digital universe. 

In this year's study, for the first time, we have managed to determine where the information in the 

digital universe was either generated, first captured, or consumed, This geography of the digital 

universe maps to the users of the devices or applications that pump bits into the digital universe or 

pull bits into one's own persona! digital solar system for the purpose of consuming information -

Internet users, digital TV watchers, structures hosting surveillance cameras, sensors on plant floors, 

and so Oil, 
91 

! Figure 4 

The Geography of the Digital Universe (2012) 

Tota:: 2,837 EB 

Source' lDC's DIgital Un"verse Study, sp;msorod by EMC, Dec€tfllber2012 

United, Slates 

" WQstern r;~roPe 

China 

411 India 

• Rest ofthe World 

In the early days, the digital universe was a developed world phenomenon, with 48% of the digital 

universe in 2005 springing forth from just the United States and Western Europe. Emerging markets 

accounted for less than 20%. However, the share of the digital universo attributable to emerging 

markets is up to 36% in 2012 and wi!! be 62% by 2020. By then, China alone will generate 21 % of the 

bit stream entering the digital universe. 

©20121DC 
92 

Figure 5 

The Rise of Emerging Markets 

25,000 

15,000 - .. -----.~ - .. -. -.-.. -.. - .. -.------.. - ........... -....... ,. 

, .. ,. E!Tl~iging Markets (E~l 

- Mature MaltaIS (Es)' 

10,000 .--.. --~~ .. --.-.. --..•. -

5,000 .-~.--.--.- .-._ .. 

2010 2fJll 2012 2[113 2014 2015 2016 2017 2018 2019 io2ri 

,* United States, Western Europa, Japan, Austra!la, flew leal amI 

Source: mc'.;; Digital Uni'Jerse study. sponsored byEMC, December2012 

It stands to reason. Even though China accounts for only 11% ofglobaJ GOP today, by 2070 it will 

account for 40% of the PCs, nearly 30% of smartphones, and nearly 30"/0 of Internet users on the 

planet- not to mention 20% of the worfd population. 

At the same time, the money invested by the regions in creating, managIng, and storing their portIons 

of the digital universe ~\'iII vary wildly - in real dollar terms and as a cost per gigabyte. 

This disparity in investment per gigabyte represents to some extent dlffering economic conditionssuch as the cost of laber - and to some extent a difference in the types of information created. 

replicated, or consumed. The cost per gigabyte from bits generated by surveillance cameras will be 

different from the cost per gigabyte from bits generated by camera phones. 

However, to another extent, this disparity also represents differences in the sophistication of the 

underlying IT, content, and information industries - and may. represent a challenge for emerging 

markets when it comes to managing, securing, and analyzing their respective portions of the digital 

universe. 
93 

Figure 6 

Investment in Managing the Digital Universe by Region (2012) 

$2.50 

$2.00 

£iJ $1.50. '" 

~ 

1!i 

8- $1.00 

$.5Q 

WestemEurope 

Source: IDC's Dig!tal Universe Study, sponsored by EMC, Decemb&r 2012 

This might not be a major issue if the geography of the digital universe were- as stable and fixed as, say, 

the geography of countries. Howevcr, bits created in one part of the physical world can easltyflnd 

themselves elsewhere, and if they come with malware attached or leaky privacy protections, it's a 

problem. The digital universe is like a digital commons, with aU countries sharing some responsibility for it. 

The installed base of unused storage bits introduces an interesting geographic twist that establishes 

a new dynamic by which to understand our digital universe. While emerging markets may indeed 

grow as a percentage of the digital universe, remember that much of the digital universe Is a result of 

massive consumption on mobile and personal devices, digital televisions, and cloud-connected 

applications cn pes. As ownership of smartphones and tablets (that have relatively low internal 

storage and rely heavily on consuming information from lithe cloud") increases exponentially within 

emerging markets, information consumption grows at an even faster pace, Given the connected 

infrastructure of our digital universe, information does not need to (and in fact will not) reside within 

the region where the information is consumed. Hence, today's welt-running datacenters will continue 

to expand and to fulfill an increasing number of requests - both local and from halfway across the 

globe - for information. 

©2012!DC 
94 

Big Data in 2020 

Last year, Big Data became a big topic across r,early every area of IT. me defines Big Data 

technologies as a llilli::: generation of technorogies and architectures, designed to economlcai!V extract 

value from very fargo volumes of a wide ~ of data by enabling hlgh-velocitv capture, discovery, 

and/or analysis. There are three maln characterIstics of Big Data: the data itself, the ana!ytics of the 

data, and the presentation of the results of the analytics. Then there are the products and services 

that can be wrapped around one or all of these Big Data elements. 

The digital universe itself, of course, comprises data - aU kinds of data. However, the vast majority 

of new data being generated is unstructured. This means that more often than not, we know little 

about the data, unless it is somehow characterized or tagged - a practice that results in metadata. 

Metadata is one of the fastest-growing subsegments of the digital universe (though metadata itself Is 

Ll. small part of the digital universe overall). We believe that by 2020, a third of lt1e data in the digital 

unlverse (more than 13,000 exabytes) 'Will have Big Data value, but only if it is tagged and analyzed 

(see "Opportunity for Big Data). 

Opportunity tor Big Data 

40 

30 

, Digita! UnNerse 

• Useful If Tagged and Analyze!l 

10 

2015 • 2020 

Source: IDC's Digital UniversCI Study, sponsored by EMC, Decembcr2012 

Not all data is necessarily useful for Big Data analytics. However, somp- data types ~re particularly 

ripe for analysis, such as: 

• Surveillance footage. Typically, generic metadata (date, time, location, etc.) is automatically 

attached to a video file. However, as !P cameras continue to proliferate, then~ is greater 
95 

opportunity to embed more intelligence into the camera {on the edge) so that footage can be 

captured, analyzed, and tagged in real time. This type of tagging can expedite crime 

investigations, enhance retail analyties for consumer traffic patiems, and, of course, improve 

military intelligence as videos from drones across multiple geographies are compared for pattern 

correlations, crowd emergence and response, or measuring the effectiveness of 

counterinsurgency. 

• Embedded and medical devices. In the future, sensors of an types (including those that may be 

implanted into the body) will capture vital and non vital biometric"" track medicine effectiveness, 

correlate bodily activity with health, monitor potential outbreaks of viruses, etc. - an in real time, 

• Entertainment and social media. Trends based on crowds or massive groups of individuals can 

be a great source of Big Data to help bring to market the "next big thing," help pick winners and 

losers in the stock market, and yes, even predict the outcome of elections - all based on 

information users freely publish through social outlets. 

• Consumer images. We say a lot about ourselves when we post pictures of ourselves or our 

families or friends. A picture used to be worth a Ihousand words, but the advent of Big Data has 

introduced a significant multiplier. The key will be the introduction of sophisticated tagging 

algorithms that can analyze images either in real time when pictures are taken or uploaded or en 

masse after they are aggregated from various Web sites. 

These are in addition, of course, to the normal transactional data running through enterprise 

computers 1n the course af norma! data processing today. nCandidates for Big Data" illustrates the 

opportunity for Big Data analytics in just these areas alone. 

Candidates for Big Data 

2020 

Surveillance 

II Embedded and. Medical 

III Data Proces~ing 

Entertainment and SocIa! MedIa 

m. Consumer images and Vo'ce 

10% 20% 30% 40% 50% 60% 70% BO% 90% iOO% 

(Share of Data That is Useful If Tagged and Analyzed) 

S::Jtlrce: IDC's Digit2J Universe Study, sponsored by EMC, Deccmber 20 12 

©201210C 
96 

All in all, in 2012, we believe 23% of the information in the digital universe (or 643 exabytes) would be 

useful for Big Data jf it were tagged and analyzed. However, technology is far from where it needs to 

be, and in practice, we think only 3% of the potentially useful data is tagged, and even less is 

analyzed. 

Call this the Big Data gap - information that is untapped, ready for enterprising digital explorers to 

extract the hidden value in the data. The bad news: This wi!! take hard work and significant 

investment. The good news: As the digital universe expands, so does the amount of useful data 

within it. 

The Untapped Big Data Gap (2012) 

Analyzed" 0.5% 

Source: IOC's Dig[l:al Universe Study, sponsored by E~C, Dec.!ffiber2012 

Information Security in 2020 

The rise in mobility and participation in social neThvorks, the increasing willingness to share more and 

more data, new technology that caplures more data about daia, and the growing business around Big 

Data all have at least one assured outcome - the need for information security. 

However, the news from the digital universe is as follows: 

• The proportion of data in tho digital universe that reqUires protection Is growing faster1han the 

d.igitai universe itself, from less than a third In 2010 to more than 40% in 2020. 

• Only about half the informat:on that needs protection has protection. That may improve slightly by 

2020, as some of the better-secured information categories will grow faster than the digital 

univef~e itself, but it still means that the amount of unprotected data will grow by a factor of 26. 

• Emerging markets have even less protection than mature markets. 

In our annual studies, we have defined, for the sake of analysis, five levels of security that can be 

associated with data having some level of sensitivity: 

1. Privacy only- an email address on a YouTube upload 

2. Compliance driven - smails that might be discoverable In litigation or subject to retention rules 

3. Custodial- account information, a breach of which could lead to or aid in identity theft 

11 
97 

4. Confidential- information the originator wants to protect, such ag trade secrets, customer lists, 

confidential memos, etc, 

5. Lockdown - information requiring the highest security, sllch as financial transactions, personnel 

files, medical records, military intelligence, etc. 

The tables and charts illustrate the scope of the security challenge but not the solution. While 

information security technology keeps getting better, so do the skills and tools of those trying to 

circumvent these protections. Just follow the news on groups such as Anonymous and the 

discussions of cyberwarfare. 

However, for enterprises and, for that matter, consumers, the issues may be more sociological or 

organizational than technological ~ data that is not backed up, two-phase security that is ignored, 

and corporate policies that are overlooked. Technological solutions win improve, but they will be 

ineffec.tilfe if consumer and corporate behavior doesn't change. 

[-F~;e 10 -----c-------------, 

The Need for Information Security 

2010 

2010 

Privacy 

DI Compliance 

• Custodial 

2020 ~I:l •• ~~~~ 10% 20% 30% 40% 

(% of the Digital Universe by Security Category) 

Source: IDC's D~g:taj Universe Stlld)', sponsored hy Er.t1C, Decerrlber 2012 

12 @20121DC 
98 

Unprotected Data (2012) 

Estimated % of Data Needing Protection That is Not Protected 

Protected . 

• Needing security ma.t is not protected 

Rest eftheWerld ;iifiiiliil 

~,":.-,~"~,,< ."," '"'>:"~~" ~"'." ,', ','"" ~ .,. c ... ".,,,,.*.,,n_,-:,' ,1\,.%_,., ~.:"., -.L:., ~r} 

WerldyJI;, ~Tnfl~"11. 'iii iiE4JII 500 I 

Data in Exabytes (E8) 

Source: IDC's Digital Universe Study, sp()nsored by E\1C, December 20'1:2 

Big Data is of particular concern when it comes to information security. The lack of standards among 

ecommerce sites, the openness of customers, the sophi::>tication of phishers, and the tenacily of 

hackers place considerable private information at risk, For example, what one retailer may keep 

private about your purchase, such as your transaction and customer profile data, another company 

may not and instead may have other data hidden. Yet intersecting these data sets with other 

seemingly disparate data sets may open up wide security holes and make public what should be 

private information. 

There is a huge need for standardizatlon among retail and financial Web sites as well as any other 

type of Web sIte that may save, collect, and gather private information so that individuals' private 

information is kept that way. 

Cloud Computing in 2020 

Between 2012 and 2020, the patch of the digital universe that CIOs and their IT staffs need to 

manage will become not just bigger but also more complex. The skills, experience, and resources to 

manage all these bits of data win become scarcer and more specialized, requiring a new, flexible, and 

scalable [T infrastructure that extends beyond the enterprise: cloud computing. 

To thfs end, the number of servers (virtual and physical) worldwide wi!! grow by a factor of 10 and the 

amount of informat1on managed directly by enterprise datacenters will grow by a factor of 14. 

Meanwhile, the number of !T professionals in the world 'Nil! grow by less than a factor of 1.5. 

13 
99 

In addition, \Nhile spending on p:.Jblic and private cloud computing accounts for less than 5% of tcta! 

IT spending today, lOG e$t!ma~es that by 2020, nearly 40% of the infonnation in the digital universe 

will be "touched" by cloud computing - meaning that a byte will be stored or processed in a ctoud 

somewhere In Its journey from originator to disposal. Pertlaps as much as 15% will be maintained in a 

cloud. 

The Digital Universe and the Cloud (2020) 

"' NOt ''Touched" by 1)\e C~~d (25,030 EBI 

• Stored In 1)\e Cloud (5,208 EBI 

•. Addlt',nal "Touched" by the ciouu (9,iti8 EBI' 

&ll .:rce: iDG's Digital Universe Stlldy, sponsored by EMC, December 2012 

Of course, cloud se/Vices come in various flavors - public, private, and hybrid. For organizations to 

offer their own cloud services, they have to do more than just run virtual servers. They must also 

allow for virtualized storage and netv/orking, self~provisjonlng, and self·service and provide 

information security and billing. 

Part of tho real genesis of ihis conversion to the cloud will be a migration to converged 

infrastructures, where servers, storage, and networks are integrated together, sold, and installed as a 

unit of IT infrastructure. Few enterprises are at this point yet, so the impact of private clouds in the 

digital universe today is small. 

@2012IDC 
100 

Type of Information in the Cloud in 2020 

C(lfT)pute~, Ph:mes, Consumer Eie.c1ronics 

$l EfI:t)rtaimnuni 

f# Embedd£ld am.! Mec:k-al 

.SurveH!ance 

Source: IDC's D!gltal Ui"iiverse Study, SPO>1sored by EMC, December 2012 

However, by 2020, it seems likely that private clouds and public clouds wi!! be commonplace, 

exchanging data seamlessly. There won't be one cloud; rather, there will be many clouds, bounded 

by geography, techno:ogy, different standards, industry, and perhaps even vendor. We may still cal! it 

cloud computing, but it wi!! be an interconnected ether, easy to traverse bt;t difficult to protect or 

manage. 

Call to Action 

Our digital universe in 2020 will be b:gger than ever, more valuable than ever, and mor~ volat11e than 

By 2020, we'll also be storing a smaller and smaller percentage of our expanding digital universe; yet 

cur digital shadows will be larger than life and on the move given the increase in mobility, and they 

wit[ require more protection than ever before. IT managers wi!! be responsible not only for ensuring 

that proper secL:rity surrourds our dlgitallives but also for managing the storing, analyzing, and 

delivery of zettabytes of content ,. no easy t8sk. 

Requests for data CQuld come frem a faraway jungle, across a mashup of connected devices and 

network points, to a device that has an obtuse screen. The delivery of the requested data must 

happen :n an acceptable amount of time, guaranteein~J that it is consumed flawlessly; if not, then a 

business may lose a customer. Consider this: 

15 
101 

Æ 

• The network is growing in importance. Latencies must get shorter, not longer. Data must be 

analyzed, security applied, and authentication verified - all in real time and in levels yet to be 

seen. Network infrastructure must be a key investment to prepare for our 2020 digital universe. 

• Big Data is going to be a big boon forthe IT 1ndustry. Web sites that gather significant data need 

to find ways to monetize this asset. Data scientists must be absolutely sure that the intersection 

of disparate data sets yields repeatable resulls if new businesses are going to emerge and thrive. 

Further, companies that deliver the most creative and meaningful ways to display the results of 

Big Data analytics will be coveted and sought after. 

• The laws and regulations governing information security must harmonize around the globe, 

though differences (or absences) will certainly exist. IT managers must realize that data will be 

requested outside geographic boundaries, and a global knowledge of information security may be 

the difference between approval and denial of a data request. 

• IT managers must find ways to drive more efficiency in their infrastructures so that IT 

administrators can focus on more value~add Initiatives suer as "bring your own device" (BYOD) 

policies, Big Data analytics, customer onboarding efficiency, security, etc. One way this is likely 10 

happen is through converged infrastructures, which integra:e storage, servers, and networks. 

Are you ready to create, consume, and manage 40 trillion gigabytes of data? 

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