The rise of Data Scientist
Dealing with Big Data in Agile Data Driven
Organisations and New Organisational Roles
Yuri Demchenko, EDISON Project
University of Amsterdam
Big Data Innovation Conference BDIC2016

16-17 November 2016, Frankfurt, Germany
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Outline•

Background and motivation
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–
–
–

•

Technologies timeline and data driven technologies
New type of Agile Data Driven Organisations (ADDO)
Demand for Data Science and data related professions
European initiatives related to Digital Single Market and Digital Skills Agenda

EDISON Data Science Framework (EDSF)– From Data Science Competences to Body of Knowledge and Model Curriculum•••

Data Science Competence Framework: Essential competences and skills
New organisational role and Data Science Professional profiles
Educational and training aspects in Data Science and organisational roles– Example Research Data Management Literacy curriculum– Professional Certification and Self-made Data Scientists•

Wide spectrum of activities and initiatives worldwide to establish Data (Science)
professions family

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Technologies Timeline•

Service Oriented Architecture (SOA): First proposed in 1996 and revived with the Web
Services advent in 2001-2002–•

Computer Grids: Initially proposed in 1998 and finally shaped in 2003 with the Open Grid
Services Architecture (OGSA) by Open Grid Forum (OGF)–•

Defined new features, capabilities, operational/usage models and actually provided a guidance
for the new technology development

Big Data and Data Intensive Science: Shaped in 2014 - Growing adoption and maturity
–
–
–

•

Migrated to cloud platform, serves as collaborative environment for research

Cloud Computing: Initially defined in 2008, matured 2011 – Now in productive phase–•

Currently standard for industry, and widely used

Defined as Ecosystem where data are the main driving component
Critical need for new digital skills and professions
Recognised need for the Data Science definition as new scientific discipline and and Data Scientist
as a new profession

Cognitive Technologies and AI: Emerged 2016 - Gaining recognition and finding use–

Leverages and consolidation of recent foundational technologies and raised role of large scalecomputing

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Visionaries and Drivers:
Seminal works, High level reports, Activities
The Fourth Paradigm: Data-Intensive Scientific Discovery.
By Jim Gray, Microsoft, 2009. Edited by Tony Hey, Kristin Tolle, et al.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
Riding the wave: How Europe can gainfrom the rising tide of scientific data.
Final report of the High Level Expert Group on
Scientific Data. October 2010.
http://cordis.europa.eu/fp7/ict/einfrastructure/docs/hlg-sdi-report.pdf

https://www.rd-alliance.org/

The Data Harvest:
How sharing researchdata can yieldknowledge, jobs andgrowth.

NIST Big Data Working Group (NBD-WG)
http://bigdatawg.nist.gov/

An RDA Europe Report.
December 2014

ISO/IEC JTC1 Big Data Study Group (SGBD)
http://jtc1bigdatasg.nist.gov/home.php

https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

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The Fourth Paradigm of Scientific Research
1. Theory and logical reasoning
2. Observation or Experiment––E.g. Newton observed apples falling to design his theory ofmechanics
But Gallileo Galilei made experiments with falling objects from thePisa leaning tower

3. Simulation of theory or model–

Digital simulation can prove theory or model

4. Data-driven Scientific Discovery (aka Data Science)– More data beats hypnotized theory

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Agile Data Driven Organisation/Enterprise (ADDO/ADDE)
Four pillars of the Agile Data Driven Organisation (ADDO)• Enterprise technological agility/mobility• Event driven analytics, cognitive technologies• Using new source of data to correlate with the company’s business (location, cultural, social
media, sentiments, profiling and targeting)• Cloud as a general computing and data management infrastructure
Steps/conditions to become ADDO
• Curious thinking to explore new data use, including use of new data sources• Management must be receptive to the role of Big Data analytics and possible
recommendations as outcome of new discovered factors and relations• Key/driving role of the Data Scientist–••

Employees must understand that Data Driven approach will help them doing their work
Availability of data analytics tools to support and facilitate data self-service by employees–••

New emerging profession capable of using scientific research methods

Tools and applications must provide overview of the process and properties of the data collected

Comprehensive and customizable data visualisation
Incorporating mobile platform for data collection and data delivery

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What’s new in “data-driven” ADDE••

Data Driven methods and model have been used long time ago – What is
new now?
IBM WoW 2016 Conference (23-24 Oct 2016 Las Vegas) discussions– Rise of cognitive technologies and new understanding of “data driven”•

All analytics reports at/by IBM are required to be correlated with “modern
sources of data”– Social Network/Media (human&incentives): Twitter, facebook, Linkedin,
Pinterest, Slack, YouTube, Google Plus+, Tumblr, Instagram, Flikr, etc– Additionally (event&trends): news, weather, biodiversity, fashion trends, etc.

•

Cloud based cognitive platforms to provide global services for data
sources alignment

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Data Driven Victories and Failures - Politics
Very high impact events and facts• US Election 2012 – Obama’s campaign and rise of Big Data analytics– Micro-targeting and Social Networks analysis•

Brexit 2016
– “Data driven Brexit” – first serious ring for right use of Big Data technologies•

US Election 2016
– Clinton’s campaign – “Data driven” but using only upper layer of SN web– Trump’s campaign – Targeting bottom SN web and “forgotten people not to
be forgotten”– Matt Oczkowski, leader on Trump’s campaign: “If he was going to win this election, it
was going to be because of a Brexit style mentality and a different demographic trend
than other people were seeing.”

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Data-Driven Brexit: A Wakeup Call for Analysts
By Barry Devlin, June 28, 2016
Book: In Data We Trust:
How Customer Data is
Revolutionising Our
Economy (Aug 2012)• A strategy for
tomorrow's data world

Data-Driven Brexit: A
Wakeup Call for Analysts
By Barry Devlin, June 28,
2016

•
•

• There are significant lessons for believers in data-driven business tolearn from how data was and wasn’t used for decision making before,
during, and after the Brexit vote.
• Human attitude -- including emotion, intuition, and social empathy -and motivation are at the heart of decision making and the action that
follows• Information will only be accepted when it conforms to preconceived
notions. Expertise is not sufficient and, in extremis, will be dismissedwith ridicule.
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Article “In Data we trust” by T.Edsall in The
New York Times
Multimillion-dollar contract for data
management and collection services
awarded May 1, 2013 to Liberty Work tobuild advanced list of voters

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Demand for Data Science and data related
professions•

McKinsey Global Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp– Estimated gap of 140,000 - 190,000 data analytics
skills by 2018•

UK Big Data skills report 2014
– 6400 UK organisations with 100+ staff will have implemented
Big Data Analytics by 2020
– Increase of Big Data jobs from 21,400 (2013) to 56,000 (2017)•

IDC Report on European Data Market (2015)– Number of data workers 6.1 mln (2014) - increase 5.7% from 2013
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply 509,000 (2014) or 7.5%•

HLEG report on European Open Science Cloud (October 2016) identified
need for data experts and data stewards– Recommendation: Allocate 5% grant funding for Data management and preservation– Estimation: More than 80,000 data stewards (1 per every 20 scientists)– Core data experts need to be trained and their career perspective improved

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Recent European Commission Initiatives 2016
Digitising European Industry: Reaping the full benefits of a Digital Single Market.
COM(2016) 180 final, Brussels, 19.4.2016•

The need for new multidisciplinary and digital skills in particular Data Scientist–

Expected rapidly growing demand will lead to more than 800 000 unfilled vacancies by 2020

European Cloud Initiative - Building a competitive data and knowledge economy inEurope, COM(2016) 178 final, Brussels, 19.4.2016•

European Open Science Cloud (EOSC) and European digital research and data
infrastructure–•

To offer 1.7 million European researchers and 70 million professionals in science and technology
open and seamless services for storage, management, analysis and re-use of research data

Address growing demand and shortage of data-related skills

A New Skills Agenda for Europe, COM(2016) 381 final Brussels, 10.6.2016••

Addresses the need for digital and complementary skills, ensure young talents flow into
data driven research and industry
Launch Digital Skills and Jobs Coalition (1st December 2016, Brussels) to develop
comprehensive national digital skills strategies by mid-2017

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OECD on Digital Economy• Demand for new type of “dynamic self-re-skilling workforce”• Continuous learning and professional development tobecome a shared responsibility of workers and organisations[ref] SKILLS FOR A DIGITAL WORLD, OECD, 25-May-2016
http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/ICCP/IIS(2015)10/FINAL&docLanguage=En

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EDISON Data Science Framework (EDSF)
Release 1 (October 2016)
DS-BoK

MC-DS

EDISON Online
Educational Environt

CF-DS
Taxonomy andVocabulary

Data Science
Framework

Edu&Train Marketpltz
and Directory

DS Prof Profiles

Foundation & Concepts•

Services

Roadmap &Sustainability• Community
Portal (CP)• Professional
certification• Data Science
career & prof
development

Biz Model

EDISON Framework components
•
•
•
•
•
•

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP – Data Science Professional profiles
Data Science Taxonomies and Scientific Disciplines Classification
EOEE - EDISON Online Education Environment

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Data Scientist definition
Based on the definitions by NIST Big Data WG (NIST SP1500 - 2015)•

A Data Scientist is a practitioner who has sufficientknowledge in the overlapping regimes of expertise inbusiness needs, domain knowledge, analytical skills,
and programming and systems engineering expertise to
manage the end-to-end scientific method process
through each stage in the big data lifecycle– … Till the delivery of expected scientific and business value
to science or industry•

Other definitions to admit such features as
–
–
–

•
•

[ref] Legacy: NIST BDWG
definition of Data Science

Ability to solve variety of business problems
Optimize performance and suggest new services for the organisation
Develop a special mindset and be statistically minded, understandraw data and “appreciate data as a first class product”

Data science is the empirical synthesis of actionable knowledge and technologies required tohandle data from raw data through the complete data lifecycle process.
Big Data is the technology to build system and infrastructures to process large volume ofstructurally complex data in a time effective way

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Identified Data Science Competence Groups•

Commonly accepted Data Science competences/skills groups include
–
–
–

•

Data Analytics or Business Analytics or Machine Learning
Engineering or Programming
Subject/Scientific Domain Knowledge

EDISON identified 2 additional competence groups demandedby organisations– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

Other skills commonly recognized aka “soft skills” or “social or professional intelligence”–•

All groups need to be represented in Data Science curriculum and training programmes–•Inter-personal skills and team work, cooperativeness

Challenging task for Data Science education and training

Another aspect of integrating Data Scientist into organisation structure–––

General Data Science (and Data) literacy for all involved roles and management
Common agreed and understandable way of communication and information/data presentationRole of Data Scientist: Provide a kind of literacy advice and guidance to organisation

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Data Science Competence Groups - Research
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design

16

Data Science Competences Groups – Business
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Process
Operations/Stages
•
•
•
•
•
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DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design17

Identified Data Science Competence Groups
Data Science Analytics Data Management(DSDA)(DSDM)

Data Science Engineering Research/Scientific Methods(DSENG)(DSRM)

0

Use appropriate
statistical techniques
and predictive
analytics on available
data to deliverinsights and discover
new relations

Develop andimplement data
management
strategy for datacollection, storage,
preservation, andavailability forfurther processing.

Use engineering
principles to research,
design, develop andimplement new
instruments andapplications for datacollection, analysis andmanagement1

DSDA01
Use predictive
analytics to analyse big
data and discover new
relationsDSBPM01
Understand business and provide
insight, translate unstructured
business problems into an abstract
mathematical framework

DSDA02
Use statistical techniq
to deliver insights

DSENG01
Use engineering principles
to design, prototype data
analytics applications, ordevelop instruments,
systems
DSENG02
Develop and apply
computational solutions

DSRM01Create new understandings andcapabilities by using scientific/
research methods or similar domain
related development methods2

DSDM01Develop andimplement data
strategy, in particular,
Data Management
Plan (DMP)DSDM02Develop data models
including metadata

DSRM02
Direct systematic study toward afuller knowledge or understandingof the observable facts

DSBPM02
Participate strategically andtactically in financial decisions

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

DSENG03
Develops specialized tools

DSRM03
Undertakes creative work

DSBPM03
Provides support services to other

4

DSDA04
Analyze complex data

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

5

DSDA05Use different analytics

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

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Data Science Domain
Knowledge, e.g. Business
Processes (DSDK/DSBPM)
Create new understandings andUse domain knowledge
capabilities by using the scientific (scientific or business) tomethod (hypothesis,
develop relevant data analytics
test/artefact, evaluation) orapplications, and adopt generalsimilar engineering methods toData Science methods todiscover new approaches todomain specific data types andcreate new knowledge andpresentations, data and processachieve research ormodels, organisational roles
organisational goals
and relations

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Identified Data Science Skills/Experience Groups•

Group 1: Skills/experience related to competences
–
–
–
–
–
–

•

Group 2: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools

Group 3: Programming and programming languages and IDE–•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data
management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
Application/subject domain related (research or business)
Mathematics and Statistics

General and specialized development platforms for data analysis and statistics

Group 4: Soft skills or Social Intelligence–

Personal, inter-personal communication, team work, professional network

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Practical Application of the CF-DS• Basis for the definition of the Data Science Body of Knowledge(DS-BoK) and Data Science Model Curriculum (MC-DS)– CF-DS => Learning Outcomes (MC-DS) => Knowledge Areas (DS-BoK)– CF-DS => Data Science taxonomy of scientific subjects and vocabulary• Data Science professional profiles definition– Extend existing EU standards and occupations taxonomies: e-CFv3.0,
ESCO, others• Professional competence benchmarking– For customizable training and career development– Including CV or organisational profiles matching• Professional certification– In combination with DS-BoK professional competences benchmarking• Vacancy construction tool for job advertisement (for HR)– Using controlled vocabulary and Data Science Taxonomy

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Example competence benchmark result•••

Red polygon indicates thechosen profession
Yellow polygon indicatesthe practitioner
Insufficient competences
are shown in a bold
typeface.

[ref] Kim Hee et al (Frankfurt Univ)“Tailored Data Science Education using
Gamification” - DTW2016 Workshop
research paper

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Data Science Professions FamilyManagers: Chief Data Officer (CDO), Data Science (group/dept) manager,
Data Science infrastructure manager, Research Infrastructure manager
Professionals: Data Scientist, Data Science Researcher, Data Science
Architect, Data Science (applications) programmer/engineer, Data
Analyst, Business Analyst, etc.
Professional (database): Large scale (cloud) database designers andadministrators, scientific database designers and administrators
Professional (data handling/management): Data Stewards, Digital Data
Curator, Digital Librarians, Data Archivists
Technicians and associate professionals: Big Data facilities operators,
scientific database/infrastructure operators
Support workers and data handling clerks: User support workers, data
entry clerks, data entry field workers
Icons used: Credit to [ref] https://www.datacamp.com/community/tutorials/data-science-industry-infographic

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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO
•
•
•
•
•
•

• Providing cross-organizational
Group Manager
services
Data Science Architect
Data Analyst
Data Science Application programmer
Data Infrastructure/facilities administrator/operator: storage, cloud,computation
Data stewards

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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO
•
•
•
•
•

• Providing cross-organizational(Managing) Data Science Architect (1)services
Data Scientist (1), Data Analyst (1)Data Science Application programmer (2)Data Infrastructure/facilities administrator/operator: storage, cloud,computing (1)Data stewards, curators, archivists (3-5)

Estimated: Group of 10-12 data specialists for research institution of 200300 research staff.

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ADDE and Big Data Organisational Roles24[ref] Cortnie Abercrombie, What CEOs want from CDOs and how to deliveron it [online] http://www.slideshare.net/IBMBDA/what-ceos-want-from-cdosand-how-to-deliver-on-it

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EXAMPLE: IBM emerging professions[ref] Mastering the art of data science: How to craft cohesive teams that create business results, IBM Institute for Business
Value, 2016•
Create data science teams with varied backgrounds and skills
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Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Data Science Model Curriculum (MC-DS)• Set Learning Units mapped to CF-DS Learning and DS-BoK Knowledge
Areas/Units– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training for self-made Data Scientists practitioners– Education and training marketplace: Courses catalog and repository
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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas(1) Data Governance(2) Data Architecture(3) Data Modelling and Design(4) Data Storage and Operations(5) Data Security(6) Data Integration andInteroperability(7) Documents and Content(8) Reference and Master Data
(9) Data Warehousing and BusinessIntelligence(10) Metadata(11) Data Quality

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Other Knowledge Areas motivated by
RDA, European Open Data initiatives,
European Open Data Cloud(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data, Open
Access, ORCID(15) Responsible data use•

Highlighted in red: Considered (Research)
Data Management literacy (minimum
required knowledge)

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Research Data Management Model Curriculum –Part of the EDISON Data Literacy Training
A. Use cases for data management and stewardship•
Preserving the Scientific Record
B. Data Management elements (organisational and individual)•
Goals and motivation for managing your data•
Data formats•
Creating documentation and metadata, metadata for discovery•
Using data portals and metadata registries•
Tracking Data Usage•
Handling sensitive data•
Backing up your data•
Data Management Plan (DMP) - to be a part of hands on session

Collaboration with the Research
Data Alliance (RDA) on developing
model curriculum on Research
Data Literacy:• Modular, Customisable,
Localised, Open Access• Supported by the network of
trainers via resource swap
board

C. Responsible Data Use Section (Citation, Copyright, Data Restrictions)
D. Open Science and Open Data (Definition, Standards, Open Data use and reuse, open government data)•
Research data and open access•
Repository and self- archiving services•
ORCID identifier for data•
Stakeholders and roles: engineer, librarian, researcher•
Open Data services: ORCID.org, Altmetric Doughnut, Zenodo
E. Hands on:a) Data Management Plan designb) Metadata and toolsc) Selection of licenses for open data and contents (e.g. Creative Common and Open Database)
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Further Developments and Actions•

Run surveys and key interviews for target communities– Primarily experts, visionaries and top executives at universities and companies••

Work with champion universities to practically validate the proposed EDSF
Run a serial of the Champion Universities conferences: Spring 2017 and Summer
2017– Extend scope to Champions, Adopters, Ambassadors and Professionals

•
•
•
•

Involve academic and industry experts and professional organisations to thedefinition of DS-BoK following from CF-DS
Formally provide suggestions to ESCO for the definition of the Data Science
professional profiles (occupations) family
Formally provide suggestions for e-CF3.0 extensions for Data Science to
CEN/PC 428
Suggest required ACM CCS(2012) Classification extensions and proposal for
Data Science curriculum definition

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Discussion• Questions• Comments•

Invitation to contribution and cooperation:– Forum, EDISON Liaisons Groups, Champions Conference (Spring & Summer 2017)•

EDISON project website http://edison-project.eu/•

EDISON Data Science Framework Release 1 (EDSF)
http://edison-project.eu/edison-data-science-framework-edsf
–
–
–
–

•

Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles-definition-dsp

Survey Data Science Competences: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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Definitions (according to e-CFv3.0)•

Competence is a demonstrated ability to apply knowledge, skills andattitudes for achieving observable results.
– Competence vs Competency (e-CF vs ACM)
• Competence is ability acquired by training or education (linked to learning outcome)• Competency is similar to skills or experience (acquired feature of a person)•

Competence is not to be confused with process or technology concepts
such as, ‘Cloud Computing’ or ‘Big Data’. These descriptions represent
evolving technologies and in the context of the e-CF, they may be
integrated as elements within knowledge and skill examples.

•

Knowledge in the context of competence definition is treated as
something to know, to be aware of, familiar with, and obtained as a partof education.

•

Skills is treated as provable ability to do something and relies on the
person’s experience.

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Data Scientist and Subject Domain Specialist•

Subject domain components
–
–
–
–
–

•

Model (and data types)
Methods
Processes
Domain specific data and presentation/visualization methods
Organisational roles and relations

Data Scientist is an assistant to Subject Domain Specialists– Translate subject domain Model, Methods, Processes into abstract data driven form
– Implement computational models in software, build required infrastructure and tools– Do (computational) analytic work and present it in a form understandable to subject
domain– Discover new relations originated from data analysis and advice subject domain
specialist– Present/visualise information in domain related actionable way– Interact and cooperate with different organizational roles to obtain data and deliver
results and/or actionable data

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Data Science and Subject Domains
Data Science domain components
Data structures &databases/storage
Visualisation• Abstract data driven
math&compute models• Data Analytics
methods• Data and Applications
Lifecycle Management

Crossorganisational
assistive role

Data Scientist functions is to translate between two domains

Domain specific components
Domain specificdata & presentation(visualization)• Models (and data types)• Methods• Processes

Organisationalroles

Data Scientist role is to maintain the Data Value Chain (domain specific):•

Data Integration => Organisation/Process/Business Optimisation => Innovation

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