# Presentation: 668311

## Innovation, Standards, and NOAA Data ManagementTed HabermannNOAA National Data Centers

*Vice Adm. Lautenbacher *

- Innovation, Standards, and NOAA Data ManagementTed HabermannNOAA National Data Centers

- Mature Organizations

**Notes:**
Vice Admiral Lautenbacher was talking in a radio interview about management challenges in NOAA. In this quote he is beginning to describe how scientists in NOAA must learn management and leadership skills as they move through their careers. His point is that scientists have succeeded by developing scientific skills and that those skills are necessary, but not sufficient, when it comes to managing or leading others.

The interesting thing about this concept is that it applies to organizations as well as to individuals. Mature Organizations are those that have been around for some time. These organizations generally have a set of capabilities (resources, processes, and values) that have served them well over some time period. Those same capabilities, however, may not be appropriate or successful in the present world, which has changed considerably and continues to change. Organizations need to learn new skills just like the individuals in those organizations. 

## The Technology S-Curve

- We all know that new technologies emerge slowly, grow quickly (if they catch on) and then fade away. This common knowledge has been described as the technology S-curve.
**Why does it exist?**

**TIME**

**Notes:**
All of us have witnessed the rise and fall of so many technologies over the last several years that we have no problem understanding the S-Curve idea. This simple model has been used to help understand many technology and business situations. Why does it exist?

## The Adoption Curve

- Geoffrey Moore has attributed the S-curve to the technology adoption life cycle where techies and visionaries are early adopters, pragmatists make up the bulk of users, and luddites fill out the tail of the distribution.

**Visionaries**

**Luddites**

**Pragmatists**

**Notes:**
The adoption curve could result from “normal” processes at work in the population. Geoffrey Moore discusses this idea in his book “Crossing the Chasm”. The concept is simple. A few people adopt new technologies early while the large part of the population waits to see how it develops. When the new technology is proven, they may come on board.

## The Chasm

- Moore has also described the “chasm” in the adoption life cycle. He proposes that many new technologies do not make it across the chasm between visionaries and pragmatists. They fall into the chasm. The technology S-curve with the chasm might look like:

**TIME**

**Notes:**
The simple model is complicated by the fact that many new technologies do not make it across the chasm between the visionaries and the pragmatists. The early adopters enjoy the technologies, but they never become practical or accepted by a large segment of the population. They fall into the chasm.

## Technology Cycle

**TIME**

- Technological
- Disruption

- Era of Ferment

- Selection

- Dominant
- Design

- Disruption #2
- (destroys existing
- competence)

**Notes:**
This brings up another possible explanation for the S-curve. The cycle starts with the introduction of a new, disruptive technology. This disruption leads to a period of experimentation and ferment during which many different approaches to applying the new technology are explored. Overall progress is slow because of this experimentation. At some point selection of a dominant design occurs and energy previously spent on exploring different design options is focused on using that dominant design effectively. Then a second disruption occurs, destroying the competence developed earlier and focusing energy once again into experimentation. 

## Types of Innovation - 1

*Sustaining / Incremental Innovation*: generally small innovations in products and processes aimed at existing customers.
*Disruptive / Discontinuous Innovation*: significant innovations generally aimed at unknown or non-existent customers.

**Notes:**
The organizational literature differentiates between a number of types of innovation. Some of this differentiation is useful. Sustaining innovation is aimed at existing customers and involves a series of small improvements along dimensions that are known to be important to those customers. Disruptive innovation, not to be confused with disruptive technologies, is not aimed at existing customers. It innovates along a dimension that is not important to existing customers.

These definitions were developed by Clayton Christensen using the disc drive manufacturing business as a clear example. In that case sustaining innovations increased the speed, capacity, or density of existing drives. Disruptive innovation involved decreasing the size of the entire disc drive, initially at the expense of speed and capacity. The customers for large drives made large computers. Size was not important to them as they wanted faster drives with more capacity. Making the drives smaller was a disruptive innovation. 

## Unidata Objectives (1998):

- “These objectives either *respond* to users' current needs or *advance* Unidata toward meeting future needs effectively. Most of the "responsive" items are continuations of current Unidata objectives, and their importance is well established. *But only by looking beyond present needs to anticipate future ones, and by pursuing the most promising technical advances, can Unidata remain effective. This is true even though some of these advances involve uncertainties, and the demand for them may not be apparent as yet**”.* *Unidata, 2003 Proposal.*

- Sustaining Innovation

- Disruptive Innovation

*Clayton Christensen, The Innovator’s Dilemma*

**Notes:**
One of the best expressions of the difference between sustaining and disruptive innovation came from a Unidata proposal written during 1998. The proposal listed a number of objectives and divided those objectives into two categories: those that served the needs of current customers (sustaining) and those that served potential future customers that may not exist (low demand). At this time Unidata was considering transitioning all of their active development into Java. That was the disruptive innovation. Unidata clearly understood that this was risky and was trying to justify the risk to the funding agency.

## Unidata (netCDF) Evolution

*Disruptive Innovation:* Always includes a decrease in metrics for current customers so it is difficult for mature organizations.

- In the Unidata case we are now seeing the disruptive switch to Java play out. The capabilities of the Java version of the netCDF libraries have now surpassed the original C version.

**Notes:**
Like the decrease in drive capacity with decreasing size, disruptive innovation always involves a decrease in metrics developed for current customers. This is what makes it difficult for mature organizations to choose the disruptive track. In the Unidata  case, there was no existing code written in Java, so mature capabilities had to be re-developed in the new language. This resulted in a serious hit to the development effort as programmers learned a new language and re-developed the code. Now, five years later, we have reached the point where the capabilities in the Java version of the Unidata tools are clearly surpassing the capabilities of the original C version. The payoff associated with the increased risk is now becoming apparent.

## Types of Innovation - 2

*Component Innovation:* Making existing components better.
*Architectural Innovation*: putting existing components together in new ways.

**Notes:**
Systems are made up, to first order, of components that are held together by architecture. Both of these can be the target of innovations. Component innovation makes the components better, architectural innovation connects existing components in new ways. When a program promises to build on existing expertise by pulling together best of class components in a new way, it is promising architectural innovation. Many NOAA programs make this promise. The cross-cutting goals may be a good example of this practice.

## Architecture = Organization

- Structure in mature organizations tends to evolve to match product architectures.  Architectural Innovation, therefore, many times includes elements of organizational change.

**Notes:**
As organizations mature their structure converges with the architecture of their products as a result of process innovation. The organizational processes are optimized to produce a particular product and the structure evolves to support those processes. This means that architectural innovation many time requires organizational change. This makes it very difficult, like the evolution to cross-cutting programs in NOAA.

## Innovation & Technology Cycle

**TIME**

- Disruptive Innovation

- Product Innovation
- Design Competition
- Community-driven
- technology change

- Component,
- Architectural,
- Sustaining and
- Process
- Innovation

- What do we make?

- How do we make
- it (better)?

**Notes:**
How do these innovation types relate to the technology cycle? Many disruptive innovations are related to disruptive technologies (this is not, however a required characteristic). The period of ferment features development of new products and designs (product innovation and design competition) and changes to the new technology. During this time the question is: what do we make? Once selection occurs, components that use the new technology are created and improved and they are put together in new ways. The technology is evolved along dimensions that serve existing customers and processes are evolved toward increased efficiency. The question at this point is: How do we make it better?

## Other Differences

**TIME**

- Research
- Prototypes
- Custom developments
- Network building
- Uncertainty

- Operational Systems
- Product Families
- Predictability
- Partnerships

- (non-compliance
- cost increases
- with time)

- Network Effects
- Value = f(N2)

- Standards

**Notes:**
	This slide shows other differences between these two periods. I want to focus a little on the difference with respect to networks (these are networks of users, not wires). The period of ferment is a time of network building, or attempts at network building. This is when you try to get your friends to use the products you develop. We do this because we believe that network size is a critical element in the selection process. The product that can quickly build the largest network wins the battle for selection (sometimes). Once selection is made, prototypes transition to operational systems, product families are developed on top of good tools, things become predictable and partnerships flourish.
	During this time standards and the associated network effects kick in for the selected technology. The network effect occurs when the value of a product depends strongly on the number of those products being used. Fax machines are the classic example. The first fax machine sold was much less valuable than the last one sold because it could not be used to communicate with others. Standards are critical to enabling the network effects.
	It is critical to understand that, once the network starts, the cost of not being in the network goes up quickly as a function of time. Individuality becomes more expensive fast. 

## How Standards Change The Game

-  Expanded Network Externalities (Network effect turns on)
-  Reduced Uncertainty and Risk in Technology Decisions
-  Reduced Consumer Lock-In to Particular Components
-  Competition in the Market vs. Competition for the Market
-  Competition on Value vs. Features
-  Competition to Offer Proprietary Extensions
-  Component vs. Systems Competition

- Standards shift the locus of competition from systems development to component development. Specialists tend to thrive in the mix-and-match environment created by interface standards. Generalists and system (stovepipe) developers tend to thrive in the absence of standards.

- In the absence of standards:
- 1) there is no architectural innovation (no mix-and-match) and
- 2) the organization can not benefit from component innovation.
- Once a standard has been agreed on (selection), the organization benefits from component innovation and architectural innovation.

**Notes:**
	Standards effect many aspects of the technology development process. First and foremost, they allow network effects to happen. Individual components must be able to plug in order to take advantage of a network. Standards decrease the risk and uncertainty of technology development efforts significantly by increasing the predictability of the results. Standards increase choice for users of technology. They are able to build systems that address their needs using collections of standard components. They can also switch components easily as their needs evolve.
 	This freedom shifts the locus of competition towards components. This occurs because many of the challenges of architectural innovation are related to making things work together. These are lessened by the standards. The competition focuses on making the components that are reliable, perform well, and fit into the standards framework.
	Standards also effect the overall organization that is involved in the technology development project. Without standards, it is difficult for the organization to benefit from any innovation. Architectural innovation is impossible and component innovation can’t contribute to the overall technology evolution.

## Andy Grove: Communication Overcomes Computing

- The framework is changing now. The Internet is redefining software. The Internet is redefining the role of computing and communication and their interaction with each other. I still don’t understand the framework. I don’t think any of us really do. But some aspects of it are pretty clear. It’s proven not to be computing based but communications based. In it computing is going to be subordinated to the communications task.

*“**Decisions Don’t Wait”, Harvard Management Update*.

**Notes:**
Andy Grove, the ex-chairman of Intel, proposes that the future of technology will be more about communications than computing. The evolution of computing from number crunching to word processing to the web clearly supports this hypothesis.

## Kevin Kelley: The Web and Sharing

- The revolution launched by Netscape’s IPO was only marginally about hypertext and human knowledge. At its heart was a new kind of participation that has since developed into an emerging culture based on sharing.

*“**We Are The Web”, Wired, August 2005*

## Infrastructural Technologies

- IT is, first of all, a transport mechanism – it carries digital information just as railroads carry goods and power grids carry electricity. And like any infrastructural technology, it is far more valuable when shared than when used in isolation. The history of IT in business has been a history of increased interconnectivity and interoperability, from mainframe time-sharing to minicomputer-based local area networks to broader Ethernet networks and on to the Internet. Each stage in that progression has involved greater standardization of the technology and, at least recently, greater homogenization of its functionality. For most business applications today, the benefits of customization would be overwhelmed by the costs of isolation. *Nicholas G. Carr, IT Doesn’t Matter, Harvard Business Review, May, 2003*

**Notes:**
	Nicholas Carr made the same observation in an article titled “IT Doesn’t Matter”. He argued that IT has become an infrastructure technology and compared the history of IT development to other similar technologies, railroads and electric power grids. These technologies share that characteristic that they are more valuable when shared. They are technologies that benefit from the network effects. Carr also points out that the amount of standardization has increased and that the cost of isolation overcomes the benefits of customization during that evolution.

## Why No Standards?

- The longer the market takes to determine a standard, the more expensive it will be for firms operating within that market. The more expensive this competition becomes, the greater the tendency for firms to cooperate at the beginning.

- The difficulty with this reasoning is that it is difficult for individual firms to determine how expensive or how long it will take the market to determine the dominant standard. Nor are companies willing to cede control of such an important aspect of their market early in a competition. *Booz Allen Hamilton, 2005*.

- The science community generally does not value sharing.

**Notes:**
Booz Allen Hamilton just published a study of the return on investment of geospatial interoperability. Two of their conclusions:
Of the projects considered for this study, the project that adopted and implemented geospatial interoperability standards had a risk-adjusted ROI of 119.0%. This ROI is a “Savings to Investment” ratio. This can be interpreted as for every $1.00 spent on investment, $1.19 is saved on Operations and Maintenance costs.
Overall, the project that adopted and implemented geospatial interoperability standards saved 26.2% compared to the project that relied upon a proprietary standard. One way to interpret this result is that for every $4.00 spent on projects based on proprietary platforms, the same value could be achieved with $3.00 if the project were based on open standards.

## Organizational Approaches / Skills

**TIME**

- Leadership
- Discovery-based
- Planning
- Resolving Critical
- Unknowns

- Management
- Detailed Plans w/
- Accountability

- Long-term Plans
- & Requirements
- PPBES

**Notes:**
The model I am describing divides the technology development cycle into two time periods that require different sorts of organizational approaches and skills. The Era of Ferment is characterized by high uncertainty and rapid potentially very significant changes. There are many critical unknowns that make long-term planning difficult and unreliable. Many authors have proposed that rapid change increases the requirement for leadership skills.

Once Selection has occurred, uncertainty decreases and planning becomes more reliable and meaningful. During this phase management skills become important.

NOAA is presently exerting significant effort on long-term requirements development and planning efforts (PPBES). In the model I am describing, this activity is appropriate after dominant designs have been selected. In many areas of NOAA’s work, this has not occurred, and maybe it will not (or should not) ever occur. This causes significant tension within the organization.

“Though accountability to plans is an effective practice in mature businesses, it can be crippling in new high-potential businesses.”
“Detailed plans (broken down by region, product line, sales channel, and so forth) are useful for mature businesses, but NewCo should focus on resolving critical unknowns, which can be accomplished at a more aggregate level.”  Govindarajan, V. and C. Trimble, 2005

## Multiple Technologies

**TIME**

- NOAA relies on many technologies which are changing rapidly. To do well in long-term, NOAA must:

- 2) develop and use mechanisms for standards adoption and selection

- 3) realize that different phases require different organizational structures /strategies,

- 4) support multiple structures / strategies simultaneously.

- 1) recognize phases of the technology cycle,

**Notes:**
Fitting simple conceptual models to the real-world is always interesting, and this case is no exception. In fact, NOAA is involved in numerous simultaneous technology development projects that are in different phases of the technology cycle. We may be able to increase our chances of success by explicitly recognizing these cycles and identifying where in the cycle certain projects are positioned. We may also benefit from recognizing that the mix of management and leadership may need to change during the life of a project and that, in fact, we need to simultaneously do both types of projects. This is the concept of the ambidextrous organization developed by O’Reilly and Tushman.

## Innovation, Standards & NOAA

- There is a considerable innovation literature that can help NOAA learn the new skills required to innovate strategically and effectively.
- Technology is evolving from a computing tool to a communication tool. It is becoming an infrastructure technology.
- Standards are critical to building value of infrastructure technologies.
- Standards are critical to organizationally effective component and architectural innovation.
- NOAA must develop and use processes for selecting and applying standards.
- The requirements and approaches to planning are very different in the different phases of the technology cycle.
- Understanding and explicitly recognizing the differences in phases of the technology cycle and the differences in balance between management and leadership skills might help NOAA.

**Notes:**
Conclusions of Section 1.

## Examples from NGDC

- NOAA Maps, The NOAA Observing System Database and NOSA Website
- 	Internet mapping in NOAA is a great example of an “Era of Ferment”. Many different tools produce maps that look and behave differently. We are collecting capabilities for over 200 NOAA Internet Maps and comparing those capabilities to a single map that includes information for ~100 NOAA Observing Systems. *How would a selection event change NOAA’s approach to internet mapping?*
- The Data Processing Pipeline
- 	An example of components/architecture for data processing and ingest in an open source Java project initiated at NGDC.

**Notes:**
The Enterprise Data Systems Division at NGDC is working on several projects that may provide insight into the technology cycle and how standards might effect the way we innovate. 

## Technology Confluence

**Geographic Information Systems**

**+ Relational Databases**

**Notes:**
The Enterprise Data Systems Division at NGDC is working at the confluence of three important technologies: Geographic Information Systems, Relational Databases, and the Internet. The confluence of these three technologies is a great example of the evolution from computing to communications and the increasing importance of standards. The isolated GIS system is very close to a pure computing system and the enterprise GIS is a system that emphasizes communication.

## NOAA Maps

- Almost 200 maps have been  captured from NOAA Web pages and described in the NOAA Maps Project.

**Notes:**
The goal of the NOAA Maps project at NGDC is to characterize how NOAA presently does mapping on the Internet and to explore how a selection event might affect that aspect of NOAA Data Management. The project now includes over 200 maps and over 100 capabilities that those maps provide. The NOAA Observing System Architecture (NOSA) map is our prototype for a standards-based internet map. The capabilities of that map will serve as a comparison to other NOAA maps that are developed using more customized tools. 

## http://nosa.noaa.gov/

- Metadata

- WIST

- LAS

- FYP

- Stations,
- QA

- Map

**Notes:**
The foundation of the architecture of the NOSA website is a geospatial database implemented using Oracle Spatial. A variety of access approaches have been implemented on top of that database using commercial and standards-based tools.

## Map With Multiple Data Layers

| Source | # |
| --- | --- |
| Base Layers | 5 |
| NESDIS | 6 |
| NMFS | 4 |
| NOS | 7 |
| NWS | 17 |
| OAR | 52 |
| OSEI | 1 |
| Collaborative Networks | 12 |
| NOAA Ships | 1 |
| Backgrounds | 7 |

- Over 100 Layers

**Notes:**
The fundamental internet mapping capability is a map with multiple layers that can be turned on and off by the user. The map also offers basic mapping capabilities such as zoom, pan and selection capabilities. The NOSA map includes over 100 point, vector, and polygon layers that are all stored in the geospatial database.

## Map With Metadata Links

**Notes:**
The NOSA map provides links to the observing system home pages

## Map With Data Links

**Notes:**
The NOSA map provides links to data pages for particular observatories when they are available.

## Integrated Data Access

- Spatial SQL

**Notes:**
The geospatial database supports many types of spatial objects (points, lines, polygons) and operators, so making spatial comparisons between layers is straightforward. In this case observatories located in a particular satellite footprint are selected.

## Observation Density Grids

- Spatial SQL

**Notes:**
We compute a 10 X 10 summary grid in the database to show the global density of NOAA observatories. These grid cells are spatial objects, so the same spatial SQL can be used to find the observatories in any cell.

## Find Your Place-NOSA in Your Neighborhood

- Geospatial database =

- very general searches

**Notes:**
	Many users know the names of spatial objects that they are using for searches rather than the latitude and longitude, so we provide spatial searches using six different reference layers. These searches are done using the spatial objects themselves rather than bounding boxes for those feature, so being in Idaho or Florida actually means being in those states. The results of these searches also provide links to the observing system home pages and to the observatories themselves.

## What just happened?

- Geospatial Database

- Scientists throughout NOAA contributed

- Spatial
- Query

- COTS
- IMS

- Links back to existing WWW resources

## OGC Web Map Services

**Notes:**
The Open Geospatial Consortium Web Map Service interface provides URLs that lead directly to maps. Many aspects of the maps can be controlled by arguments in the URLs. The NOSA website provides example URLs with various spatial limits and backgrounds.

## Web Image Slide Trays (WIST)

- The WIST provides simplified access to maps of particular layers for pre-determined regions using a Web Map Server (WMS) and a custom tag library for Java Server Pages.

**Notes:**
The Interactive Map Interface has many features and can be difficult to use. Sometime a simple interface that allows comparison of multiple maps can be more effective. The Web Image Slide Tray is an example of such an comparison interface. It is build on top of an Open Geospatial Consortium Web Map Service interface.

## OGC Web Feature Service

**Notes:**
The Open Geospatial Consortium Web Map Service provides images of maps. The Web Feature Service provides actual spatial features and associated data/attributes. This information is provided in Geographic Markup Language (GML). The example above shows the GML representation of values from the database table nosa_ns.nws_boy_41001_tsqp for station_id=41008.

## Maps at NGDC

**Notes:**
The technology developed for the NOSA Map is also being used at NGDC for:
Multibeam Bathymetry Data Access,
The World Glacier Inventory (with NSIDC),
Seafloor Geologic Samples,
National Emissions Inventory (with Aeronomy Lab and EPA),
Tsunami Runups,
Ocean Observations (with OGP and NDBC),
and many other datasets. We are now drawing about 100,000 internet maps / month.

## NOSA Website @ NGDC

**Notes:**
The second example from NGDC is the data processing pipeline for ingesting data into the geospatial database, and other data processing tasks.

## The Data Processing Pipeline

- A pipeline executes a sequence of plug-able data processing tasks. The NGDC data processing pipeline provides a set of pipeline utilities designed around work queues that run in parallel to sequentially process data objects. The pipeline is an open source project hosted in the *Jakarta Commons Sandbox*
- (_[http://jakarta.apache.org/commons/sandbox/index.html](http://jakarta.apache.org/commons/sandbox/index.html)__)._
- Processing steps are specified as a series of stages in an XML configuration file.

**Notes:**
Data processing goes on constantly at NGDC and in other parts of NOAA. It is an element of data management that features many opportunities for re-inventing the wheel simply because we all do so much of it. The Data Processing Pipeline was developed at NGDC to provide an infrastructure for geospatial data processing at NGDC. The pipeline is built around the Open Geospatial Consortium Simple Features Specification which is used as a canonical representation in memory.  Readers are written to read data in particular formats and create spatial objects in memory. Those features can then be operated on and written to the database.

## DMSP Orbit Processing

- Stage 1. Find Matching Files
- Stage 2. Avoid Duplicate Processing
- Stage 3. Read Data / Create Spatial Objects
- Stage 4. Low-Res Thinning
- Stage 5. High-Res Thinning
- Stage 6. Write Spatial Data to Database

**Notes:**
This example shows the steps that we are taking to process Defense Meteorological Satellite Program spatial footprints from orbit files into a spatial database.

## Find Matching Files

- <!-- Get names of files to process from the ingest directory -->
- <stage className="org.apache.commons.pipeline.impl.FileFinderStage“
-    filePattern=".*\.OIS">
-    <stageDriver
-      className="org.apache.commons.pipeline.impl.SingleThreadStageDriver"/>
-    <enqueue>
-       <value>/mnt/stout/raid/lv2/ois/incoming/</value>
-    </enqueue>
- </stage>

**Notes:**
The “Find Matching Files” stage finds files that match the pattern “*.OIS” in a directory called /mnt/stout/raid/lv2/ois/incoming/. As these files are identified, they are added to the processing queue.

## Read Data / Create Spatial Objects

- <!-- Read the limb coordinates from orbit data files and create OGC polygons and associated metadata -->
- <stage className="gov.noaa.nosa.dmsp.OLSFileReader“
-    executable="bin/ols2gmt" path="bin“
-    libraryPath="dmspnl_ols2gmt/libdmspnl">
-    <stageDriver
-       className="org.apache.commons.pipeline.impl.SingleThreadStageDriver“
-       faultTolerant="false"/>
- </stage>

**Notes:**
A custom component is used to read the OLS file format. We have developed general readers for delimited formats and specific readers for complex satellite data formats. These readers store the spatial data they read as spatial objects that are compliant with the Open Geospatial Consortium Simple Features Specification. The data associated with the spatial objects are stored as regular numbers or character strings in the database.

## Write Data to Inventory

- <!-- Write the polygon and associated metadata into the spatial database -->
- <stage className="gov.noaa.eds.sdif.db.SpatialTableWriter" createTable="false“
-    truncateExisting="false" dropExisting="false
-    dataClassName="gov.noaa.nosa.dmsp.OLSRecord" SRID="0">
-    <stageDriver
-       className="org.apache.commons.pipeline.impl.SingleThreadStageDriver“
-       faultTolerant="false"/>
-    <database-proxy
-       className="gov.noaa.gdsg.sql.informix.InformixDatabaseProxy"/>
-    <datasource user="*******" password="*******" type="informix“
-       host="informix1.ngdc.noaa.gov" port="1543" database="gis”
-       server="informix1"/>
-    <tablePath path="dmsp_ols_footprints"/>
-    <!-- Convert all OGC polygons to the database-specific spatial representation -->
-    <defaultConverter
-       className="gov.noaa.gdsg.sql.informix.GeoObjectConverter“
-       convert="com.vividsolutions.jts.geom.Geometry“
-       result="com.informix.geodetic.types.GeoObject"/>
- </stage>

**Notes:**
Once the data

## Building on Experience

*Louis Liebenberg, The Art of Tracking: The Origin of Science (Wired, June 2003)*

**Notes:**
This picture shows South African Bushman using a Palm/GPS to enter data they derive from tracks and other signs in the environment. Louis Liebenberg has proposed that, in the history of mankind, tracking is the origin of science. Liebenberg's work seamlessly connects the earth's oldest form of knowledge to its most modern, sophisticated, and automatic counterpart.

From Wired: In the far reaches of the Kalahari Desert, in southern Africa, researcher Louis Liebenberg is deploying what may be the first illiterate computers integrated into a hunter-gatherer society, a group known as the San Bushmen. The desert natives, now thought to be the first people, are famous for their mysterious capacity to decipher animal tracks, or spoor, in the natural environment. The plethora of specific data that a Bushman can extract from even a partial spoor has astonished scientists for decades: This unusual ability is subtle and multispectral; it's steeped in an experience of nature that recognizes no division of life into distinct categories. 
Liebenberg's handheld device allows a Bushman to enter spoor readings and other observations, hit a button, and register, via satellite, the place and time of the observation. The information is transferred to a central database, where it is correlated to produce a dynamic map of the location, and then used to study ecological relationships, animal behavior patterns, and even poaching activity (a Bushman can tell from a track whether an animal is fleeing a human or natural predator). The info is also used to inform guides about activities of scientific, documentary, or tourist significance, as well as for a wide variety of conservation applications. 
Tracking is one of the longest continuous traditions of systematic knowledge in existence today. But it's vanishing with alarming speed in the wake of such modernizations as the GPS transmitter, the all-terrain vehicle, state-administered animal husbandry, tourism, and so on. Liebenberg's work seamlessly connects the earth's oldest form of knowledge to its most modern, sophisticated, and automatic counterpart. It represents an extraordinary moment in technology transfer. Indeed, Liebenberg has produced something akin to a Stone Age computer by hacking into a bygone world. 

## Leadership Model: Positive Deviance

- Positive deviance says that if you want to create change, you must scale it down to the lowest level of granularity and look for people within the social system who are already manifesting the desired future state. Take only the arrows that are already pointing toward the way you want to go, and ignore the others. Identify and differentiate those people who are headed in the right direction. Give them visibility and resources. Bring them together. Aggregate them. *Barbara Waugh*

**Notes:**
Positive Deviance is a model of organizational change based on the premise that organizations already contain members that are instantiating the desired end state of a change process. These are the secret change agents in the organization. The idea is that these people can lead the change in the broader organization.

## Ted.Habermann@noaa.gov

## References

- Christensen, C., The Innovator’s Dilemma, Harvard Business School Press, 1997, 225p.
- Clark and Wheelwright, Revolutionizing Product Development, The Free Press, New York, 1992, 364p.
- Govindarajan, V. and C. Trimble, Building Breakthrough Businesses Within Established Organizations, Harvard Business Review, May 2005, p. 58-68.
- Lautenbacher, C., Business of Government Radio Interview, http://www.businessofgovernment.org/main/interviews/bios/conrad_lautenbacher_frt.asp, 2005.
- Moore, G., Crossing the Chasm, Marketing and Selling High-Tech  Products to Mainstream Customers, Harper Business, 1991, 211p.
- O’Reilly, C.A. and Tushman, M.L., The Ambidextrous Organizations, Harvard Business Review, April 2004.
- The Positive Deviance Initiative, http://positivedeviance.org/
- Pascale, R.T. and J. Sternin, Your Company’s Secret Change Agents, Harvard Business Review, May 2005, p. 72-81.
- Tushman, M.L., Anderson, P., and O’Reilly, C.A., Technology Cycles, Innovation Streams, and Ambidextrous Organizations: Organizational Renewal Through Innovation Streams and Strategic Change, in Managing Strategic Innovation and Change, Tushman and Anderson, eds., Oxford University Press, New York, 1997, 657p.