"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right. Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide. The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary. This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.

1. © 2015 IHS. ALL RIGHTS RESERVED.
KNOWLEDGE ARCHITECTURE: IT’S
IMPORTANCE TO AN ORGANIZATION
Combining Strategy, Data Science and
Information Architecture to Transform Data to
Knowledge
David Meza
Chief Knowledge Architect
NASA Johnson Space Center
Connected Data London
July 12, 2016

2. AGENDA
• Why Connected Data
• Knowledge Architecture
• Opportunities
• Questions?
2

5. “The most important contribution
management needs to make in the
21st Century is to increase the
productivity of knowledge work and the
knowledge worker.”
PETER F. DRUCKER, 1999

6. NASA Challenges
• Hundreds of millions of documents, reports, project data, lessons learned,
scientific research, medical analysis, geo spatial data, IT logs, etc., are
stored nation wide
• The data is growing in terms of variety, velocity, volume, value and veracity
• Accessibility to Engineering data sources
• Visibility is limited

7. To convert data to knowledge a convergence of Knowledge
Management, Information Architecture and Data Science is
necessary.
7
Knowledge Management
Data Science
Information Architecture

8. Knowledge Architecture
• The people, processes, and technology of designing, implementing, and
applying the intellectual infrastructure of organizations.
• What is an intellectual infrastructure?
• The set of activities to create, capture, organize, analyze, visualize,
present, and utilize the information part of the information age..
• Information + Contexts = Knowledge
• Information Architecture + Knowledge Management + Data Science =
Knowledge Architecture
• KM without applications is empty (Strategy Only)
• Applications without KA are blind (IT based KM)
• Data Science transforms your data to knowledge
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9. “We have an opportunity for everyone in the world to have access to all the world’s
information. This has never before been possible. Why is ubiquitous information so
profound? It is a tremendous equalizer. Information is power.”
ERIC SCHMIDT (FORMER CEO OF GOOGLE)

11. Areas of Opportunity
11
• Search
• Storage
• Data Driven Visualization

12. 30%
of total R&D spend is
wasted duplicating
research and work
previously done.
Source: National Board of Patents
and Registration (PRH), WIPO, IFA
54%
of decisions are made
with incomplete,
inconsistent and
inadequate information
Source: InfoCentric Research
Opportunity 1: Search in the Enterprise
46%
Workers can’t find the
information they need
almost half the time.
Source: IDC

13. Google It!
13
Courtesy of SocMedSean.com

14. Page Rank By The Numbers
Google 5 Billion queries per day
Enterprise 1000 queries per day
What We
Are Looking
For

15. NASA SEARCH EVALUATION
15
• There is No One Solution
• Master Data Management Plan is essential
• Identify Critical Data
• Develop Standards for Government and Contractor created data
• Analytics is essential
• Meta Data

16. TOP USER REQUIREMENTS
16
• Semantic search
• Cognitive Computing – Clustering, topic modeling
• Faceting
• Repository specific searches
• Ability to save searches
• Alerts

17. There was a sad engineer…

19. Repository Specific
Clustering
Save, Alerts
Facet Filter

22. Opportunity 2: Storage and Access

23. Document to Graph
23

24. PATTERNS EMERGE
24

25. There was a inquisitive engineer…

26. LESSON LEARNED DATABASE
26
2031 lessons submitted across NASA. Filter by date and Center only.
Useful information stored in database.

27. TOPIC MODELING
27
Topic models are based upon the idea that documents are mixtures of topics, where a
topic is a probability distribution over words.
LDA Model from Blei (2011)
David Blei homepage - http://www.cs.columbia.edu/~blei/topicmodeling.html
Blei, David M. 2011. “Introduction to Probabilistic Topic Models.” Communications of the ACM.

28. GRAPH MODEL OF LESSON LEARNED
DATABASE
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29. GRAPH MODEL OF LESSON LEARNED DATABASE
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30. GRAPH MODEL OF LESSON LEARNED DATABASE
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31. OPPORTUNITY 3: DATA DRIVEN VISUALIZATION
31

37. 37
WHAT COULD YOU ACCOMPLISH IF YOU COULD:
• Empower faster and more informed decision-making
• Leverage lessons of the past to minimize waste,
rework, re-invention and redundancy
• Reduce the learning curve for new employees
• Enhance and extend existing content and document
management systems

38. Contact Information
David Meza – david.meza-1@nasa.gov
Twitter - @davidmeza1
Linkedin - https://www.linkedin.com/pub/david-meza/16/543/50b
Github – davidmeza1
Blog
davidmeza1.github.io
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39. Contents
© 2015 IHS. ALL RIGHTS RESERVED. 39
Report Name / Month 2015
QUESTIONS?