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© 2015 IHS. ALL RIGHTS RESERVED.
KNOWLEDGE ARCHITECTURE: IT’S
IMPORTANCE TO AN ORGANIZATION
Combining Strategy, Data Scien...
AGENDA
• Why Connected Data
• Knowledge Architecture
• Opportunities
• Questions?
2
“The most important contribution
management needs to make in the
21st Century is to increase the
productivity of knowledge...
NASA Challenges
• Hundreds of millions of documents, reports, project data, lessons learned,
scientific research, medical ...
To convert data to knowledge a convergence of Knowledge
Management, Information Architecture and Data Science is
necessary...
Knowledge Architecture
• The people, processes, and technology of designing, implementing, and
applying the intellectual i...
“We have an opportunity for everyone in the world to have access to all the world’s
information. This has never before bee...
Areas of Opportunity
11
• Search
• Storage
• Data Driven Visualization
30%of total R&D spend is
wasted duplicating
research and work
previously done.
Source: National Board of Patents
and Regis...
Google It!
13
Courtesy of SocMedSean.com
Page Rank By The Numbers
Google 5 Billion queries per day
Enterprise 1000 queries per day
What We
Are Looking
For
NASA SEARCH EVALUATION
15
• There is No One Solution
• Master Data Management Plan is essential
• Identify Critical Data
•...
TOP USER REQUIREMENTS
16
• Semantic search
• Cognitive Computing – Clustering, topic modeling
• Faceting
• Repository spec...
There was a sad engineer…
Repository Specific
Clustering
Save, Alerts
Facet Filter
Opportunity 2: Storage and Access
Document to Graph
23
PATTERNS EMERGE
24
There was a inquisitive engineer…
LESSON LEARNED DATABASE
26
2031 lessons submitted across NASA. Filter by date and Center only.
Useful information stored i...
TOPIC MODELING
27
Topic models are based upon the idea that documents are mixtures of topics, where a
topic is a probabili...
GRAPH MODEL OF LESSON LEARNED
DATABASE
28
GRAPH MODEL OF LESSON LEARNED DATABASE
29
GRAPH MODEL OF LESSON LEARNED DATABASE
30
OPPORTUNITY 3: DATA DRIVEN VISUALIZATION
31
37
WHAT COULD YOU ACCOMPLISH IF YOU COULD:
• Empower faster and more informed decision-making
• Leverage lessons of the pa...
Contact Information
David Meza – david.meza-1@nasa.gov
Twitter - @davidmeza1
Linkedin - https://www.linkedin.com/pub/david...
Contents
© 2015 IHS. ALL RIGHTS RESERVED. 39
Report Name / Month 2015
QUESTIONS?
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION
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KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION

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David Meza's slides from his talk at Connected Data London. David is a Chief Knowledge Architect at NASA, Johnson Space Centre. His keynote talk proposed how combining strategy, data science and information architecture can help transform data to knowledge.

Published in: Technology

KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION

  1. 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. 2. AGENDA • Why Connected Data • Knowledge Architecture • Opportunities • Questions? 2
  3. 3. “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
  4. 4. 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
  5. 5. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary. 7 Knowledge Management Data Science Information Architecture
  6. 6. 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 8
  7. 7. “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)
  8. 8. Areas of Opportunity 11 • Search • Storage • Data Driven Visualization
  9. 9. 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
  10. 10. Google It! 13 Courtesy of SocMedSean.com
  11. 11. Page Rank By The Numbers Google 5 Billion queries per day Enterprise 1000 queries per day What We Are Looking For
  12. 12. 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
  13. 13. TOP USER REQUIREMENTS 16 • Semantic search • Cognitive Computing – Clustering, topic modeling • Faceting • Repository specific searches • Ability to save searches • Alerts
  14. 14. There was a sad engineer…
  15. 15. Repository Specific Clustering Save, Alerts Facet Filter
  16. 16. Opportunity 2: Storage and Access
  17. 17. Document to Graph 23
  18. 18. PATTERNS EMERGE 24
  19. 19. There was a inquisitive engineer…
  20. 20. LESSON LEARNED DATABASE 26 2031 lessons submitted across NASA. Filter by date and Center only. Useful information stored in database.
  21. 21. 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.htmlBlei, David M. 2011. “Introduction to Probabilistic Topic Models.” Communications of the ACM.
  22. 22. GRAPH MODEL OF LESSON LEARNED DATABASE 28
  23. 23. GRAPH MODEL OF LESSON LEARNED DATABASE 29
  24. 24. GRAPH MODEL OF LESSON LEARNED DATABASE 30
  25. 25. OPPORTUNITY 3: DATA DRIVEN VISUALIZATION 31
  26. 26. 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
  27. 27. 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 38
  28. 28. Contents © 2015 IHS. ALL RIGHTS RESERVED. 39 Report Name / Month 2015 QUESTIONS?

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