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Κnowledge Architecture: Combining Strategy, Data Science and Information Architecture to Transform Data to Knowledge at NASA

Κnowledge Architecture: Combining Strategy, Data Science and Information Architecture to Transform Data to Knowledge at NASA

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"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.

"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.

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Κnowledge Architecture: Combining Strategy, Data Science and Information Architecture to Transform Data to Knowledge at NASA

  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.html Blei, 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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