knowledge architecture: graphing your knowledge
TRANSCRIPT
© 2015 IHS. ALL RIGHTS RESERVED.
KNOWLEDGE ARCHITECTURE: GRAPHING YOUR KNOWLEDGE
Combining Strategy, Data Science and Informatics to Transform Data to Actionable Knowledge
Government Graph DayFebruary 28, 2017
David MezaChief Knowledge Architect NASA Johnson Space Center
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AGENDA• Challenges• Knowledge
Architecture• Graphing LLDB• Questions?
“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
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 informationSource: InfoCentric Research
46%Workers can’t find the information they need almost half the time. Source: IDC
Challenges
To convert data to knowledge a convergence of Knowledge Management, Informatics and Data Science is necessary.
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Knowledge Management
Data ScienceInformatics
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• Knowledge Management + Informatics + Data Science = Knowledge
Architecture• KM without Informatics is empty (Strategy Only)• Informatics without KM is blind (IT based KM)• Data Science transforms your data to knowledge 6
“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)
There was a inquisitive engineer…
LESSON LEARNED DATABASE
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2031 lessons submitted across NASA. Filter by date and Center only. Useful information stored in database.
Document to Graph
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PATTERNS EMERGE
TOPIC MODELING
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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.
CORRELATION BY CATEGORY
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To find the per-document probabilities we extract theta from the fitted model’s topic posteriors
TOPIC TRENDS
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Using mean of theta by years to trend topics
TOPIC VISUALIZATION
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GRAPH MODEL OF LESSON LEARNED DATABASE
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http://davidmeza1.github.io/2015/07/16/Graphing-a-lesson-learned-database.html
GRAPH MODEL OF LESSON LEARNED DATABASE
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GRAPH MODEL OF LESSON LEARNED DATABASE
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DATA DRIVEN VISUALIZATION
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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
Contact Information
David Meza – [email protected]
Twitter - @davidmeza1
Linkedin - https://www.linkedin.com/pub/david-meza/16/543/50b
Github – davidmeza1
Blogdavidmeza1.github.io
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Contents
© 2015 IHS. ALL RIGHTS RESERVED. 28Report Name / Month 2015
QUESTIONS?