Trends and challanges for IT in Knowledge Management

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Trends and challanges for IT in Knowledge Management

  1. 1. Yury Kupriyanov AIST’2014 1
  2. 2. Who am I to lecture you 2 Head of practice Enterprise crowdsourcing and knowledge management at WikiVote! New media trainer Freelance lecturer at HSE (Moscow) WikiVote! specializes in creating crowdsourcing platforms for citizen participation in drafting laws, analysing and constructing corporate and governmental strategic documents.
  3. 3. KM strategies Push ••••••••••••••••••••••Pull Codification  ••••••Personalization 3 Techno-centric EcologicalOrganizational
  4. 4. KM in Russian companies 4 Models of knowledge management in Russian institutions: social and psychological analysis, T.Nestik, 2013. Knowledge management tool/technique % of companies News portal 95,2 Internal consults/trainers 81 Knowledge base/electronic library 71,4 Enterprise forums/blogs 71,4 Mentoring 71,4 Remote learning 57,1 Competence models 47,1
  5. 5. KM in Russian companies 5 Models of knowledge management in Russian institutions: social and psychological analysis, T.Nestik, 2013. Knowledge management tool/technique % of companies Workgroups communication systems 38,1 Online experts networks 38,1 Innovations and idea management 33,3 Expert knowledge elicitation 28,6 Communities of practice 28,6 Wikis 9,5 Corporate experts locator 9,5 Centers of competence 9,5
  6. 6. KM mainstream now is… • Organizational strategy • Pull methods • Personalization approach • People-oriented approach 6
  7. 7. Big Data on the Hype • And what computer scientists offer to the industry is the data analysis tools. 7 Big Data
  8. 8. And where they meets? Knowledge Information Data 8 What companies need What scientists offer
  9. 9. What’s the difference? 9 OODA loop. Boyd, John R. Observe Orient Decide Act Data Information Knowledge
  10. 10. What can IT do anyway? • Intuition: –Accelerate processes –Increase an effectiveness –Decrease costs • Facts? 10
  11. 11. ROA – no correlation 11 Pisello T., Strassmann P.  IT Value Chain Management — Maximizing the ROI from IT Investments. Information Economics Press, 2003.
  12. 12. ROE – no correlation 12 Pisello T., Strassmann P.  IT Value Chain Management — Maximizing the ROI from IT Investments. Information Economics Press, 2003.
  13. 13. SG&A correlation 13 Pisello T., Strassmann P.  IT Value Chain Management — Maximizing the ROI from IT Investments. Information Economics Press, 2003.
  14. 14. Organizational changes 14 Brynjolfsson E., Hitt L.M. Computing Productivity: Firm-Level Evidence // MIT Sloan Working Paper. 2003.
  15. 15. IT is about acceleration 15 • IT by itself generate no new business value. • IT can only accelerate existing processes. • If you have sick business-processes, IT will accelerate the sickness. • If your solution do not offer a business value in terms of ROI, it will be useless or rather harmful.
  16. 16. What can we accelerate? 16
  17. 17. What can we accelerate? 17 • On the Observe phase: – Collection, extraction and elicitation data • Automatic (people are not involved) • Crowdsourcing (people are actively involved, i.e. weak signals) – Construct new quantitative business parameters • Big Data • or at least some data.
  18. 18. What can we accelerate? 18 • On the Orient phase: – Information analysis and visualization with specific purpose – Alignment of ontologies, points-of-view and a priori information – Acceleration of communication
  19. 19. What can we make? 19 • On the Decide phase: – Coordination of information flow – Elimination of waste of time • Lean information work process • Just-in-time information delivery – Decision support systems • Automated reasoning • Knowledge bases • Lessons learned repositories
  20. 20. What can we accelerate? 20 • On the Act phase: – Knowledge management in production and supply chains – ―Real‖ crowdsourcing (collaborative creating) – Co-creation • And don’t forget Feedback loops! – Measurement – Communication
  21. 21. KM dimensions 21 – Strategy: • Techno-centered or organizational • Codification or personalization • Pull or push – Level of DIKW (phase of OODA loop) – Type of Knowledge: tacit or explicit – Number of participants – Planning perspective (strategic, tactical, operational) – Creation or transfer
  22. 22. Conclusion 1. KM mainstream: organizational, pull, personalization, people-oriented. 2. KM in Russian companies: techno- oriented, push, codification, people-oriented. 3. IT solutions are not work without corresponding organizational changes. 22
  23. 23. Recommendations • When you make a new product or research related to KM, remember dimensions and think about just two question: How your product will help business to make decisions? What people would do with your product? 23
  24. 24. Thanks for your attention! Questions?

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