Impact of Knowledge Management System in Enterprise Architecture

1. Impact of Knowledge Management System in Enterprise Architecture Name John Laskar – Presenter PhD student at Engineering Management and Systems Engineering Department, George Washington University. Other Authors – Dr. Tom Holzer Dr. Tim Eveleigh Dr. Shahryar Sarkani Engineering Management and Systems Engineering Department, George Washington University. September 17, 2014

2. Agenda Introduction Problems Background Methodology Conclusion

3. Introduction/ Definitions

4. Purpose/Objective • Present the dissertation research Problem • Assess impact of the research • Receive feedback from the participating INCOSE technical community • Seek participants to fill out the Survey

5. Research Question, Problem statement and Research Objective Research Question 1: Do the critical barriers inhibit use of KMS in Enterprise Architecture? Research Question 2: Does enterprise fail to leverage on KMS? Problem Statement: Existing barriers of KMS, and enterprise’s failure to realize their benefits inhibit application of KMS in EA Research Objective: Create a new technique for KMS application

6. Problem • ● Problem 1: Enterprise fails to see KMS needs. Realizes only after spending significant amount of IT budget • Problem Significance: • Unprecedented Knowledge loss. Millions of Baby-boomers retire this decade; only 25% of US companies has plans (according to INC) • Hypotheses: There is urgent need to Preserve Tacit Knowledge, use of KMS helps to solve this problem • Productive technologies worth $115 Billion sit idle today in U.S. companies (Estimates of Technology transfer broker “BTG”). • Hypotheses: • Enterprise need to use KMS to control IT waste

7. Problem • Problem 2: Critical barriers inhibit KMS in an enterprise • Problem Significance: • Enterprise Information management problem is very serious Sarbanes- Oxley Act • Requires executives to take responsibility for what happens within their companies. Hypotheses: Organization culture, Management’s understanding of needs, IT investments , lack of performance measurements are barriers of using KMS

8. Problem • ●Problem 3: Best Practices and Lessons learned are not shared • Problem Significance: • Create Knowledge Gaps • Redundant operations • Excessive resource waste • Increase expenses • The Cost: $588 Billion per annum in the United States alone (A study by “Basex” 2005) • Misuse of KM tools (i.e., e-mail, Web, instant messaging, social networking) • Interrupt knowledge work • Significant downtime Hypotheses: EA includes KMS to control IT waste

9. Background The Four KM Pillars by Stankosky/Baldanza and validated by Calabrese/Bixler)

10. Background (continued) The Four KM Pillars by Stankosky/Baldanza and validated by Calabrese/Bixler

11. Background (continued) • Successful KM program requires a more inclusive set of disciplines, elements, and processes, i.e., a KM framework model applicable to virtually all business domains (Stankosky, 2002)

12. Background (continued) (Stankosky, 2001)

13. Possible Solutions • Solution to Problem 1: • To become thought leaders, KM-driven Enterprise need: • collaborative thought leadership • New visualization tools (John Lewis) • Solution to Problem 2: • Recognition of Knowledge-based economy • Knowledge is org.’s most critical resource (Liebowitz, Lynch) • Solution to Problem 3: • Leverage on Knowledge assets (Stankosky) • Improve performance • Effectiveness • Innovation

14. Methodology Enterprise Arch Lit. review KM Lit. review Essence of EA Best Practices FEA KM body of Knowledge Stankosky KM Framework CPIC EA Knowledge Repository Intellectual Capital, KM Tools OMB’s Performance Improvement Life Cycle IT Innovation Knowledge Base KM and EA Research Gap Hypotheses Survey

15. Methodology • Key KM focus: • Systematic Process • Acquire knowledge assets • Organize knowledge assets • Communicate knowledge assets • Information Technology (IT) • Intelligence Capital (IP) • Organization efficiency • Innovation • Human development • Competitive business advantage • Organization Performance improvement • Key EA focus: • Manage change within organization • Achieve strategic initiatives • Systematic Process • Acquire knowledge assets • Organize knowledge assets • Communicate knowledge assets • Information Technology (IT) • Intelligence Capital (IP) • Organization efficiency • Innovation • Human development • Competitive business advantage • Organization Performance improvement Knowledge Management (KM) Enterprise Architecture (EA) KMS area KM EA • Key KMS focus: • KM toolkit, Technology • Managing Knowledge in an organization • Knowledge integration in virtual teams • Motivating Knowledge sharing • Measuring KM performance, KM metrics • Bridge between KM consultants and technologists

16. Methodology – Status update • Two overarching research questions • Literature review generates hypotheses • Survey questions relating each hypotheses • A survey Questionnaire designed that are meaningful and relevant while also interesting, engaging, and quickly answered

17. Data Analysis method • Research Hypotheses: Literature review leads to 4 Barriers, 4 Benefits and 4 Groups • 4 Barriers • Govt. vs. Private : 4 Hypotheses • Large vs. Small : 4 Hypotheses • Management vs. K-worker : 4 Hypotheses • Product vs. Service : 4 Hypotheses • 4 Benefits • Govt. vs. Private : 4 Hypotheses • Large vs. Small : 4 Hypotheses • Management vs. K-worker : 4 Hypotheses • Product vs. Service : 4 Hypotheses • Total Number of Hypotheses: 16 + 16 = 32 4 Barriers Descriptive Statistics 4 Benefits Descriptive Statistics Group Correlation Large and Small Government and Private Product and Service Manager and K- worker

18. Theoretical Concept Diagram Survey Question Numbers Survey Sample areas Research Question1 (RQ1) Part A Q1-Q19 Large vs. Small Enterprise H1- H16 H1- H4 Part A Q1-Q19 Government vs. Private Enterprise H5- H8 H9- H12 Part A Q1-Q19 Manager vs. K. worker Enterprise KMS Barriers in EA H13- H16 KMS Benefits In EA Part A Q1-Q19 Product vs. Service Enterprise H17-20 Part B Q1-Q11 Large vs. Small Enterprise H21-24 Government vs. Private Enterprise Part B Q1-Q11 Research Question 2 (RQ2) H25-28 Manager vs. K. worker Enterprise Part B Q1-Q11 H 17- H32 H29-32 Product vs. Service Enterprise Part B Q1-Q11

19. Data Collection/Analysis • Survey 4 groups • Government and non-Government • Large and Small enterprise • Product and Service enterprise • Executive Managers and Knowledge workers • Develop cross-correlation statistics among groups • Descriptive statistics, t-test hypotheses (using SPSS/minitab/Excel spread sheet)

20. Conclusion • Identified 3 different problems • Literature Review corresponds with initial findings • Survey Instrument “Questionnaire” Pilot developed, Pre-tested, and finalized • Survey in progress • Data collection and Analysis preliminary stage

21. References • Tamara Schweitzer, INC.http://www.inc.com/news/articles/200703/boomers.htmlsearched on April 23, 2012. • John Lewis, The Explanation Age, Option Outlines, 2012 • Liebowitz, 1999, Lynch, 2002 Organizational learning • M. Stankosky, L. Vandergriff, A. Green, In Search of Knowledge Management: Pursuing Primary Principles, Emerald, 2010 • J.W. Ross, P. Weill, D. C. Robertson, Enterprise Architecture As Strategy, Harvard Business School Press, 2006 • M. Franco, S. Mariano, Information Technology Repositories and Knowledge Management Processes: A Qualitative analysis, VINE: The Journal of Information and Knowledge Management Systems, • Vol. 37, No. 4, 2007, pp 440-451.

22. Discussion • Do you have any questions? • Will you Fill Out Survey?