Data Analytics of Strategic Information Technology Asset Reviews in the Office of Investment Management (OIM) Component ofat the Social Security Administration (SSA).

1. Data Analytics of Strategic Information Technology Asset Reviews Brian Bissett Staff Analyst Social Security Administration Office of the Chief Information Officer

2. Overview Background & Goals Qualitative Data Collection SharePoint Surveys & Lists Quantitative Data Collection InfoPath/SharePoint Excel Templates Data Analysis & Scoring Resource Maximization Data Reporting & Dashboarding Action Items, Lessons Learned, & References

3. Background The Social Security Administration has had Administrative Expenses of combined expenditures from its trust funds of less than or equal to 1.0% since 1989, an enviable record that few (if any) organizations in the private or public sector can match. 1 As the Agency is tasked with legislative mandates to take on more Health Care related IT programs, care must be taken to select the best IT programs to maintain low administrative expenses and maximize the benefits to the public. 1. http://www.ssa.gov/oact/STATS/admin.html

4. Why We have not been Privatized Sources: http://krugman.blogs.nytimes.com/2009/07/06/administrative-costs/ http://wonkroom.thinkprogress.org/2009/04/14/administrative-costs-good/

5. Pay for Performance?Not All the Time! The Government Accountability Office has concluded that private plans channel extra subsidies towards increased profit, not improved benefits.1 Just as with a mutual fund, paying an administrative fee does not pay for better performance, it pays for a salesman. 1. GAO-08-827R June 24, 2008.

6. Reality and Consequences SSA is a conservative agency. The Checks have to go out. SSA has legacy systems that use 60 million lines of Cobol computer code. SSA continues to heavily utilize mainframes. With such low administrative expenses, SSA is intolerant of poor funding choices. Failure to make the right IT investments for both the present and the future can handicap an organization to the point where it can no longer accomplish its mission directives.

7. GOAL OF SITAR: Keeping Admin Costs Low The Strategic IT Asset Review (SITAR) process was created to evaluate the programmatic costs and benefits of proposed IT programs and to ensure a uniform process is utilized in selecting the best efforts for the agency to start or continue funding. Determine the Bottom 1/3 Performing IT Investments at SSA and then. . . . . Turn them around or KILL them.

8. Survey There are two methods of easily Collecting information in SharePoint, a Survey or a List.

9. Conditions to Survey Has the Scope of the initiative changed? (From what the PSA specified.) Changes to the Initiatives Schedule. Concerns regarding government or contractor resources. Concerns regarding user acceptance. Concerns regarding risk. (how have risk mitigation strategies impacted)

10. Data Collection: Excel Web Access

11. Data Collection – InfoPath

12. Data Collection: PID Information

13. Data Analysis – OCIO Excel Tab SME Matrix PID Level ROI CBA PID Rollup Report Card

14. Scoring Algorithm Initiatives are evaluated by components within SSA by answering a series of questions about various conditions in the initiative with four possible answers. Has the condition changed from specifications? Yes/No Initial Question; No = 100 Yes - Minor Changes = 50 Yes - Some Changes = 25 Yes - Major Changes = 0

15. Scoring Algorithm A Final Question assesses the Overall Project Health which has three possible answers: The overall health of this initiative is: Acceptable = 75 Questionable = 50 Poor = 25 This question is answered by the AC in charge of the initiative.

16. The answers to the questions are then averaged and binned. Bins for Icons: Red 25 > Score (Average) Yellow 50 > Score (Average) >= 25 Green Score (Average) >=50 Reporting: Traffic Light Style

17. Subject Matter Experts - SMEs Subject Matter Expert – is any non-Systems person or persons who understand the business process or area well enough to describe it to the IT Staff and answer questions as they arise. They are also the persons who are in the critical path of the project and provide input and feedback during the many steps of a project’s lifecycle. Does not include casual meeting attendees.

18. SME MATRIX Initiative/Component

19. Resource Utilization Modeling Many Resource Utilization Models assume linearity. This is done because Linear Behavior is very predictable where as non Linear Behavior is seldom predictable with the exception of exponential growth and decay. But few things in life exhibit the regular recurring predictable behavior associated with Linearity, most often they are Skewed.

20. Resource Modeling Right Skew Typical Behavior, Lots of Resources at the Beginning, fewer Resources utilized toward completion.

21. Resource Modeling Left Skew Less Frequent, more indicative of projects that are resource intensive on the back end such as construction projects.

22. Resource Modeling No Skew Resembles a Normal Distribution. Planning, Building, Integrating, Testing type of Model.

23. Resource Modeling Notch The Failure Curve. Build, Test, Rebuild. Same effort level at Beginning and End.

24. How much can be Supported? Question: We have 10 Automation SME resources, and 30 Projects, each of which require 1/3 of a Automation SME Man Year. How many Projects can be supported? Answer: It Depends on the Skew. If all projects are left skewed we can only support 10 projects. If 1/3 of the projects are skewed left, 1/3 skewed right, and 1/3 skewed center, we can support all of them.

25. Dilemma Obviously, the case where all projects can be supported is optimal. So it would be best to have a situation where resources are distributed equally across all potential time frames which mimics linear behavior. Linear Behavior is desirable for Analysis. But Linear Behavior in Life is Uncommon.

26. “Faking Linearity”

27. Burn Rates – Ideal Case

28. Burn Rates - Reality

29. Resource Utilization (Burn) Key Points: Projects will not burn resources in a linear manner. The PM should be aware if the project is over burning or under burning its resources, and take appropriate action if the burn rate is not appropriate for the phase of the project. When over burning occurs, are SME and other resources being deprived to other more critical projects?

30. Data Reporting: Rollup

31. Data Reporting: Presentation

32. IT Health DashboardExcel Conditional Formatting RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA

33. IT Health DashboardExcel Published HTML RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA RANDOMLY GENERATED DATA

34. IT Health DashboardLimitations of Implementation

35. Component/Project Breakdownby Work Years

36. SSA Open Government Dashboards – Top Level

37. SSA Open Government Dashboards – Investments The Government Really is Open, just look at this rating.

38. Candidates for Termination Has Limited Strategic Value Based on the current Agency Strategic Plan (ASP). Is on the line and has not been started. Is on the line and is not doing well. SME Hog Unfavorable ROI

39. When Termination is not Possible Some Projects simply cannot be killed because they are legislatively mandated, or so called “Sacred Cows.” While these projects must get done, flexibility exists in that we can change how they are done if they are underperforming. It is important to identify underperforming programs even when they are mandated by Congress.

40. Lessons Learned SharePoint Functions well as a document repository, but needs improvement for efficient data collection. InfoPath is not a mature product and lacks many basic functions that would be expected in a third generation product. Excel can function well as a Data Analysis tool when used with VBA, but Worksheets must be Locked down.

41. Lessons Learned Continued How a SharePoint site is administered can have grave consequences in terms of limiting the ability of groups to create custom solutions to complex agency initiatives. It is always easier when deploying a new solution to incrementally feed a component a piece of the new pie than to try to stuff the whole pie down their throat at once.

42. Recommended Third Party Tools SparkLines for Excel http://sparklines-excel.blogspot.com/ Add-Ins for Excel Productivity Suite http://www.add-ins.com/ Bamboo Solutions http://store.bamboosolutions.com/ Excel Web Services http://msdn.microsoft.com/en-us/library/aa973804(office.12).aspx QdabraqRules http://www.qdabra.com/en/products/qRules.aspx

43. The Visual Display of Quantitative Information – Edward Tufte Edward R. Tufte, Ph.D., Yale University http://www.yale.edu/polisci/people/etufte.html http://www.edwardtufte.com/tufte/index Four Books on Data Visualization: The Visual Display of Quantitative Information, Envisioning Information, Visual Explanations,Beautiful Evidence.

44. Books Practical Pharmaceutical Laboratory Automation Hardcover: 464 pages Publisher: CRC Press (May 2003) ISBN: 0849318149 Automated Data Analysis with Excel Softcover: 442 Pages Chapman & Hall (June 2007) ISBN: 1-58488-885-7

45. Acknowledgements Lester Diamond – Associate CIO Donna Meekins – Director OCIO/OIM Don Ingraham – Director OCIO/OIM Jeff Wilhide – SharePoint Administrator OIM S. Jennifer Haggerty – IT Specialist OCIO/OIM Sue Meekins – IT Specialist OCIO/OIM Wayne Slechter – IT Specialist OCIO/OIM David Ordonio – IT Specialist OCIO/OIM Carla Sateriale – Research Assistant at IMF Brandon Williams – IT Specialist OCIO/OIM