NYS Forum Master Data Management

1. NYS Forum Master Data Management Using NPV to Justify the MDM Project Rick Humphress Oracle Corporation 28 October 2010

2. Agenda MDM NPV Examples • Introduction • Definitions: MDM, NPV • State Tax Gap • Problem, Solution, Parameters, NPV Analysis, Next Steps • Improper Payments • Problem, Solution, Parameters, NPV analysis, Next Steps • Summary

3. Master Data Management Definition “Master data is the consistent and uniform set of identifiers and extended attributes that describe the core entities of the enterprise and are used across multiple business processes.” Gartner Group, MDM Magic Quadrant1 Examples of core entities: Parties– people: citizens, employees, vendors, suppliers Places - locations, offices, regional alignments, geographies Things- accounts, assets, policies, products, services 3

4. MDM as a Manufacturing Process2 Total Information Quality Management3 Data Definition Quality Assess Info Quality Measure Cost of Quality Reengineer, Correct Data Continuous Improvement 4

5. Net Present Value (NPV) NPV = Sum of Benefits + Sum of Costs4 • Positive discounted cash flows metric (NPV > 0) • Uses “With-Without” principle • Useful throughout the project lifecycle • “Sanity” NPV v. formal NPV • Costs front loaded; benefits back loaded Indirect Direct Revenue Enhancement Cost Avoidance 5

6. Tax Gap: Problem • New York personal income tax gap is $2.8B5 • Unknown errors: exemptions, deductions, credits6 • Falling incomes, business revenues, base valuations7 • Half of small business, self-employed cheat8 • Only 1.03% of returns audited9 6

7. Tax Gap: An MDM Solution • Incomplete Data • Poor design • Poor structure • Poor implementation • Politics Poor Data Quality • Data Decay • Data Fragmentation • Poor design • Overwriting • Transactional Errors • Internal ERP • External Web • MDM Entities/Attributes • Parties • Human person • Corporate person • Places • Jurisdictions • Locations • Things • Income brackets • Bases 7

8. Tax Gap: Forming “Sanity” Assumption • Enrich the data • D&B, Acxiom and others • IRS, SSA, Homeland Security • Other NY State agency data • Other States • Reduce errors at system source • Edits at time of data entry • Better pattern matching • Better audit decision metrics • More automation; less human time 8

9. Tax Gap: Parameters Variable Value Description • Cost Discount 8% Cost cash flow discount rate • Benefit Discount 12% Benefit cash flow discount rate • Tax Gap $2,800,000,000 NY Personal Income Tax gap11 • Current results $500,000,000 NY Audit/Compliance enforcement results12 • Return multiplier 14 Highest typical return on tax improvement project13 • Benefit target $48,941,482 Cost straw man is $3,495,820 • Old Percent Gap 13.725% NY PIT tax gap as % of $20.4b total obligation • New Percent Gap 13.295% “Sanity” assumption • Difference 0.430% Current gap minus expected gap • DRE attribute 70% Direct revenue enhancement projection • DRE1 attribute 70% nonwage income increased • DRE2 attribute 30% exemptions, deductions, credit accuracy • IRE attribute 30% Indirect revenue enhancement projection increased deterrence • Rework, scrap % 0.05% Internal cost of quality • Headcount avoid 3 “Sanity” assumption • Headcount cost $120,000 Fully-burdened employee cost avoidance 9

10. Tax Gap: NPV Analysis 10

11. Tax Gap: Suggested Next Steps • Gather data relating data quality to nonwage income • Gather data relating data quality to exemptions, deductions, and credits • Measure the cost of data quality14 • Prevention, Assessment, Internal Failure, External Failure • Reengineer data acquisition process; correct data • Determine the best tax fraud models to launch against the complete and accurate data • Determine who to audit Total Information Quality Management Data Definition Quality Assess Info Quality Measure Cost of Quality Reengineer, Correct Data Continuous Improvement

12. Improper Payments: Problem • Program fraud rates between 5-14% of budget15 • NY State Operating Fund: $78,998,000,00016 • First, second, third person fraud • Federal scrutiny in the era of accountability17 The Federal Problem18 • $110 billion in 2009 • $180 million to 20,000 dead people • $230 million to 14,000 fugitive felons 12

13. Improper Payments: An MDM Solution • Poor Data Quality • Data Decay • Data Fragmentation • Poor design • Overwriting • Transactional Errors • Internal ERP • External Web • Incomplete Data • Poor design • Poor structure • Poor implementation • Politics • MDM Entities/Attributes • Parties • Human person • Agencies • Places • Jurisdictions • Banks • Things • Accounts • Policies 13

14. Improper Payments: Specific Suggestions • Enrich data • D&B, Experian, and others • GSA, SBA • Other NY State agency data • Other states • Enforce debarment • Better pattern matching • Alerts for suspicious transactions • Better anomalous pattern matching • “Qui tam”20 14

15. Improper Payments: Parameters Variable Value Description • Cost Discount 8% Cost cash flow discount rate • Benefit Discount 12% Benefit cash flow discount rate • Total Operating Fund $78,998,000,000 NYS Operating Fund • Current fraud rate 5.00% Lowest fraud rate assumption • New fraud rate 4.98% “Sanity” assumption • Difference 0.02% Fraud rate improvement 15

16. Improper Payments: NPV Analysis 16

17. Improper Payments: Suggested Next Steps • Gather data relating data quality to potential frauds • Measure the cost of data quality • Prevention, Assessment, Internal Failure, External Failure • Reengineer data acquisition process; correct data • Determine the best fraud models to launch against the complete and accurate data • Initiate legal process against fraudsters Total Information Quality Management Data Definition Quality Assess Info Quality Measure Cost of Quality Reengineer, Correct Data Continuous Improvement 17 17

18. Summary • MDM is a continuous improvement process • Estimate and then search for strengthening evidence • MDM solves significant data quality problems: • Data decay and fragmentation • Incomplete data • MDM can generate substantial real-world benefits: • Close the tax gap • Decrease social service payments • Reduce improper payments Can you afford not to master your data? 18

19. Questions Rick Humphress Oracle Corporation 240-426-4445 Rick.Humphress@Oracle.com

20. References [1] Radcliffe, J. (2010). Magic Quadrant for Master Data Management of Customer Data. Gartner Group. Gartner RAS Core Research Note G00206031. Retrieved on 22 October 2010 (subscription required) from http://www.gartner.com/technology/media-products/reprints/oracle/article158/article158.html [2] Even, A., & Shankaranarayanan, G. (2009). Utility cost perspectives in data quality management. Journal of Computer Information Systems, 50(2), 127-135. Retrieved from Computers & Applied Sciences Complete database. [3] English, L. (2004). Six Sigma and Total Information Quality Management (TIQM). DM Review, 14(10), pp. 44-73. [4] Ross, S., Westerfield, R., & Jaffe. J. (1996). Corporate finance, 4th ed. Chicago: Irwin. [5] Bolognino, D. (2005). New York state personal income tax compliance baseline study: Tax year 2002. New York State Department of Taxation and Finance Office of Tax Policy Analysis. Retrieved on 09 October 2010 from http://www.taxadmin.org/fta/meet/05rev_est/bolognino.pdf [6] Reed, B. J., & Swain, J. W. (1997). Public finance administration, 2nd ed. Thousand Oaks, CA: Sage Publications. [7] Johnson, N., Nicholas, A., & Pennington, S. (2009). “Tax Measures Help Balance State Budgets”. Center on Budget and Policy Priorities. Retrieved on 23 October 2010 from http://www.cbpp.org/cms/index.cfm?fa=view&id=2815 [8] Morse, S. C. (2009). Using Salience and Influence to Narrow the Tax Gap. Loyola University Chicago Law Journal, 40(3), pp. 483-530. Retrieved on 09 October 2010 from http://www1.luc.edu/law/activities/publications/lljdocs/vol40_no3/vol40_issue3/Morse.pdf [9] Morse, 2009 [10] n.a. (2010). State government finances. US Census Bureau. Retrieved on 23 October 2010 from http://www.census.gov/govs/state/

21. References [11] Bolognino, 2005 [12] Bolognino, 2005 [13] Robinson, M. W. (2009). The state’ stake and role in closing the federal “tax gap”. Virginia Tax Review, 28(4), p959-984. [14] Krajewski, L. J., Ritzman, L. P., and Malhotra, M. K. (2010). Operations management: Processes and supply chains, 9th ed. Upper Saddle River, NJ: Prentice Hall. [15] n.a. (2010). Report to the nations. Association of Certified Fraud Examiners. Retrieved on 23 October 2010 from http://www.acfe.com/rttn/2010-rttn.asp [16] Paterson, D. & Megna, R. (2010). Five Year financial plan executive summary. State of New York. Retrieved on 23 October 2010 from http://publications.budget.state.ny.us/eBudget1011/financialPlan/1011_FinPlanSummary2.html [17] Obama, B. (2009). Reducing improper payments and eliminating waste in federal programs. US Office of Management and Budget. Retrieved on 23 October 2010 from http://www.whitehouse.gov/the-press-office/executive-order-reducing-improper-payments [18] n.a. (2010). President Obama to Sign Improper Payments Elimination and Recovery Act. US Office of Management and Budget. Retrieved on 23 October 2010 from http://www.whitehouse.gov/the-press-office/president-obama-sign-improper-payments-elimination-and-recovery-act [19] Cressey, D. (1958). Other People's Money: A Study in the Social Psychology of Embezzlement. Montclair, N.J.: Patterson Smith, 1973. ISBN 9780875852027 [20] Fraser, E. (2008). Reducing fraud against the government: Using FOIA disclosures in qui tam litigation. University of Chicago Law Review, pp. 497-532. Retrieved on 24 March 2010 from Academic Search Complete database.