DLIS Data Quality Richard A. Hansen Chief, Data Integrity Branch DLIS-SDQ October 11, 2007

1. DLIS Data Quality Richard A. Hansen Chief, Data Integrity Branch DLIS-SDQ October 11, 2007

2. Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier DNA of the DOD Supply Chain • “Data is the DNA of supply chain management” • Acquisition • Financial management • Hazardous material • Freight & packaging • Maintenance • Sustainability • Disposal • Demilitarization Weapon System Lifecycle Management Define New Requirements Sustain Design Test Build Retire Deploy INFORMATION MANAGEMENT DLIS Material Supply and Services Management Who is the customer? What is needed? How many are needed? Where is it needed? Maintenance & Configuration Acquisition Management -Contract -Provision -Purchase Ongoing Requirements & Demand Management Materials Management & Warehousing Distribution & Transportation Management Disposal • What meets the requirement? • How many do we have and where? or, Where/how can we obtain? • How must it be handled? Quality Finance Reporting Retail

3. Costs and Benefits • Geometric Cost Model for Bad Data • $1.00 to correct an error at data entry • $10.00 to correct a number of errors after the fact with batch processing • $100 cost of not correcting an error • Benefits • Eliminates extra time to reconcile data • Alleviates customer dissatisfaction • Helps prevent loss of system credibility • Eliminates need for extra mailings to explain system downtime • Prevents some revenue loss • Assists with compliance issues

4. DLA Data Master

5. DLIS Data Quality Process • Knowledge exchanges with the experts – Universities, Gartner, others • Plan addresses: People-Process-Technology • Management priority / visibility • Program managers: overall responsibility • Data stewards: analyze, measure, report and support PMs • Elaborate, fact-based methodology / measures • Edits, profiling tools and system checks

6. DATAQUALITY METHODOLOGY The Process People Process Technology Accuracy Consistency Currency Completeness The Results

7. QUALITY ASSESSMENT- JUNE 07 Process Step – Measure/Baseline A – Accuracy CN – ConsistencyCR – CurrencyCM- Completeness NM-Not Measured Over all Over all DQ ISSUES A CN CR CM DQ ISSUES A CR CM CN 1. National Item Identification Number (NIIN) 99% 98% 100% 98% 99% 6. Net Unit Weights 50% NA NA 53% 51% 2. Hazardous Characteristics Code (HCC) NA NA NA 70% 70% 99% NA NA 99% 99% 7. Invalid FSCs 3. Hazardous Material Indicator Code (HMIC) 99% 99% 99% 99% 99% 79% NA 79% 79% 79% 8. HMIC of “D” 99% NA NA NA 99% 9. Preliminary Records 97% 97% NA 97% 97% 4. HCC in FLIS Seg C, But not in Seg A 5. FLIS CAGE Verification 50% NA NA 50% 50% Grading Scale DCB Recommendations: 90-100% EXCELLENT 80-89% GOOD 70-79% FAIR 60-69% POOR 59%-0% BAD NM Not Measured NA Not Applicable • Issue 8 Zero Filled LIINs/NIINs Removed • Issue 1 Re-measured, no longer issue. Remove after 90 day check (9/4/07) • Updated stats to Issues 2,7, PMO/DS: Revision: 16 Date Briefed:

8. Root Cause Analysis Flow If training exist, revise as needed If training does not exist, establish it Administer refresher of training as needed Data Issue = Cause = Focus Area = Yes Training problem? No Yes If policy exist, revise as needed If policy does not exist, establish it Administer refresher of Policy as needed Policy problem? No Document resolution and close problem Yes If procedures exist, revise as needed Procedure problem? If procedures do not exist, establish them Administer refresher of Procedures as needed No Yes Internal system problem? If edits exist, revise as needed If edits do not exist, establish them No Yes If interface exist, revise as needed If interface does not exist, establish it Interface problem? Other errors No Sy System/Product: Revision: Date:

9. Root Cause Analysis Data Quality Issue # 10 Invalid Cities (not alpha) Error Type = 3,4,5 Focus Area = A Identified as a Data Quality Issue System interrogated for analysis Accuracy, Consistency, Currency and Completeness = 100% Not a Policy problem Not an Internal System Error Entered Incorrectly by Focal points Revise Procedures with changes below Training problem Revise Training with changes below Interface System Error Not a Procedure problem Migration of dirty data – Require comparisons and verifications Fix existing errors Re-measured for new percent ratings Document resolution and close problem System/Product:HMIRS Revision: A Date: 8-29-05

10. Emerging Systems • Planning • Establish Data Cleansing Champion • Subject matter experts • Business rule development • Review data • Authoritative data sources • Develop business rules • Establish frames • Data Cleansing • Reporting • Track progress • Auditing Start Early!!! Dedicate resources

11. International Standards • ISO 22745 is a standard that that covers the tools for encoding data (*expected to be published in 2007) • ISO 8000 is a standard for information quality in terms of encoding, completeness, origination and accuracy (*expected to be published in 2008)

12. Data Quality • Data Quality is not magic: It’s the “new normal” and must be done DOD wide • Functional “owners” have a key role: Data experts lead the quality effort • Transformation programs: Need for quality permeates all DOD logistics data • Built into each new system • Participation • COURAGE

13. Data Quality