Data Quality The Logistics Imperative

1. Elaine S. Chapman Defense Logistics Information Service Chief, Data Integrity Branch October 26, 2006 Data QualityThe Logistics Imperative

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. DATA INTEGRITY . . . It’s About Parts From a logistics perspective . . . supporting an F-15 is about 171,000 parts flying. . . and a Bradley is about 14,000 parts rolling in close formation

4. In this case $125,000,000The price of a Mars Climate Orbiter How Expensive Can Bad Quality Data Be?

5. Root Causes of Poor Data Quality Shared Data Problems Interface Disconnects Interface Novaces, LLC

6. Benefits • Saving money right from the start • $1 to correct an error at data entry • $10 to correct a number of errors after the fact with batch processing • $100 cost of not correcting an error • Benefits • Eliminates time to reconcile data • Alleviates customer dissatisfaction • Prevents loss of system credibility • Eliminates system downtime • Prevents some revenue loss • Assists with compliance issues

7. Master Data Management BSM • Authoritative sources • Data Standards • Meta Data ARMY NAVY …No New Stovepipes Air Force

8. Vendor Master Example Data Input Data Output > 500 BUSINESS RULES > 500 EDITS CAGE INTERNATIONAL Public Web Search On-line D&B Parent Linkage On-line DUNS Validation Daily Extracts 3rd Party Data Validation/Certification XML Transactions SBA CAGE ERP DLA Business Systems Modernization, Army Logistics Modernization Program USPS Federal Reserve CCR Tools IRS NOC NO ANNUAL UPDATE = INACTIVE

9. 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

10. Action Plan Define – Identify Data Issues Measure – Apply appropriate metrics. Improvements – Address needed enhancements. Implement – Initiate approved changes/corrections. Monitor – Re-measure for effectiveness. Report – Document status improvements and cost savings. The Results Data Quality Methodology The Process People Process Technology Accuracy Consistency Currency Completeness

11. DQ Applied • Identified top five queries for program • Worked with Program Manager to prioritize data elements • Broke them into small pieces that can be measured • Worked with contractor • Began building metrics • Get downstream systems involved in reviewing/implementing solutions

12. Quality Assessment Oct 06 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 11 Character In RF ITV NM 45% NM NM NA 49% 49% 49% 49% 49% AV DODAAC Wrong Van Owner Data 30% NM NM NM NA NM NM NM 98% NM A0 VLIPS flow to AV NM NM NM 4% NA AS VLIPS flow to AV NM NM NM 0% NM No Van Owner Data NM 48% NM 48% NA AE VLIPS flow to AV NM NM NM 52% NM Leading Zeros NM NM NM 24% NA DR VLIPS flow to AV NM NM NM 0% NM Cross Dock Operations Issues/Concerns: DCB Recommendations: Grading Scale We have identified 5 of 117 public queries. We have researched 3 of 105 data elements of the 5 queries 90-100% A Green 80-89% B Yellow 70-79% C Orange 60-69% D Pink 59%-0% E Red Not Established - White Data Date: 1 Jun 06 % PM/DS: Teresa Lindauer / Rich Hansen Participants: Date Briefed: 5 Jun 06 Baseline Grade

13. System/Product: Asset Visibility/ In-Process-Doc ID Revision: 1 Date: 15 May 06 Root Cause Analysis Doc ID Error Types 4 - Interface 5 - Procedure Yes The Doc ID are not flowing from DMARS/SOMA to AV Training problem? Interface problem? Analysis step (DQ indicators 1., 2.) Proposed solution No Yes Policy problem? No Document resolution and close problem Monitor improvement via metric No Yes The DOC ID A4x and ACx are not in the AV ODS Procedure problem? Procedure problem? Analysis step (DQ indicator 3. and 4. Proposed solution No No Other Error

14. Template for Analysis of System Potential IssuesAnalyze and Improve

15. Using Standards to Ensure Quality • The NATO Codification System is the foundation of an international standard for product and service descriptions • The eOTD is an open standard for encoding product data through the life cycle of a product – from design through disposal

16. ISO 8000**in development • Labeling:  Each data element must be tagged using a globally unique identifier that can be resolved to its terminology through a free (anonymous) internet interface • Originating and cataloging organizations * The originating and cataloging organizations for each data element must be identified using globally unique identifiers that can be resolved to contact information through a free (anonymous) internet interface • Origination and cataloging date* The origination and cataloging date of each data element must be specified

17. Requirements of ISO 8000 Information quality at the level of the organization • Assessment of the level (grade) of information management capabilities

18. What can you do? • Insist on access to quality information • Participate in the development of Standard Identification Guides to define your data requirements • Promote alignment and interoperability among standards efforts by insisting on standard data labeling (internally and externally) • Encourage your data providers to prepare for ISO 8000

19. Data Quality

20. Questions?