DQF Case Study

1. Proctor & Gamble GS1 Mexico DQF Case Study

2. Agenda • Situation • Lessons Learned • Retailer Perspective • Next Steps

3. Situation – GS1 México • Data Quality in México • SECODAT measurement service begun in 2005 • 58% items tagged by SECODAT with quality flag • GDSN in México • 2,936 suppliers; 270,790 items • July 2011 - 8 major retailers started requiring a data quality validation from SECODAT on new item information in 3 categories (grocery, beauty and care, beverages).

4. Situation – GS1 México Options to obtain a Data Quality flag: • Data capture of each product using SECODAT, or • Self-Certification*: • Self-assess using the Data Quality Framework (DQF) and obtaining a minimum score of 80%, AND • Audit a sample of products inspected by SECODAT; achieving a 100% score on the sample. • Companies meeting these 2 requirements would be awarded a SECODAT Data Quality flag and their products need not be inspected. • No company had attempted to attain a DQ flag

5. Situation – P&G – Aug 2011 As a result of the July 2011 retail requirements and SECODAT validation process: • The GDSN process in México was in jeopardy for Procter & Gamble (P&G) • Several P&G product launch initiatives were stalled • P&G opted to pilot Self-Certification • Become the first self-certified manufacturer in México • Collaborate with GS1 México to pilot self-certification process • Share learning’s with the industry

6. Lessons Learned P&G • Self certification is hard work it takes time, resources and commitment from senior levels in an organization • Plant resources turn over – requiring continuous training • Self certification can be achieved with internal or 3rd party resources • Sample audits must address seasonality & out-of-stock • % accuracy targets must be set between individual suppliers and retailers • 100% is not realistic for all attributes

7. Wal-Mart Perspective Overall very positive meeting with Wal-Mart • Agreed to document learnings in a white paper and highlight GS1 México as an industry pilot for DQF globally. • Agreed that the industry committee will revisit the percentage of tolerance in 26 fields that today are validated by SECODAT • Agreed to revisit tolerance guidelines. Audit the data every 3 months during pilot and align on % target • Providing an opportunity to have greater collaboration and more realistic expectations between P&G and Wal-Mart

8. Lessons Learned GS1 México • Improving data quality is a long journey • It’s well understood, that quality information is required by trading partners to drive successful processes, but action is still gradual – this effort was spurred by a Retailers’ mandate • Clear communication is key between all the key stakeholders during the process • Companies interested in data quality may not have the dedicated resources for it; in the meanwhile they use GS1 México SECODAT services.

9. Key Messages • How data accuracy is improved is a strategic choice and can be achieved with internal resources or 3rd party measurement services. • Collaboration with retailers and GS1 is important so all parties are aligned on target improvement • The pilot will benefit the industry overall by paving the way for other suppliers to engage in their own data accuracy effort - more work needs to be done and learning's will continue

10. Next Steps • Joint white paper – GS1 México and P&G • GS1 México and the Global DQ leadership Industry Committee will review recommendations for a range of tolerances, at item, case, and pallet level in 26 fields validated by SECODAT • Leverage pilot as an industry example for global application of DQF • Identified opportunities to apply the DQF to model to address multiple situations as required by Suppliers

11. Questions?

12. Wrap Up

13. Data Quality Key Messages • Drive Incremental Value Proportional to Business Demand • Align the Data Governance programme with a key business initiative that will contribute to your strategic goals • Don't try to fix everything at once. Score quick wins along the way • Take a Standards Based Approach to Data Governance • Data standards are the cornerstone of an effective Data Governance programme • Balance centrally agreed standards vs. the need for a dynamic business with departmentally-driven goals • Applications come and go, but the data largely stays the same. The Data Governance decisions you make today will have a profound impact on your business • Take a Rigorous Approach to Organizational Alignment • Data Governance is not an IT-only initiative. It requires active involvement and leadership from the business as well as within the IT • Am Executive Sponsor must provide leadership and senior data stewards must accept accountability • Building an integrated and accurate enterprise view won't happen on its own – align initiatives across business units

14. Data Quality Guiding Principles • Achieve alignmentof information models with business models at the start • Enable people with the right skills to build and manage new information systems • Improve processes around information compliance, policies, practices • and measurement • Quantitativelyidentify data governance problems and resolve them • Perform root cause analysis that led to poor data governance • Remove complexity by ensuring all information is exchanged through standards • Increase automation for the exchange of information across systems

15. Data Quality Essential Elements DQSM • Governance: Top down sponsorship, adequate funding and business unit buy-in as unwavering mandate • Clarity of objectives: well defined end vision based on clear requirements analysis, and end-user agreement. • Business ownership: Business requirements as driver, IT as enabler • Strategic leader: Empowered and accountable for achieving EDM objectives • Balanced team: joint business and IT team members with sufficient staffing and knowledge about the data • Holistic business case & processes: covering enterprise wide interests and incorporating data quality, timeliness, linkages, and process improvements Recognize complexities: understand data and process dependencies associated with linking corporate, regional and local requirements across lines of business. Adhere to core policies/procedures: including data model consistency, business rules, and data quality stewardship. Applications adapt to the model not the other way around. Phased implementation: iterative, realistic and disciplined approach to defining project milestones. Phased migration with clear and incremental ROI for stakeholders. Testing, training, and internal marketing: The absence of a well defined change management process may contribute to project delays, administrative rework, or an inability to realize predicted outcomes.

16. Local Data Quality Process (LDQP)An Approach for an Assessment • Get Executive Buy-in • DQ Assessment • Look at Critical Business Processes • Internal Lens – run the business • External Lens – supporting customer POV • Identify Key Attributes when missing or incorrect will cause those critical business processes to fail. • Based on standards • Fix the critical stuff • Quick wins - Low hanging fruit /biggest bang for your buck, 80/20 • In house or external Third party • Synchronize the data • Information Governance Program (Long Term) • Policies, procedures, information lifecycle, organization (roles responsibilities)

17. Data Quality Website and Library http://www.gs1.org/gdsn/dqf • Website • Library http://www.gs1.org/gdsn/dqf/library • Data Quality Framework and support documentation • Case studies, white papers • Data Quality Program Internal Implementation Example • Data Quality Videos • Links to Related Technical Documents on standards

18. Contact Details Liz Crawford Director, Data Quality & GDSN Princeton Pike Corporate Center 1009 Lenox Drive, Suite 202 Lawrenceville, NJ 08648 T + 1 609 557 4245 liz.crawford@gs1.org W www.gs1.org