© 2012 GS11

DQF Case Study

Proctor & Gamble

GS1 Mexico

© 2012 GS1

Agenda

• Situation• Lessons Learned• Retailer Perspective• Next Steps

© 2012 GS13

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).

© 2012 GS14

Situation – GS1 México

Options to obtain a Data Quality flag:

1. Data capture of each product using SECODAT, or

2. 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

© 2012 GS1

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

© 2012 GS1

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

© 2012 GS17

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

© 2012 GS18

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.

© 2012 GS1

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

© 2012 GS1

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

Questions?

© 2012 GS112

Wrap Up

© 2012 GS113

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

© 2012 GS1

Data Quality Guiding Principles

• Achieve alignment of 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• Quantitatively identify 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

© 2012 GS1

Data Quality Essential Elements DQSM

1. Governance: Top down sponsorship, adequate funding and business unit buy-in as unwavering mandate

2. Clarity of objectives: well defined end vision based on clear requirements analysis, and end-user agreement.

3. Business ownership: Business requirements as driver, IT as enabler

4. Strategic leader: Empowered and accountable for achieving EDM objectives

5. Balanced team: joint business and IT team members with sufficient staffing and knowledge about the data

6. Holistic business case & processes: covering enterprise wide interests and incorporating data quality, timeliness, linkages, and process improvements

7. Recognize complexities: understand data and process dependencies associated with linking corporate, regional and local requirements across lines of business.

8. 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.

9. Phased implementation: iterative, realistic and disciplined approach to defining project milestones. Phased migration with clear and incremental ROI for stakeholders.

10.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. 15

© 2012 GS116

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)

© 2012 GS1

Data Quality Website and Library

• Website

• Library

http://www.gs1.org/gdsn/dqf

• 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

http://www.gs1.org/gdsn/dqf/library

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

Contact Details