Proctor & Gamble
GS1 Mexico
DQF Case Study
© 2012 GS1
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Agenda
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© 2012 GS1
Situation
Lessons Learned
Retailer Perspective
Next Steps
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).
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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
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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
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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 GS1
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
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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.
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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
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Questions?
Wrap Up
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Data Quality
Key Messages
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Drive Incremental Value Proportional to Business Demand
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Take a Standards Based Approach to Data Governance
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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
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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
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
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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 buyin as unwavering mandate
2. Clarity of objectives: well defined end
vision based on clear requirements
analysis, and end-user agreement.
7. Recognize complexities: understand data
and process dependencies associated with
linking corporate, regional and local
requirements across lines of business.
3. Business ownership: Business
requirements as driver, IT as enabler
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.
4. Strategic leader: Empowered and
accountable for achieving EDM
objectives
9. Phased implementation: iterative, realistic
and disciplined approach to defining project
milestones. Phased migration with clear
and incremental ROI for stakeholders.
5. Balanced team: joint business and IT
team members with sufficient staffing and
knowledge about the data
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.
6. Holistic business case & processes:
covering enterprise wide interests and
incorporating data quality, timeliness,
linkages, and process improvements
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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)
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Data Quality Website and Library
• Website
http://www.gs1.org/gdsn/dqf
• Library
http://www.gs1.org/gdsn/dqf/library
© 2012 GS1
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Data Quality Framework and support
documentation
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Case studies, white papers
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Data Quality Program Internal
Implementation Example
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Data Quality Videos
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Links to Related Technical Documents
on standards
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
[email protected]
W www.gs1.org