1. Data Quality Framework and Data Synchronisation

2. The Data Quality Framework version 2
Contents
Why Data Quality?
What is Data Quality?
The Data Quality Framework version 2
3.1. Background
3.2. Governance
3.3. Content of the Data Quality Framework
Reference Materials & Resources
Final Thoughts

3. Back to contents
1. Why Data Quality?

4. Why Data Quality?
To realise the full potential of the GDSN, Trading Partners must ensure the following:
Accurate product information is aligned across internal manufacturer systems
Good quality product information is synchronised through the GDSN
Product information within retailer systems is aligned with product information received via the GDSN
The industry must be able to trust the quality of data flowing through the GDSN!

5. Why Data Quality? (Cont’d)
Without reliable data in the Network, trading partners are forced to set up additional means to control data quality, resulting in a longer, more complicated ‘road’ for the information.

6. Why Data Quality? (Cont’d)
The impact of bad data is highlighted on data synchronisation processes, but has consequences for all the processes in the supply chain!
Benefits obtained by doing data synchronisation will be nullified if data is erroneous and trading partners are forced to correct it.
The impact of bad data is multiplied when considering the cost of initially creating the (bad) data, plus the cost of correcting it and compensating for the problems it caused.

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2. What is Data Quality?

8. What is Data Quality?
In order to achieve objectives on data quality, trading partners must agree on a clear vision of what can be considered ‘good quality’ data.
Additionally, data quality is the shared responsibility of manufacturers and retailers:
Information providers are the source of the product data and so are the starting point for needed improvements in process for creating good data
Information recipients have responsibility to maintain accurate data within their systems and ensure its integrity in their processes
Trading partners must work together in order to assure the right conditions exist for developing data quality initiatives.

9. Data Quality Principles
Manufacturer
Retailer
GDSN
Product Information
Recipient Systems
Source Systems
PIM/Publication Process
PIM/Receiving Process
Las 5 dimensiones de la calidad de datos*:
Completeness
All the required values are electronically recorded
Standards-based
Data conforms to industry standards
Consistency
Data values aligned across systems
Accuracy
Data values are right, at the right time
Time-stamped
Validity timeframe of data is clear
*Source: GCI/CapGemini Report: “Internal Data Alignment”, May 2004

10. Data quality must be sustainable throughout time!
Pursuing Data Quality
Data quality must be sustainable throughout time!
Short-term ‘remedies’ for data quality may yield some quick results, but maintaining them through time is an resource-exhaustive activity and still will not provide the desire data quality objectives.

11. Pursuing Data Quality (Cont’d)
In order to have a sustainable approach for data quality, trading partners must become engaged in several actions that complement one another and help to maintain quality on the data
A central component to these effort is having internal processes that result in a consistently good quality data output

12. Actions for Data Quality
Trading partners must collaborate and establish the right set of actions to guarantee quality through time. +
Product inspections
Cumulative cost
Education and training
Data Quality Management System
Internal Data Alignment - - +
Sustainability in Time

13. Constant data corrections and fixes
Why are internal processes important: The “Leaky Pipes” of Data Quality
Internal processes
Internal
Process
Constant data corrections and fixes

14. How to get there?
The Industry has realised that in order to achieve sustainable data quality, internal processes must be shaped to build a sustainable cycle.
This realisation led to several key Industry organisations to collaborate on the development of a unified approach and solution to data quality.
This resulted on the Data Quality Framework which is now under the stewardship of GS1.

15. Data Quality Framework:
Key Definitions
Data Quality:
The desirable characteristics of data when published by trading partners
Complete, standards based, consistent, accurate and time stamped
Data Quality Framework:
Best practices for the management of data quality systems
Depending on market needs, compliance can be demonstrated through:
Self-declaration
Third party certification based on inspection and auditing

16. Key Definitions (Continued)
Internal Data Alignment (IDA):
Internal management of data across various business systems to achieve data quality
One aspect of achieving data quality
Measurement Services:
External measurement service to help businesses publish accurate dimensional data
Offered by several GS1 Member Organisations and Data Pools
Voluntary or mandatory based on market agreement

17. 3. The Data Quality Framework version 2
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3. The Data Quality Framework version 2

18. Back to contents
3.1 Background

19. An Industry Call to Action …
In late 2004 / early 2005, a number of different industry and country-specific work groups were independently formed to address the data quality issue
However, the work groups encountered the risk of creating multiple solutions
As a result, in April 2005, the GCI Executive Board recommended the creation of a Joint Business Planning Data Accuracy Task Force
… with the charter to develop a framework for a global data quality solution

20. Achievements of the Data Accuracy JBP
Created Data Quality Framework, including:
Data Quality Guiding Principles
Data Quality Protocol (for industry review)
Data Quality Management System (DQMS)
Data Inspection Procedure
Aligned with, or considered, other industry initiatives
Measurement Tolerances Data Accuracy GSMP Project Team
Internal Data Alignment (IDA) methodologies
Agreed an industry governance model and transition and hand-off to GS1 (GDSN)

21. Further developments …
In GS1 collaborated with AIM and Capgemini to develop a self-assessment module which would allow organisations to conduct assessments of their compliance with the Data Quality Framework.
Within that work, a KPI model was also developed as a means to monitor the actual accuracy of data and validate the effectiveness of internal processes for data quality.
A new version of the Framework was then produced including the self-assessment module and the KPI model.
This new version was approved by the Steering Committee on January 2008.

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3.1 Governance

23. Governance and Management
Upon being entrusted with the stewardship on the document, GS1 (under GDSN) created the Data Quality Steering Committee as the group responsible to manage and maintain the Data Quality Framework
Data Quality Steering Committee reports directly to GDSN Board
The Data Quality Steering Committee has established a sub-group called the ‘Data Quality Adoption Group’ and has commissioned it with the task to further develop education, communication and tools to support the adoption of data quality and the Data Quality Framework.

24. Steering Committee Members
Manufacturers:
Coca Cola Company
Kraft Foods
Procter & Gamble
Reckitt Benckiser
SCA
Unilever
Retailers:
Ahold
Carrefour
Coles Group
Metro
Safeway
Wal*Mart
Wegman’s
Advisors:
European Brands Association
Food Marketing Institute
Global Commerce Initiative
Grocery Manufacturers of America
PepsiCo
GS1 Member Organisations:
GS1 Australia
GS1 Mexico
GS1 Netherlands
GS1 UK
GS1 US

25. GDSN Inc. Organisation Chart
Architecture
Committee
GDSN Staff
Global Product
Classification (
GPC )
GDSN Users Group
President ,
GDSN
Inc .
GDSN Board of
Directors
Project Teams
CEO GS 1
Zoltan Patkai
Group Manager
Advisory Group
Susie McIntosh -
Hinson Sr
Director
Business Operations
Data Quality
Protocol
Alan Hyler
Director Program Mgmt
Gabriel Sobrino
Program Manager
Peter Alvarez
GDSN Healthcare
TBD
Technical Operations

26. GDSN in GS1 Michel van der Sally Herbert Heijden President, President
GDSN, Inc.
Michel van der
Heijden
President
Healthcare
GDSN, Inc.
Data Quality
Protocol
GPC
Healthcare
GDSN
Alan Hyler
Susie McIntosh-Hinson
* GDSN Budget
Zoltan Patkai
* GS1 GPC Budget
Pete Alvarez
* GS1 Healthcare Budget
Gabriel Sobrino
* GS1 DQ Budget

27. GS1 (GDSN)– Data Quality Framework Manager Stewardship / Certification Oversight / Continuous Improvement

28. 3.3 Content of the Data Quality Framework
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3.3 Content of the Data Quality Framework

29. Data Quality Framework Guiding Principles
Based on user needs
Strongly encouraged, yet voluntary
Can adapt to the needs and requirements of specific trading partner relationship
Comprehensive, yet flexible
Can be included in any kind of quality management system
Minimises implementation costs – enabling benefits
Complementary to GS1 System standards
Open to certification and self-declaration

30. Data Quality Framework
Main sections:
Data Quality Management Systems (DQMS) Requirements, including chapters on:
Self-declaration
Certification
A management system like ISO 9000, aimed at the proper management of data
Self-assessment procedure
Procedure to execute a self-assessment
Questionnaire to assess conformity to DQMS requirements
KPI Model to validate actual accuracy of the data
Data Inspection Procedure
A procedure for the physical inspection of products and data
Stand alone, or
Part of a Data Quality Management Systems audit

31. Best practice procedures regarding how to manage data
Data Quality Management Systems Requirements (Chapter 3 of the Framework)
Best practice procedures regarding how to manage data
Establishing a Data Management Policy
Setting objectives
Defining responsibilities
Providing resources
Establishing the work processes
Establishing a database infrastructure
Establishing an IT infrastructure
Internal communications

32. Operational controls:
Data Quality Management Systems Requirements (Chapter 3 of the Framework) II
Operational controls:
Data generation and verification
Product measurement
Data input
Data publishing
Measuring and monitoring
Processing user feedback
Establishing preventive action
Establishing corrective action

33. Data Quality Management Systems Requirements (Chapter 3 of the Framework) III
Closing the circle:
Internal audits
Management review
Continuous improvement

34. Compliance Assessment
Conformity with the Framework can be proven through:
Self-declaration (Chapter 4)
Chapter 4 provides guidance for organisations undertaking an assessment
Third party auditing (Chapter 5)
Chapter 5 provides requirements for the third party auditors

35. Self-assessment (Chapter 4 of the Framework) I
Chapter 4 contains a procedure that organisations can use to assess their compliance against the Framework (requirements from Chapter 3).
Self-assessment procedure may be performed in isolation or with assistance to record results.
Organisations may define the scope of the assessment (processes included, goal and timeframe)

36. Self-assessment (Chapter 4 of the Framework) II
Self-assessment questionnaire consists of a total of 74 questions that assess conformity with the requirements on Chapter 3.
Questions are divided in basic questions (34) and general questions (40). An organisation willing to self-declare must score at least a total score of 80% and fulfil all the basic questions.
The results of a successful self-assessment must be validated by high marks on the KPI model.
Organisations may wish to assess individual processes in order to identify opportunities for improvement.

37. Self-assessment (Chapter 4 of the Framework) III
The KPI model covers the following categories:
Overall item accuracy
Generic attribute accuracy
Dimension and weight accuracy
Hierarchy accuracy
Active/Orderable
KPIs can be inspecting using the product inspection procedure (Chapter 6)
Recommendation for ‘benchmark’ goals on the KPIs

38. Inspection procedure (Chapter 6 of the Framework)
Comparison of a sample size of actual product against related data
Limited to 15 key attributes
Procedure prescribes best practices for sample size, measurement methodology and result analysis
KPI Model used to monitor progress and upgrades on the accuracy
Procedure(s) can be used to be used:
Internally
By Third party
As part of an audit or as a best practice

39. The Industry “DQ Framework” Elevator Pitch
Rationale & Benefits:
Without good, accurate data, Global Data Synchronisation will only enable the rapid, seamless transfer of bad data!
Data Quality is achievable & many companies are reaping benefits now
What is it?
A process for improving data quality within your business
Who manages it?
GS1 (GDSN) manages the Framework for the industry
Why do I need to use it?
Because inaccurate, unreliable data is costing you and your trading partners money
What is the role of the GS1 Member Organisation?
Educate and support the trading partners
For more information visit the link below:

40. 4. Reference Materials & Resources
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4. Reference Materials & Resources

41. Getting Started with Data Quality
Comprehensive compilation of information about data quality which helps organisations position their efforts and objectives around data quality.

42. GDSN Data Quality Web Site Resources
Data Quality Framework and support documentation
Frequently Asked Questions (FAQs)
Data Quality Implementation Guide
Data Quality Program Internal Implementation Example
DQ Framework Background Presentation
Data Quality Videos
Links to Related Technical Documents
Measurement Tolerances Standard
Package Measurement Rules for Data Alignment
GDSN Standards Documents
GPC

43. Back to contents
5. Final Thoughts

44. Critical Success Factors
Consistent interpretation and implementation across Member Organisations (SME community)
Education and awareness in key data pools supporting major retailers and manufacturers
Continued industry awareness and focus on data quality as part of GDS
Constant communication between trading partners
Participation and involvement of middle-management and operational levels
Making data quality assurance part of daily activities

45. For more information: www.gs1.org/dataquality dataqualityinfo@gs1.org
Gabriel Sobrino
Data Quality Programme Manager
GS1 GDSN, Inc E