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Data Quality Framework and Data Synchronisation. ©2008 GS1 2 Contents 1.Why Data Quality?Why Data Quality? 2.What is Data Quality?What is Data Quality?

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Presentation on theme: "Data Quality Framework and Data Synchronisation. ©2008 GS1 2 Contents 1.Why Data Quality?Why Data Quality? 2.What is Data Quality?What is Data Quality?"— Presentation transcript:

1 Data Quality Framework and Data Synchronisation

2 ©2008 GS1 2 Contents 1.Why Data Quality?Why Data Quality? 2.What is Data Quality?What is Data Quality? 3.The Data Quality Framework version 2The Data Quality Framework version Background 3.2. GovernanceGovernance 3.3. Content of the Data Quality FrameworkContent of the Data Quality Framework 4.Reference Materials & ResourcesReference Materials & Resources 5.Final ThoughtsFinal Thoughts

3 ©2008 GS Why Data Quality? Back to contents

4 ©2008 GS1 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 ©2008 GS1 5 Why Data Quality? (Contd) 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 ©2008 GS1 6 Why Data Quality? (Contd) 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.

7 ©2008 GS What is Data Quality? Back to contents

8 ©2008 GS1 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 ©2008 GS1 9 Las 5 dimensiones de la calidad de datos*: Completeness All the required values are electronically recorded *Source: GCI/CapGemini Report: Internal Data Alignment, May 2004 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 Data Quality Principles Manufacturer Retailer GDSN Product Information Recipient Systems Source Systems PIM/Publication Process PIM/Receiving Process

10 ©2008 GS1 10 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 ©2008 GS1 11 Pursuing Data Quality (Contd) 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 ©2008 GS1 12 Actions for Data Quality Sustainability in Time Cumulative cost Data Quality Management System Internal Data Alignment Product inspections Education and training Trading partners must collaborate and establish the right set of actions to guarantee quality through time.

13 ©2008 GS1 13 Why are internal processes important: The Leaky Pipes of Data Quality Process Constant data corrections and fixes Internal Internal processes

14 ©2008 GS1 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 ©2008 GS1 15 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 ©2008 GS1 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 ©2008 GS The Data Quality Framework version 2 Back to contents

18 ©2008 GS Background Back to contents

19 ©2008 GS1 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 ©2008 GS1 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 ©2008 GS1 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.

22 ©2008 GS Governance Back to contents

23 ©2008 GS1 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 ©2008 GS1 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 Wegmans 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 ©2008 GS1 25 GDSN Inc. Organisation Chart

26 ©2008 GS1 26 GDSN in GS1 Sally Herbert President, GDSN, Inc. Data Quality Protocol GPC Healthcare GDSN Alan Hyler Susie McIntosh-Hinson * GDSN Budget Gabriel Sobrino * GS1 DQ Budget Zoltan Patkai * GS1 GPC Budget Pete Alvarez * GS1 Healthcare Budget Michel van der Heijden President Healthcare

27 ©2008 GS1 27 GS1 (GDSN)– Data Quality Framework Manager Stewardship / Certification Oversight / Continuous Improvement

28 ©2008 GS Content of the Data Quality Framework Back to contents

29 ©2008 GS1 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 ©2008 GS1 30 Data Quality Framework Main sections: 1.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 2.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 3.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 ©2008 GS1 31 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 ©2008 GS1 32 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 ©2008 GS1 33 Data Quality Management Systems Requirements (Chapter 3 of the Framework) III Closing the circle: Internal audits Management review Continuous improvement

34 ©2008 GS1 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 ©2008 GS1 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 ©2008 GS1 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 ©2008 GS1 37 Self-assessment (Chapter 4 of the Framework) III The KPI model covers the following categories: 1.Overall item accuracy 2.Generic attribute accuracy 3.Dimension and weight accuracy 4.Hierarchy accuracy 5.Active/Orderable KPIs can be inspecting using the product inspection procedure (Chapter 6) Recommendation for benchmark goals on the KPIs

38 ©2008 GS1 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 ©2008 GS1 39 The Industry DQ Framework Elevator Pitch 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 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 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 For more information visit the link below:

40 ©2008 GS Reference Materials & Resources Back to contents

41 ©2008 GS1 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 ©2008 GS1 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 ©2008 GS Final Thoughts Back to contents

44 ©2008 GS1 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 ©2008 GS1 45 For more information: Gabriel Sobrino Data Quality Programme Manager GS1 GDSN, Inc E

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