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Data Quality Framework and Data Synchronisation

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Presentation on theme: "Data Quality Framework and Data Synchronisation"— Presentation transcript:

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.

7 Back to contents 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
Back to contents 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.

22 Back to contents 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
Back to contents 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
Back to contents 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:
Gabriel Sobrino Data Quality Programme Manager GS1 GDSN, Inc E

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