2The Data Quality Framework version 2 ContentsWhy Data Quality?What is Data Quality?The Data Quality Framework version 23.1. Background3.2. Governance3.3. Content of the Data Quality FrameworkReference Materials & ResourcesFinal Thoughts
4Why Data Quality?To realise the full potential of the GDSN, Trading Partners must ensure the following:Accurate product information is aligned across internal manufacturer systemsGood quality product information is synchronised through the GDSNProduct information within retailer systems is aligned with product information received via the GDSNThe industry must be able to trust the quality of data flowing through the GDSN!
5Why 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.
6Why 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.
8What 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 dataInformation recipients have responsibility to maintain accurate data within their systems and ensure its integrity in their processesTrading partners must work together in order to assure the right conditions exist for developing data quality initiatives.
9Data Quality Principles ManufacturerRetailerGDSNProduct InformationRecipient SystemsSource SystemsPIM/Publication ProcessPIM/Receiving ProcessLas 5 dimensiones de la calidad de datos*:CompletenessAll the required values are electronically recordedStandards-basedData conforms to industry standardsConsistencyData values aligned across systemsAccuracyData values are right, at the right timeTime-stampedValidity timeframe of data is clear*Source: GCI/CapGemini Report: “Internal Data Alignment”, May 2004
10Data quality must be sustainable throughout time! Pursuing Data QualityData 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.
11Pursuing 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 dataA central component to these effort is having internal processes that result in a consistently good quality data output
12Actions for Data Quality Trading partners must collaborate and establish the right set of actions to guarantee quality through time.+Product inspectionsCumulative costEducation and trainingData Quality Management SystemInternal Data Alignment--+Sustainability in Time
13Constant data corrections and fixes Why are internal processes important: The “Leaky Pipes” of Data QualityInternal processesInternalProcessConstant data corrections and fixes
14How 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.
15Data Quality Framework: Key DefinitionsData Quality:The desirable characteristics of data when published by trading partnersComplete, standards based, consistent, accurate and time stampedData Quality Framework:Best practices for the management of data quality systemsDepending on market needs, compliance can be demonstrated through:Self-declarationThird party certification based on inspection and auditing
16Key Definitions (Continued) Internal Data Alignment (IDA):Internal management of data across various business systems to achieve data qualityOne aspect of achieving data qualityMeasurement Services:External measurement service to help businesses publish accurate dimensional dataOffered by several GS1 Member Organisations and Data PoolsVoluntary or mandatory based on market agreement
173. The Data Quality Framework version 2 Back to contents3. The Data Quality Framework version 2
19An 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 issueHowever, the work groups encountered the risk of creating multiple solutionsAs 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
20Achievements of the Data Accuracy JBP Created Data Quality Framework, including:Data Quality Guiding PrinciplesData Quality Protocol (for industry review)Data Quality Management System (DQMS)Data Inspection ProcedureAligned with, or considered, other industry initiativesMeasurement Tolerances Data Accuracy GSMP Project TeamInternal Data Alignment (IDA) methodologiesAgreed an industry governance model and transition and hand-off to GS1 (GDSN)
21Further 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.
23Governance 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 FrameworkData Quality Steering Committee reports directly to GDSN BoardThe 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.
24Steering Committee Members Manufacturers:Coca Cola CompanyKraft FoodsProcter & GambleReckitt BenckiserSCAUnileverRetailers:AholdCarrefourColes GroupMetroSafewayWal*MartWegman’sAdvisors:European Brands AssociationFood Marketing InstituteGlobal Commerce InitiativeGrocery Manufacturers of AmericaPepsiCoGS1 Member Organisations:GS1 AustraliaGS1 MexicoGS1 NetherlandsGS1 UKGS1 US
26GDSN in GS1 Michel van der Sally Herbert Heijden President, President GDSN, Inc.Michel van derHeijdenPresidentHealthcareGDSN, Inc.Data QualityProtocolGPCHealthcareGDSNAlan HylerSusie McIntosh-Hinson* GDSN BudgetZoltan Patkai* GS1 GPC BudgetPete Alvarez* GS1 Healthcare BudgetGabriel Sobrino* GS1 DQ Budget
283.3 Content of the Data Quality Framework Back to contents3.3 Content of the Data Quality Framework
29Data Quality Framework Guiding Principles Based on user needsStrongly encouraged, yet voluntaryCan adapt to the needs and requirements of specific trading partner relationshipComprehensive, yet flexibleCan be included in any kind of quality management systemMinimises implementation costs – enabling benefitsComplementary to GS1 System standardsOpen to certification and self-declaration
30Data Quality Framework Main sections:Data Quality Management Systems (DQMS) Requirements, including chapters on:Self-declarationCertificationA management system like ISO 9000, aimed at the proper management of dataSelf-assessment procedureProcedure to execute a self-assessmentQuestionnaire to assess conformity to DQMS requirementsKPI Model to validate actual accuracy of the dataData Inspection ProcedureA procedure for the physical inspection of products and dataStand alone, orPart of a Data Quality Management Systems audit
31Best practice procedures regarding how to manage data Data Quality Management Systems Requirements (Chapter 3 of the Framework)Best practice procedures regarding how to manage dataEstablishing a Data Management PolicySetting objectivesDefining responsibilitiesProviding resourcesEstablishing the work processesEstablishing a database infrastructureEstablishing an IT infrastructureInternal communications
32Operational controls: Data Quality Management Systems Requirements (Chapter 3 of the Framework) IIOperational controls:Data generation and verificationProduct measurementData inputData publishingMeasuring and monitoringProcessing user feedbackEstablishing preventive actionEstablishing corrective action
33Data Quality Management Systems Requirements (Chapter 3 of the Framework) III Closing the circle:Internal auditsManagement reviewContinuous improvement
34Compliance Assessment Conformity with the Framework can be proven through:Self-declaration (Chapter 4)Chapter 4 provides guidance for organisations undertaking an assessmentThird party auditing (Chapter 5)Chapter 5 provides requirements for the third party auditors
35Self-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)
36Self-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.
37Self-assessment (Chapter 4 of the Framework) III The KPI model covers the following categories:Overall item accuracyGeneric attribute accuracyDimension and weight accuracyHierarchy accuracyActive/OrderableKPIs can be inspecting using the product inspection procedure (Chapter 6)Recommendation for ‘benchmark’ goals on the KPIs
38Inspection procedure (Chapter 6 of the Framework) Comparison of a sample size of actual product against related dataLimited to 15 key attributesProcedure prescribes best practices for sample size, measurement methodology and result analysisKPI Model used to monitor progress and upgrades on the accuracyProcedure(s) can be used to be used:InternallyBy Third partyAs part of an audit or as a best practice
39The 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 nowWhat is it?A process for improving data quality within your businessWho manages it?GS1 (GDSN) manages the Framework for the industryWhy do I need to use it?Because inaccurate, unreliable data is costing you and your trading partners moneyWhat is the role of the GS1 Member Organisation?Educate and support the trading partnersFor more information visit the link below:
404. Reference Materials & Resources Back to contents4. Reference Materials & Resources
41Getting Started with Data Quality Comprehensive compilation of information about data quality which helps organisations position their efforts and objectives around data quality.
42GDSN Data Quality Web Site Resources Data Quality Framework and support documentationFrequently Asked Questions (FAQs)Data Quality Implementation GuideData Quality Program Internal Implementation ExampleDQ Framework Background PresentationData Quality VideosLinks to Related Technical DocumentsMeasurement Tolerances StandardPackage Measurement Rules for Data AlignmentGDSN Standards DocumentsGPC
44Critical Success Factors Consistent interpretation and implementation across Member Organisations (SME community)Education and awareness in key data pools supporting major retailers and manufacturersContinued industry awareness and focus on data quality as part of GDSConstant communication between trading partnersParticipation and involvement of middle-management and operational levelsMaking data quality assurance part of daily activities
45For more information: www.gs1.org/dataquality email@example.com Gabriel SobrinoData Quality Programme ManagerGS1 GDSN, IncE