Data Quality Framework andData Synchronisation

©2008 GS12

Contents

1. Why Data Quality?

2. What is Data Quality?

3. The Data Quality Framework version 23.1. Background3.2. Governance3.3. Content of the Data Quality Framework

4. Reference Materials & Resources

5. Final Thoughts

©2008 GS13

1. Why Data Quality?

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©2008 GS14

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!

©2008 GS15

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.

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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.

©2008 GS17

2. What is Data Quality?

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©2008 GS18

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.

©2008 GS19

Las 5 dimensiones de la calidad de datos*:CompletenessCompleteness All the required values are electronically recordedAll the required values are electronically recorded

*Source: GCI/CapGemini Report: “Internal Data Alignment”, May 2004

Standards-basedStandards-based Data conforms to industry standardsData conforms to industry standards

ConsistencyConsistency Data values aligned across systemsData values aligned across systems

AccuracyAccuracy Data values are right, at the right timeData values are right, at the right time

Time-stampedTime-stamped Validity timeframe of data is clearValidity timeframe of data is clear

Data Quality Principles

Manufacturer

Retailer

GDSN

Pro

du

ct In

form

ati

on

Recipient Systems

Source Systems

PIM/Publication Process

PIM/Receiving Process

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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.

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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

©2008 GS112

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.

©2008 GS113

Why are internal processes important:The “Leaky Pipes” of Data Quality

2008 2009

Process

Constant data corrections and fixes

Internal

Internal processes

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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.

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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

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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

©2008 GS117

3. The Data Quality Framework version 2

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©2008 GS118

3.1 Background

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©2008 GS119

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

©2008 GS120

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)

©2008 GS121

Further developments …

• In 2006-2007 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

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©2008 GS123

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.

©2008 GS124

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

©2008 GS125

GDSN Inc. Organisation Chart

ArchitectureCommittee GDSN StaffGlobal Product

Classification (GPC)GDSN Users Group

President, GDSN, Inc.

GDSN Board of Directors

Project Teams

CEO GS1

Zoltan PatkaiGroup Manager

Advisory GroupSusie McIntosh-

HinsonSr. Director

Business Operations

Data Quality Protocol

Alan HylerDirector Program Mgmt

Gabriel SobrinoProgram Manager

Peter AlvarezSr. Director

GDSN Healthcare

TBD. Technical Operations

©2008 GS126

GDSN in GS1

Sally HerbertPresident, GDSN, Inc.

GDSN, Inc. Data QualityProtocol

GPCHealthcare

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

PresidentHealthcare

©2008 GS127

GS1 (GDSN)– Data Quality Framework ManagerStewardship / Certification Oversight / Continuous Improvement

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3.3 Content of the Data Quality Framework

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©2008 GS129

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

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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

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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

©2008 GS132

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

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Data Quality Management Systems Requirements (Chapter 3 of the Framework) III

Closing the circle:

• Internal audits

• Management review

• Continuous improvement

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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

©2008 GS135

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)

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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.

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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

©2008 GS138

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

©2008 GS139

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: http://www.gs1.org/productssolutions/gdsn/dqf/index.html

©2008 GS140

4. Reference Materials & Resources

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©2008 GS141

Getting Started with Data Quality

• Comprehensive compilation of information about data quality which helps organisations position their efforts and objectives around data quality.

• http://www.gs1.org/productssolutions/gdsn/dqf/start.html

©2008 GS142

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

http://www.gs1.org/productssolutions/gdsn/dqf/data_quality_framework.html

©2008 GS143

5. Final Thoughts

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©2008 GS144

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

©2008 GS145

For more information:www.gs1.org/dataqualitydataqualityinfo@gs1.org

Gabriel SobrinoData Quality Programme ManagerGS1 GDSN, IncE gabriel.sobrino@gs1.org