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©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.
©2008 GS16
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
©2008 GS110
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.
©2008 GS111
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
©2008 GS114
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.
©2008 GS115
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
©2008 GS116
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.
©2008 GS122
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
©2008 GS128
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
©2008 GS130
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
©2008 GS131
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
©2008 GS133
Data Quality Management Systems Requirements (Chapter 3 of the Framework) III
Closing the circle:
• Internal audits
• Management review
• Continuous improvement
©2008 GS134
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)
©2008 GS136
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.
©2008 GS137
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/[email protected]
Gabriel SobrinoData Quality Programme ManagerGS1 GDSN, IncE [email protected]