Prйsentation PowerPoint

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Data Quality Framework andData Synchronisation
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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

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• 1. Why Data Quality?

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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

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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.

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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.

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• 2. What is Data Quality?

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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.

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Data Quality Principles
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
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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 (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

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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
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Sustainability in Time
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Why are internal processes importantThe Leaky
Pipes of Data Quality
Internal processes
Process
Internal
Constant data corrections and fixes
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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

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• 3. The Data Quality Framework version 2

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• 3.1 Background

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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

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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)

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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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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.

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Steering Committee Members

• Manufacturers
• Coca Cola Company
• Kraft Foods
• Procter Gamble
• Reckitt Benckiser
• SCA
• Unilever
• Retailers
• Ahold
• Carrefour
• Coles Group
• Metro
• Safeway
• WalMart
• 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

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GDSN Inc. Organisation Chart
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GDSN in GS1
Sally Herbert 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
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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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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
• 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

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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

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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

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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
• 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

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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

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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
http//www.gs1.org/productssolutions/gdsn/dqf/inde
x.html
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• 4. Reference Materials Resources

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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/star
  t.html

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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
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• 5. Final Thoughts

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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

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For more information www.gs1.org/dataquality data
qualityinfo_at_gs1.org
Gabriel Sobrino Data Quality Programme
Manager GS1 GDSN, Inc E gabriel.sobrino_at_gs1.org