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Data Quality Framework Including the Data Quality Protocol

January 2008 Version 2 All contents copyright © GS1 2008 of 66

Data Quality Framework Including the Data Quality Protocol Version 2, January 2008


Document Summary Document Item Current Value

Document Title Data Quality Framework Including the Data Quality Protocol

Date Last Modified January 2008

Current Document Issue Issue 2

Status Approved

Document Description

Contributors This framework has been written with the input from many retailers, manufacturers, industry associations, certification bodies and others. The Joint Business Planning Group wishes to thank the following people for providing support, guidance and invaluable input to this report:

■ Bud Babcock, The Procter & Gamble Company

■ Nigel Bagley, Unilever

■ Paul Bokdam, Lloyd’s Register Quality Assurance

■ Hugo Byrnes, HBA Consultants

■ Hein Gorter de Vries, GS1 the Netherlands

■ Chris Havenga, GS1 South Africa

■ Sally Herbert, GDSN Inc. (for GS1)

■ Peter Irish, SCA

■ Sharon Jeske, CIES – The Food Business Forum

■ Peter Jordan, Kraft Foods

■ Kathleen van Maele, SCA

■ Thierry Morizur, Carrefour

■ Paul Povey, The Procter & Gamble Company

■ Abdul Razak, Campbell Soup Company

■ Katrin Recke, European Brands Association (AIM) /ECR Europe

■ Sabine Ritter, Global Commerce Initiative (GCI)

■ Alistair Robinson, Tesco

■ Alan Sargeant, The Procter & Gamble Company

■ Adrian Segens, GS1 UK

■ Pam Stegeman, Grocery Manufacturers Association of America (GMA)

■ Marianne Timmons, Wegmans Food Markets

■ Milan Turk Jr., The Procter & Gamble Company

■ Lionel Tussau, Georgia Pacific

■ Pat Walsh, Food Marketing Institute (FMI)



In particular we want to thank Lloyd’s Register Quality Assurance and Hugo Byrnes Associates for their contribution.

Simon Glass Ruud van der Pluijm Sue Mackesey

The Procter & Gamble Company Royal Ahold Kraft Foods

Co-Chairmen of the JBP Data Accuracy Group

Notes on version 2 The objective of this new version of the Data Quality Framework incl. the Data Quality Protocol is to enable companies to take a practical approach on the implementation of a Data Quality management System (DQMS).

Version 2 incorporates the self-assessment and self-declaration section developed by AIM, Capgemini and GS1.

Additionally, a KPI model for master data is now included as a way to effectively assess the quality of the information. The KPI model also provides validation to a proper implementation of a Data Quality Management System.

The Data Quality Steering Committee wishes to thank all the organisations and people that participated on the development of the self-declaration module as well as in the revision and creation of version 2 of the Data Quality Framework for their valuable contributions, leadership and insight:

■ Kraig Adams, The Coca-Cola Company

■ Bud Babcock, The Procter and Gamble Company

■ Nigel Bagley, Unilever

■ Vincent Bergere, Kraft Foods

■ Edwin Boer, Capgemini

■ Mauricio Breña, GS1 Mexico

■ Greg Buckley, Pepsico

■ Hugo Byrnes, Royal Ahold

■ Debbie Edmondson, The Coca-Cola Company

■ André Frank, Sara Lee

■ Britt Galbreath, SCA

■ Glenn Griglack, Reckitt Benckiser

■ Dave Grissom, The Coca-Cola Company

■ Hein Gorter de Vries, GS1 Netherlands

■ Bruce Hawkins, Wal*Mart

■ Sally Herbert, GS1 GDSN, Inc.

■ Rob Hoffman, The Hershey Company

■ Jeanne Iglesias, The Grocery Manufacturers Association (GMA/FPA)

■ Kees Jacobs, Capgemini

■ Mats Johansson, SCA

■ Richard Jones, GS1 Australia

■ Urs-Ulrich Katzenstein, Metro Group Buying



■ Detlef Konig, Masterfoods

■ Sue Mackesey, Kraft Foods

■ James Martin, Kimberly-Clark

■ Sandy Matayka, Kraft Foods

■ Terry Mochar, Reckitt Benckiser

■ Susan Moore, Safeway

■ Olivier Mouton, Carrefour

■ Jeff Mumford, Reckitt Benckiser

■ Doug Naal, Kraft Foods

■ Pamela Stegeman, The Grocery Manufacturers Association (GMA/FPA)

■ Brad Papietro, Wegmans Food Markets

■ Ruud van der Pluijm, Royal Ahold

■ Petra Potma, Sara Lee

■ Katrin Recke, The European Brands Association (AIM)

■ Sabine Ritter, Global Commerce Initiative (GCI)

■ Gabriel Sobrino, GS1 GDSN, Inc.

■ Jim Tersteeg, Capgemini

■ Marianne Timmons, Wegmans Food Markets

■ Peter Tinnemans, Capgemini

■ Patrick Walsh, Food Marketing Institute (FMI)

■ Tom Warren, Kraft Foods

■ Greg White, The Procter & Gamble Company

■ Mary Wilson, GS1 US

■ Gert van Zanten, Kimberley-Clark

Log of Changes in Version 2 Issue No. Date of Change Changed By Summary of Change

Disclaimer Whilst every effort has been made to ensure that the guidelines to use the GS1 standards contained in the document are correct, GS1 and any other party involved in the creation of the document HEREBY STATE that the document is provided without warranty, either expressed or implied, of accuracy or fitness for purpose, AND HEREBY DISCLAIM any liability, direct or indirect, for damages or loss relating to the use of the document. The document may be modified, subject to developments in technology, changes to the standards, or new legal requirements. Several products and company names mentioned herein may be trademarks and/or registered trademarks of their respective companies.



Table of Contents 1. Data Quality Framework – Executive Summary ........................................................................7

1.1. Background ........................................................................................................................................ 7 1.2. The Benefits of Good Quality Data .................................................................................................... 7 1.3. Data Quality Framework .................................................................................................................... 7

1.3.1. Data Quality Protocol ............................................................................................................... 7 1.3.2. Governance Model ................................................................................................................... 8

2. Guiding Principles......................................................................................................................10

3. Data Quality Management System Requirements (DQMSR) ..................................................11 3.1. Scope ............................................................................................................................................... 11 3.2. Data Quality Management System .................................................................................................. 12

3.2.1. General requirements............................................................................................................. 12 3.2.2. Data quality management policy ............................................................................................ 12 3.2.3. Planning.................................................................................................................................. 13 3.2.4. Implementation and operation................................................................................................ 14 3.2.5. Measuring and monitoring...................................................................................................... 16 3.2.6. Management review of system performance ......................................................................... 17

4. Self-Assessment and Self-Declaration.....................................................................................18 4.1. Introduction....................................................................................................................................... 18

4.1.1. Background ............................................................................................................................ 18 4.1.2. Purpose of the self-assessment and self-declaration ............................................................ 18 4.1.3. Main elements of self-assessment and self-declaration ........................................................ 19

4.2. Self-assessment and self-declaration guideline............................................................................... 19 4.3. Master data quality KPIs .................................................................................................................. 24

4.3.1. Master data quality KPIs definition......................................................................................... 25 4.3.2. Scope ..................................................................................................................................... 26 4.3.3. Inspect and measure guide on data quality KPIs................................................................... 26

4.4. Self-assessment questionnaire........................................................................................................ 28 4.4.1. How to use the self-assessment questionnaire...................................................................... 28 4.4.2. Preliminary questions on scope self-assessment .................................................................. 28 4.4.3. Scoring model ........................................................................................................................ 29

5. DQMSR Certification System ....................................................................................................30 5.1. Introduction....................................................................................................................................... 30 5.2. Organisation of the certification body auditing against DQMSR...................................................... 30

5.2.1. Work area of the certification body......................................................................................... 30 5.2.2. Organisation of the certification body..................................................................................... 30 5.2.3. Competence of the personnel ................................................................................................ 31 5.2.4. Other organisational facilities and procedures ....................................................................... 31

5.3. Document review, implementation review and certificate................................................................ 32



5.3.1. The organisation..................................................................................................................... 32 5.3.2. Certification audit.................................................................................................................... 32 5.3.3. Observation, improvement note and non-conformance......................................................... 33 5.3.4. Report..................................................................................................................................... 33 5.3.5. Time allocation ....................................................................................................................... 34 5.3.6. Certificate ............................................................................................................................... 34 5.3.7. Validity and renewal ............................................................................................................... 34

6. Data Inspection Procedure........................................................................................................34 6.1. Introduction....................................................................................................................................... 34 6.2. Inspection body selection................................................................................................................. 35 6.3. Inspection body staff qualification and experience .......................................................................... 35 6.4. Scope of inspection.......................................................................................................................... 35 6.5. Inspection preparation...................................................................................................................... 36 6.6. Inspection planning .......................................................................................................................... 36 6.7. Sample identification ........................................................................................................................ 36 6.8. Measuring equipment....................................................................................................................... 37 6.9. Inspection......................................................................................................................................... 37 6.10. Inspection reporting.......................................................................................................................... 37 6.11. Distribution of report......................................................................................................................... 38 6.12. Appeals procedure ........................................................................................................................... 38 6.13. Complaints ....................................................................................................................................... 38 6.14. Corrective measures ........................................................................................................................ 38

7. Definitions...................................................................................................................................38

8. Reference Documents................................................................................................................40

A. Annex 1: The self-assessment questionnaire .........................................................................41 A.1 Planning ........................................................................................................................................... 41 A.2 Implementation and operation.......................................................................................................... 45 A.3 Measuring and monitoring................................................................................................................ 50 A.4 Management review of system performance ................................................................................... 52

B. Annex 2: Scoring model for the self-assessment questionnaire...........................................55

C. Annex 3: Sampling .....................................................................................................................59

D. Annex 4 : Pre-inspection documentation requirements.........................................................60

E. Annex 5: Inspection report requirements ................................................................................61

F. Annex 6: List of GDSN ATTRIBUTES to be included in data certification ............................62

G. Annex 7: Guidelines on KPIs targets for the Industry ..........................................................666



1. Data Quality Framework – Executive Summary

1.1. Background In July 2005 a Joint Business Planning (JBP) team was assembled to address the issue of data quality within the global supply chain. The JBP team was comprised of suppliers, retailers and representatives from the following industry associations: AIM, CIES, ECR Europe, FMI, GCI, GMA and GS1.

The suppliers represented on the JBP team were Unilever, P&G, Kraft, SCA, Campbell’s and General Mills. The retailers represented on the team were Ahold, Carrefour, Tesco and Wegman’s.

The team’s goal was to develop a best practice framework for a global data quality solution. A set of guiding principles articulates key principles adhered to by the JBP Task Force in developing the data quality framework.

The challenge for the JBP team (and the industry at large) is to develop a framework that will meet both current and future data quality needs. Therefore the JBP team requests those who review the JBP materials to be mindful of both current opportunities, and data quality considerations that will drive future collaborative commerce.

1.2. The Benefits of Good Quality Data Good quality data is foundational to collaborative commerce. Good quality data means that all master data is complete, consistent, accurate, time stamped and industry standards based. By improving the quality of data within the end-to-end global supply chain, trading partners will reduce costs, improve productivity and accelerate product speed to market.

Good data quality will improve internal business processes for manufacturers, retailers, wholesalers, intermediaries and other third parties. For example, more accurate information on product weights and dimensions will contribute to better freight utilisation, eliminate the need for multiple measurement of the same product along the supply chain, and reduce the number of resources required to re-work planogrammes.

Suppliers of data have a responsibility to timely synchronise complete, consistent, accurate, available, time stamped and standard compliant data to their customers. The JBP Data Accuracy team recommends data synchronisation through the industry best practice Global Data

Synchronisation Network (GDSN). In return, recipients of data must have the internal processes and procedures in place to protect the integrity of data they receive via the GDSN. For example, Purchase Order data sent to a supplier should be consistent with data received via the GDSN.

1.3. Data Quality Framework

1.3.1. Data Quality Protocol Central to the Data Quality Framework is the Data Quality Protocol. The protocol has two components:

■ A data quality management system to validate the existence and effectiveness of key data management business processes

■ An inspection procedure to physically validate product attributes.



The objective of the data quality management system is to provide guidance for organisations to establish, implement, maintain and improve a data quality management system. The JBP team views the data quality management system as critical to the medium to longer-term vision for consistent high quality data to flow through the global supply chain. This system will focus on the existence of internal business processes, procedures and common performance criteria.

The objective of the inspection procedure is to define a standardised approach for data inspection. Existing GS1 standards are referenced in the inspection procedure, such as the GS1 GDSN Package Measurement Rules. The procedure will evolve with GS1 standards, and as an example, will include tolerances for different product categories as defined by the Global Standards Management Process (GSMP). Where standards do not currently exist the procedure provides best practice guidelines – such as a list of common attributes for inspection and sample size recommendations.

It is also the objective for the inspection procedure on the medium to longer-term implementation basis to merge into the data quality management system.

The JBP team recognises the inspection procedure must be flexible and implemented based on the requirements of a given trading partner relationship. For example, depending on how advanced a supplier and/or retailer are in their data quality journey, an initial requirement could be to inspect only a subset of the attributes included in the inspection procedure.

The JBP team recommends the widespread usage of the Data Quality Protocol, especially for companies in the GDSN community. However, the JBP team also recognises that compliance to and usage of the protocol is voluntary.

1.3.2. Governance Model GS1, owning the Protocol, provides overall stewardship of the Data Quality Framework and GS1 GDSN Inc. manages it. GS1 GDSN Inc. will promote it within the industry, has set up a Steering Committee and will implement a continuous improvement process for the Framework.

The Data Quality Framework is based on an open system whereby any accredited business entity may offer product inspection, and/or data quality management certification with reference to the Data Quality Protocol.

GS1 GDSN Inc. will employ a neutral accreditation authority to accredit certification bodies (for trading partner relationships that require certification).

The model (see figure 1) is based on the premise that trading partners should choose the data quality approach that best meets the needs of their trading partner relationship, for example, engage with an accredited service provider to obtain certification or engage with a non-accredited service provider to record compliance to the protocol. Widespread usage of the protocol is actively encouraged (as improved data quality will result from compliance to the protocol), although certification cannot be provided by non-accredited organisations. Alternatively self-declaration option will be available for companies who have the internal capabilities to demonstrate compliance with the protocol.

GS1 will further define how their organisation will support the overall stewardship and management of the Data Quality Framework, including programmes for the implementation of the Framework through self-assessment and accreditation/certification procedures.

It is also accepted that future evolution of the Framework will adhere to the guiding principles developed by the JBP team.

The seven industry associations represented on the JBP Data Accuracy team have facilitated an industry review period with their member companies. The JBP Data Accuracy team is now formally disbanded and has handed responsibilities over to GS1 GDSN Inc.



GS1: Overall Stewardship over the Framework

Data Quality Steering Committee

Trading Partners (User Companies)

Certification or self-declaration of compliance to the Framework

Accreditation Body

Accredited Certification

Body (service

provider)

Non-accredited

service providers

Certification can not be issued

Recording compliance

through a non-accredited

service provider Compliance through self-declaration Compliance through

certification by an accredited third party



2. Guiding Principles The JBP Data Accuracy charter is to develop a best practice data quality framework for data synchronised within the global supply chain.

The Global Data Dictionary (GDD) attributes provide the starting point for the framework. It is expected that the framework will evolve to include more data attributes and business information as exchanged between trading partners, with the evolvement of the GDD.

The JBP Data Accuracy team believes the full potential of the Global Data Synchronisation Network (GDSN) will not be realised until trading partners enable the following:

■ Good quality 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 the product information received via the GDSN

In developing the Data Quality framework, the following guiding principles have been adhered to. The data quality framework:

1. Is based on user needs (e.g., suppliers and recipients of data)

2. Is strongly encouraged within the Global Data Synchronisation Network community, yet is voluntary

3. Is implemented based upon requirements of a given trading partner relationship

4. Is comprehensive in its structure and potential implementation, yet provides for flexible implementation, as required by the trading partners

5. Minimises implementation, management and other additional costs to the global supply chain, and enables readily quantifiable benefits to all supply chain partners

6. Is complementary to and evolves with changes to GS1 standards

7. Is based on a Data Quality industry protocol

□ The protocol has two components; i) data inspection against product characteristics, and ii) a data quality management procedure to validate the existence and effectiveness of key data management business processes - The inspection component of the protocol defines a standardised approach for product

inspection (e.g., use GS1 measuring rules, inspect common attributes, use a common sample size and leverage GS1 packaging tolerances). It accounts for small, medium and large enterprises

- The data quality management component of the protocol provides guidance for organisations to establish, document, implement, manage, maintain and improve a data quality management system

8. Enables trading partners to choose their data quality approach (e.g., engage with accredited entity to obtain certification, engage with a non-accredited entity, or self-declare). Self-declaration is an option for companies that have internal capabilities to comply with the protocol

9. Is based on an open system, whereby any accredited business entity may offer product inspection and/or data quality management certification with reference to the Data Quality protocol

10. Allows any business entity to use the Data Quality protocol -- its widespread application is actively encouraged. However, certification cannot be provided by a non-accredited organisation

11. Has been subject to an industry review period



12. Includes ongoing governance to provide stewardship over the framework. The Data Quality Steering Committee will be responsible for employing a neutral accreditation authority to accredit certification bodies (for business entities that wish to be certified). The Steering Committee will also be responsible to implement a continuous improvement process for the data quality framework

13. Is based on a principle that manufacturers own and are responsible for the data they synchronise through their "Home Data Pool", and that they do not accept any third-party updates in the public domain (without their consent).

The intended user community for this framework is comprised of manufacturers, retailers, wholesalers, intermediaries, GS1 Member Organisations, data pools and solution providers.

3. Data Quality Management System Requirements (DQMSR)

3.1. Scope This section specifies requirements for a data quality management system, to enable an organisation to develop and implement a policy and objectives for data quality in order for them to produce good quality data. It takes into account data synchronisation and other requirements to which the organisation subscribes. It does not state specific data quality management performance criteria.

The DQMSR are applicable to any organisation that wishes to:

■ Establish, implement, maintain and improve a data quality management system

■ Assure itself of conformity with its stated data quality management policy

■ Demonstrate conformity to this section by:

□ Making a self-declaration, or

□ Seeking confirmation of its conformance by parties having an interest in the organisation, such as customers, or

□ Seeking certification of its data quality management system by an external organisation.

■ Seek confirmation of its self-declaration by a party external to the organisation.

All the requirements in this section are intended to allow incorporation into any data quality management system. The extent of the application depends on factors such as the data quality management policy of the organisation, the nature of its activities, products and services and the requirements made by other users of the organisation’s data.

This section is applicable for all organisations that generate and store product data before publishing product data into (external) data pools.

This section does not apply to data use in general and does not include requirements for maintaining data structure and completeness in processes such as procurement. To manage these data issues, data users shall commit to GDSN rules for data use.



3.2. Data Quality Management System

3.2.1. General requirements

3.2.1.1. General requirements The organisation shall establish, document, implement, manage and maintain a data quality management system and continually improve its effectiveness in accordance with the requirements of this section.

Responsible management shall provide evidence of its commitment to the development and implementation of the data quality management system and to continually improving its effectiveness by:

■ Communicating to the organisation the importance of meeting this section’s requirements

■ Establishing a data quality management policy

■ Ensuring that quality objectives are established.

Where separate data quality management systems exist, the organisation shall ensure that the information provided by these systems is consistent. Where an organisation chooses to outsource any process that affects compliance with the requirements in this section, the organisation shall ensure control over such processes. Control of such outsourced processes shall be identified within the data quality management system.

3.2.1.2. Documentation requirements The data quality management system documentation shall include:

■ Documented statements of a data quality management policy and data quality management objectives

■ A data quality management manual

■ Documented procedures where indicated in this section

■ Documents needed by the organisation to ensure the effective planning, operation and control of its data quality management processes

■ Records where indicated in this section.

3.2.2. Data quality management policy Responsible management shall ensure that the data quality management policy:

■ Is aimed at assuring good quality data, including data accuracy

■ Includes a commitment to comply with relevant requirements, like GDSN, ISO and GS1 requirements and continually improve the effectiveness of the data quality management system

■ Provides a framework for establishing and reviewing data quality objectives

■ Is communicated and understood within the organisation

■ Is reviewed for continuing suitability.



3.2.3. Planning

3.2.3.1. Data quality management information The organisation shall have in place a documented data quality structure, which is designed and maintained to meet all of the requirements established under section 3.2.1.1 of this protocol and to provide adequate support and information to the organisation for this.

It shall include provision to support the development, implementation and achievement of the data quality management policy, strategy, risk identification, assessment and control, objectives, targets and plans. It shall also support all of the requirements related to implementation and operation, checking and corrective actions and the management review.

The information shall be accessible to all relevant employees and other relevant third parties including contractors as appropriate.

3.2.3.2. Data quality requirements The organisation shall establish and maintain a procedure for identifying and accessing the data synchronisation requirements and other (legal) requirements that are applicable to data management.

The organisation shall keep this information up-to-date. It shall communicate relevant information on data quality and other related requirements to its employees and relevant third parties including contractors.

3.2.3.3. Data quality management processes The organisation shall plan and carry out all data quality management processes under controlled conditions. Controlled conditions shall include, as applicable:

■ The availability of information that describes the origin of the data

■ The availability of work instructions

■ The use of suitable equipment

■ The availability and use of monitoring and measuring processes and devises

■ The implementation of monitoring and measurement

■ The implementation of release, delivery and post delivery activities.

3.2.3.4. Product data database structure and IT infrastructure and safeguards The organisation shall determine, provide and maintain the product data database(s) and IT infrastructure needed to achieve conformity to data quality requirements.

The structure shall:

■ Secure integrity of the data

■ Have a unified data source for external communication

■ Be suitably formatted for data processing and storage

■ Be accessible for review and verification purposes

■ Have access provisions and limitations

■ Ensure traceability of amendments

■ Be suitable for internal and external data exchange.



3.2.3.5. Objectives Responsible management shall ensure that data quality management objectives, including those to meet data synchronisation requirements, are established at relevant functions and levels within the organisation. The data quality management objectives shall be measurable and consistent with the data quality management policy.

3.2.4. Implementation and operation

3.2.4.1. Responsibilities Responsible management shall ensure that data quality management responsibilities and authorities are defined, documented and communicated within the organisation.

Responsible management shall appoint a manager or managers who, irrespective of other responsibilities, shall have the responsibility and authority to:

■ Ensure that processes needed for the data quality management system are established, implemented and maintained

■ Report to responsible management on the performance of the data quality management system and any need for improvement

■ Ensure the promotion of awareness of data quality requirements throughout the organisation.

If more than one manager is appointed the division of responsibilities shall be recorded and communicated throughout the organisation.

Responsible management shall ensure that the integrity of the data quality management system is maintained when changes to the data quality management system are planned and implemented.

3.2.4.2. Reviews At suitable stages responsible management shall perform systematic reviews of processes, procedures, documents and product data in accordance with planned arrangements:

■ To evaluate the ability to meet data quality requirements

■ To identify any issues and propose necessary action.

Participants in such reviews shall consist of representatives of functions concerned with data quality. Records of the results of the reviews and any necessary actions shall be maintained.

3.2.4.3. Personnel, competence, skills and experience Personnel performing work that might affect data quality shall be competent on the basis of appropriate education, training, skills and experience.

The organisation shall:

■ Determine the necessary competence for personnel performing work that might affect data quality

■ Provide training or take other actions to satisfy these needs

■ Evaluate the effectiveness of these actions

■ Ensure that its personnel are aware of the relevance and importance of their activities and how they contribute to the achievement of the quality objectives

■ Maintain appropriate records of education, training, skills and experience.



3.2.4.4. Internal communication Responsible management shall ensure that appropriate communication processes are established within the organisation and that communication takes place regarding the importance of and performance on data quality.

3.2.4.5. Operational control

3.2.4.5.1. General The organisation shall establish, maintain and document the operational processes needed for product measuring and data generation, product master data input, product master data handling and external data publishing, consistent with the requirements of the data quality management system.

Operational processes and business decisions influencing product attributes shall be identified. Where appropriate, provisions shall be made in these processes to ensure that any change in data characteristics is recorded and that appropriate actions are undertaken to continuously guarantee the good quality of published data.

3.2.4.5.2. Data generation and verification and product measurement The organisation shall establish and maintain a procedure / procedures for data generation and verification, and product measurement in accordance with GS1 requirements.

The organisation shall determine appropriate:

■ Methods for measuring product attributes

■ Measuring equipment

■ Measuring locations and conditions

■ Personnel to perform the measurements

■ Methods for the recording of measurement data.

These procedures shall be reviewed for adequacy.

The measurement output data shall be:

■ Stated in internationally accepted units of measurement

■ Suitably formatted for review and data processing.

3.2.4.5.3. Product master data input into internal data systems The organisation shall establish and maintain procedures for data input and creation and shall review these for adequacy. The data input process shall ensure that received data is correctly entered into the internal (data supplier) database.

3.2.4.5.4. Product master data management The organisation shall establish and maintain a procedure for product master data management.

The master data management process shall include:

■ All necessary provisions to ensure that product data is not changed or deformed after data input

■ Access and change authorisation

■ Data storage that assures data integrity



3.2.4.5.5. External data publishing The organisation shall establish and maintain procedures to control the process of publishing product data into external data pools.

The data publishing process shall include all necessary provisions to ensure that product data published into external data pools are accurate, are based upon the actual product characteristics and that published data can be traced back to its origin.

The data publishing procedure shall include:

■ Data publishing with sufficient safeguards for accuracy, integrity and completeness

■ Data verification prior to publishing where the resulting output cannot be verified by measurement

■ Data publishing co-ordination throughout the organisation and its production locations, business units, divisions and departments

■ Appropriate authorisation

■ Traceability back to source for verification and correction

■ Adherence to GTIN-allocation rules.

Responsible management shall appoint a manager or managers who, irrespective of other responsibilities, shall be made responsible for data publishing.

If more than one manager is appointed the division of responsibilities shall be recorded and communicated throughout the organisation.

3.2.5. Measuring and monitoring

3.2.5.1. Monitoring processes and analysis The organisation shall apply suitable methods for monitoring the data quality management system processes, and, where applicable, measure results.

These methods shall demonstrate the ability of the processes to achieve policy objectives and shall include (key) performance indicators defined at relevant functional levels within the organisation.

At regular intervals the performance of the data quality management system shall be evaluated against these performance indicators.

When planned results are not achieved, corrective action shall be taken as appropriate to ensure conformity of the data quality management system.

3.2.5.2. User feedback The organisation shall establish and maintain a documented procedure for dealing with user feedback (including complaints) received from data recipients and other relevant parties. This procedure shall include feedback analysis and a written response to the data recipient or other relevant party.

3.2.5.3. Preventive action The organisation shall establish and maintain a documented procedure to eliminate the causes of potential data quality issues in order to prevent their occurrence. Preventive actions shall be appropriate to the effects of the potential problems.

This procedure shall include provisions to:

■ Determine data quality issues and their causes



■ Evaluate the need for action to prevent occurrence of data quality issues

■ Determine and implement necessary actions

■ Keep records of results of action taken

■ Review preventive action taken.

3.2.5.4. Corrective action The organisation shall establish and maintain a documented procedure to eliminate the cause of data quality issues in order to prevent recurrence. Corrective actions shall be appropriate to the effects of the data quality issues encountered.

This procedure shall include provisions to:

■ Review data quality issues (including user feedback)

■ Determine the causes of data quality issues

■ Evaluate the need for action to ensure that data quality issues do not recur

■ Determine and implement action needed

■ Correct data in the product master data

■ Record the result of action taken and

■ Review corrective action taken.

All corrections should be made both in product master data and published data.

3.2.5.5. Internal audits The organisation shall conduct internal audits at planned intervals to determine whether the data quality management system conforms to the planned arrangements, the requirements of this section and the data quality management system requirements established by the organisation, and whether it is effectively implemented and maintained.

Audit programmes shall be planned, established, implemented and maintained by the organisation, taking into consideration the importance of the data quality management system processes and the results of previous audits.

The organisation shall establish and maintain a documented audit procedure that addresses:

■ Responsibilities and requirements for planning and conducting audits, reporting results and retaining associated records,

■ Determination of audit criteria, scope, frequency and methods.

The selection of auditors and the conduct of audits shall ensure objectivity and impartiality of the audit process.

3.2.6. Management review of system performance Responsible management shall review the organisation’s data quality management system and performance on data quality at planned intervals, to ensure its continuing suitability, adequacy and effectiveness. This review shall include assessing opportunities for improvement and the need for changes to the data quality management system, including the data quality management policy and objectives.

Records from management reviews shall be maintained.



The Review input shall include:

■ Results of audits

■ Reports from data quality management inspections

■ Data user and stakeholder feedback

■ Process performance

■ Data accuracy

■ Status of preventive and corrective actions

■ Follow-up actions from previous management reviews

■ Changes that could affect the data quality management system and

■ Recommendations for improvement.

The Review output shall include any decisions and actions related to:

■ Improvement of the effectiveness of the data quality management system and its processes to ensure data quality and accuracy

■ Improvement of customer related requirements with respect to data quality management

■ Resource needs.

4. Self-Assessment and Self-Declaration

4.1. Introduction

4.1.1. Background To test the level of compliance to the requirements specified in section 3 of the GS1 Data Quality Framework including the Data Quality Protocol, a self-assessment is provided in this section. This self-assessment procedure offers a tool for organisations to tests their compliance to the Framework and may result in a self-declaration if the outcome of the self-assessment meets the necessary standards.

The self-assessment has been developed in 2006/2007 by a team consisting of consumer product manufacturers, retailers, GMA/FPA, AIM, GS1 GDSN, and Capgemini.

Section 3 (Data Quality Management System Requirements) from the GS1 Data Quality Framework is the basis for the self-assessment. The requirements from that section have been formatted as a questionnaire during several workshops in Europe and the US. Following the workshops, the self-assessment questionnaire and KPI scorecard have been tested in a pilot in both the US and the EU. Based on the pilot results and revisions by the Data Quality Steering Committee and other Industry groups, the questionnaire has been further modified into the current format.

4.1.2. Purpose of the self-assessment and self-declaration The self-assessment and self-declaration can be used in two ways:

■ The results from the self-assessment can be used internally for benchmarking purposes and for internal improvement. Internal benchmarking can be done by comparing the results from the various internal organisational entities that have performed the self-assessment. The self-assessment will also point out the areas of improvement for each organisational entity, on which an improvement agenda can be based.



■ With the self-declaration, the self-assessment results can be used in the communication between the manufacturer and retailer. If a manufacturer or retailer is meeting prerequisites set, they can then self-declare compliance to the protocol.

Note: Every organisation can choose, at its own discretion, the best course of action to execute a self-assessment; for instance, companies may wish to execute an assessment ‘in isolation’ before involving other third parties and/or external consulting aid in the process.

4.1.3. Main elements of self-assessment and self-declaration The self-assessment and self-declaration will be conducted using a guideline (section 4.2). The core of the self-assessment is the self-assessment questionnaire (section 4.4) and its scoring model. Besides the questionnaire, organisations will also need to assess a set of master data KPIs (section 4.3), based on the accuracy of actual measurements and key attributes.

4.2. Self-assessment and self-declaration guideline The self assessment and self declaration guideline is described in the table below. The details on how to use the KPI scorecard and questionnaire are available on sections 4.3 and 4.4



1. Decision toconduct selfassessment

2. Determine scope:- products- locations-markets

3. Plan self-assessment - participants - timeline - communication - training

4. Conduct selfassessment

5. Gather results &analyze

6. Calculate scores

Fulfillment ofrequirements for self

declaration

7. Fill self declarationformat

yes

9. Identifyimprovement

areas

no

8. Communicate selfdeclaration to retailers



No. Process Description

What: A decision to perform a self-assessment on the master data management structure can result from internal or external drives. A decision to conduct the self-assessment has to be taken if an organisation wants to self-declare compliancy to the protocol. Also, it may be taken for internal or external benchmarking, or to develop an internal improvement agenda on master data management

Who: The decision to conduct a master data self-assessment can be taken by various organisational departments, but is most likely to occur within master data management.

When: Self-assessments can be conducted for analysis of the internal master data management structure, benchmarking, and/or for self-declaration purposes.

1 Decision to conduct self-assessment

How: The decision to conduct the self-assessment should be taken in collaboration with to relevant parties, both internal (departments) and external (GS1 GDSN, retailer)


What: To make a self assessment feasible to conduct, the exact scope has to be determined. The scope may include the geographic region, specific plants or target markets (retailers) and products or product categories.

Who: This decision should be taken by the management (master data department), taking into consideration other self-assessment activities to prevent overlap.

When: Decisions regarding scope should be taken before a self-assessment procedure is started, as the scope decision influences the remaining steps of the procedure

2 Determine scope

How: Decisions on scope are dependent on the organisational structure. In determining the scope, the questions in section 4.4.2 can be used as a guideline.


What: After determining the scope, the assessment has to be planned, people have to be informed and resources have to be made available. This planning also includes assigning responsibilities for the various elements of the assessment.

Who: One or multiple persons from the master data department

When: As soon as the scope has been determined

3 Plan self-assessment

How: The resources required for conducting the self-assessment have to be informed and made available. A schedule for performing the self-assessment should be developed.




What: Conducting the self assessment consists of filling out the questionnaire by professionals in the organisation and verifying the results with the KPI measurement. Both are described in more detail in next paragraphs (from 4.3 onwards).

Who: The self-assessment is conducted within various organisational entities, depending on the scope.

When: Before starting the self-assessment the timeframe should be predefined in a planning

4 Conduct self-assessment

How: the self-assessment is conducted by a predefined group of persons, who each carry responsibility. The self-assessment may be made available in various formats, e.g. in an online tool or via paper.


What: The results of the self assessment are to be gathered from the professionals in the organisation that filled out the questionnaire. After that the results are first checked on completeness. Next, the results can be analyzed to identify the internal improvement areas. The structure of the questionnaire structure gives direction to the areas of improvement in the results.

Who: The results should be gathered centrally, e.g. at the master data management department. It could also be an independent (internal) department that gathers the results and analyses it.

When: Dependent on the planning, the results are gathered after all resources have completed their part of the questionnaire/KPIs

5 Gather results and analyze

How: It is dependent on the method chosen (web-enabled tool, paper, excel) for self-assessment how the results are gathered and analysed


What: Results from the questionnaire can be calculated by using the scoring model. All answers generate a score and these scores add up to a total score that can be compared with the minimum self declaration level.

Who: The scores should be calculated centrally, if possible by an independent (internal) department.

When: the scores can only be calculated after all results are finalized, in order to prevent making changes based on the scoring.

6 Calculate scores

How: the scoring model as given in this section can be used in the calculations (section 4.4.4)




What: When the minimum score for self declaration is reached, the self declaration format can be filled out. This format may contain the scope on which the self-assessment has been conducted. The format may also include organisational information, and the number of points scored (in total or per paragraph).

Who: The self-declaration format can be filled out by organisations themselves, but need to registered centrally (tbd)

When: The format can be filled in when an organisation complies to the standards set in this section (score questionnaire).

7 Fill self-declaration format

How:


What: After the self-assessment has been conducted and the scoring allows an organisation to self declare, the self-declaration format is completed. Next, this format is communicated to the retailers. Retailers are then knowledgeable of the level of master data management at the organisation. The retailers should have been determined in the scope decision.

Who: The department that has conducted the self-assessment is responsible for communication of the self-declaration.

When: the self-declaration format can only be communicated to retailers after requirements have been met

8 Communicate self declaration to retailers

How: Dependent on the agreement between the retailer and manufacturer (email, postal mail)


What: When the minimum level for self declaration is not reached, improvement areas have to be identified to be able to improve internal master data management processes before starting a new assessment. In case the minimum level is reached and an organisation can self declare, it is still advisable to identify improvement areas and work on continuous improvement of master data management processes.

Who: The master data management department should identify the improvement areas

When: The improvement areas are determined after conducting the self-assessment and analyzing the results

9 Identify improvement areas

How: Improvement areas are determined based on the results from the self-assessment.



4.3. Master data quality KPIs As a tool for validating the self assessment results, a KPI scorecard is made available consisting of 5 key performance indicators for data quality output.

This Data Quality KPI Scorecard covers the following KPIs:

1. Overall item accuracy

2. Generic attribute accuracy

3. Dimension and weight accuracy

4. Hierarchy accuracy

5. Active/Orderable

Overall item accuracy

Active / Orderable

Hierarchy accuracy

zzd

Dimension and weight accuracy

Generic attribute accuracy

These KPIs are an integral part of the overall assessment of data quality and compliance to the Data Quality Framework. In principle, when an organisation has solid internal processes for the management of the quality of the data, the information output should reflect the effectiveness of these internal processes by obtaining consistent high marks on the KPI assessments. If the information obtains low results on the KPIs, it is to be taken as an indication that the assessment of the internal processes requires further revision and correction.

At the same time, obtaining high marks on the KPI results while having a low score on the assessment questionnaire, also means that processes are not properly connected and that the accuracy of the data is being affected by additional factors. Thus, it should be the objective of any organisation attempting to implement the Data Quality Framework to obtain satisfactory results on the KPIs which validate the efficiency of internal processes.

The Data Quality Steering Committee recommends trading partners to internally establish collaborative goals for the for KPI measurements; however, a general guideline for the Industry on target KPI levels has been included in Annex 7 as a means to provide orientation over the course of action that organisations should follow depending on the quality of their data.

Note: The KPI model here described is applicable to all sections of the Data Quality Framework, thus it may also be used in conjunction with the product inspection procedure prescribed on section 6 as a stand-alone best practice to perform audits on the data.

Internal process

assessment

Validation of proper implementation of the

Data Quality Framework

Consistent KPI

results



4.3.1. Master data quality KPIs definition

KPI-definition

1. Overall item accuracy

Definition The percentage of items that have accurate and complete values for all data attributes included in the scope of the Data Quality Protocol.

2. Generic attribute accuracy

Definition The percentage of items that have the GS1 standards correctly applied and have accurate and complete values for all generic data attributes (see list below).

Attributes - globalTradeItemNumber (GTIN)

- classificationCategoryCode (GPC Code)

- tradeItemDescription*

- netContent

*Note: The attribute “tradeItemDescription” is to be populated according to current conventions defined by definitions of the GS1 System and is thus, used for information purposes only.

3. Dimension and weight accuracy

Definition The percentage of items that have the GS1 standards correctly applied and have accurate and complete values for all dimension and weight attributes (see list below) based on the GDSN Package Measurement Rules (including tolerances).

Attributes - depth

- width

- height

- grossWeight

4. Hierarchy accuracy

Definition The percentage of items that have the GS1 standards correctly applied and have accurate and complete values for all hierarchy attributes (see list below).

Attributes - totalQuantityOfNextLowerLevelTradeItem

- quantityOfTradeItemsPerPalletLayer

- quantityOfTradeItemsPerPallet

- quantityOfLayersPerPallet

- quantityOfCompleteLayersContainedInATradeItem

- quantityOfTradeItemsContainedInACompleteLayer

- quantityOfNextLevelTradeItemWithinInnerPack



Note: Please note that the hierarchy attributes listed above do not apply at the same time to all items; please refer to Annex 6 for the applicability and definition of each attribute in order to determine their relevance and usability in a specific item.

5. Active / Orderable

Definition The percentage of items in the home data pool that are still active/orderable or have an accurate end date

4.3.2. Scope Since this is a new activity, some pragmatic scope-restrictions are taken into account in order to increase the feasibility of execution:

■ Focus on selected set of GDSN attributes (see table on Annex 6), for example:

□ globalTradeItemNumber

□ classificationCategoryCode

□ netContent

Other attributes (like additional optional attributes and/or retailer-specific attributes) are out of scope, but in future these can be included based on agreements between specific trading partners. In case of optional and dependant fields, specific checks may need to be conducted – resulting in additional KPI’s.

■ Inspection of data published in the Manufacturer Home Data Pool (i.e. after publishing by the manufacturer, based on the data that is available for the recipient data pool of the retailer).

When a manufacturer does not participate in a data pool, measurements should be taken at the end of the process within the manufacturer before “virtual publication”.

■ Inspection and measurement takes place on both consumer item level and trade item levels (pallet, merchandising unit, trade unit).

4.3.3. Inspect and measure guide on data quality KPIs

4.3.3.1. How to inspect and measure the Accuracy KPIs 1. Define scope of inspection:

a. Product category b. Location c. Target market d. Factory

2. Sampling (please refer to Annex 3: Sampling)

The manufacturer determines the scope of the KPI measurement by selecting the target market and retailer(s) as well as the products he wants to be included in the sample.

It is recommended to include representative articles per product group. “Older” and “newer” articles should represent each group. Per selected article the entire product chain (Pallet, Traded Unit, Merchandising Unit, Consumer Unit) needs to be included.

List the GTINS to be inspected. Allocate the manufacturer’s internal item number of the most recent variant to the GTIN. The size and characteristics of the sample to be taken is prescribed in Annex 3: Sampling.



3. Inspection preparation: Recommendation: read the Best Practice Guidelines for Standard Package Measurement Tolerances (http://www.gs1.org/docs/gsmp/gdsn/GDSN_Standard_Package_Measurement_Tolerances_Best_Practice_i1.pdf) a. Inspection lists should be prepared per article and the related product chain (see

‘Inspection List’ template). The manufacturer’s internal item codes should be pre-printed on the Lists. To prevent measurement from being influenced, the data pool values should not be printed on the lists. b. Locate physical products of the listed internal item codes c. Nominate people with the right knowledge about GS1 rules in order to do an efficient and accurate

inspection. d. Make sure you have all the listed items available in warehouse(s) and/or factory(ies).

4. Inspection All physical measures (dimensions and weight) have to be taken by using appropriate equipment. Physical measures and item classifications have to be in line with the related GS1 System rules. Other information (e.g. GTIN, net weight) needs to be read directly from the real items. Following attributes need to be inspected and measured as described below, with the results being recorded on the prepared Inspection List:

a. Generic attribute accuracy

- globalTradeItemNumber

- classificationCategoryCode

- tradeItemDescription*

- netContent

b. Dimension and weight accuracy

- depth - width - height - grossWeight

c. Hierarchy Accuracy - totalQuantityOfNextLowerLevelTradeItem

- quantityOfTradeItemsPerPalletLayer

- quantityOfTradeItemsPerPallet

- quantityOfLayersPerPallet

- quantityOfCompleteLayersContainedInATradeItem

- quantityOfTradeItemsContainedInACompleteLayer

- quantityOfNextLevelTradeItemWithinInnerPack

5. Analysis

The results from the Inspection lists will have to be compared against the information that is stored in the manufacturer’s home data pool (or internal master data system if no data pool is in use yet). Organisations may also use the ‘Data Quality Master Data KPI Checklist’ to indicate whether the values of each inspected attribute is matching the data from the data pool. The ‘Data Quality Master Data KPI Checklist’ already incorporates GS1 applicable standards such as tolerances.



If the ‘Data Quality Master Data KPI Checklist’ is not used to analyse the results, applicable GS1 System rules and standards must be considered by the organisation.

Only items that are accurate on all attributes are considered accurate on item level.

4.3.3.2. How to inspect and measure the Active/orderable KPIs This KPI measures the percentage of items in the home data pool that are still active/orderable or

have an accurate end date.

■ Take all items from the Manufacturer Home Data Pool and match them with the manufacturer ordering systems.

■ Count the number of items that occur in both the data pool and the ordering system.

■ Count the number of items that do not occur in the ordering system but have a valid (in the past) end date in the data pool.

■ Sum up the two counts and divide them by the number of items in the Manufacturer Home Data Pool.

■ Multiply the result with 100% and the KPI score is generated.

4.4. Self-assessment questionnaire

4.4.1. How to use the self-assessment questionnaire The self-assessment questionnaire may be made available through several means (e.g. on paper, web-enabled tool, etc.). All means follow the same way of completion.

First, the scope to which the self-assessment is done is determined, as discussed in section 4.2. Within the organisational element the self-assessment is executed, first, the resources to answer the questionnaire should be selected. Main resources are the roles within the organisational element that will complete the questionnaire. It is possible that multiple roles complete the questionnaire. In that case, the questions should be assigned to the roles beforehand. Questions may be assigned individually or per paragraph. The answers to the questions given by these separate roles should then be collected.

The questionnaire is comprised of basic questions and general questions. Basic questions touch upon the basic elements of a data quality management system. Both on the basic questions and general questions the final score needs to be above a certain level before an organisational element can self-declare. The basic questions have been put in Bold in the questionnaire. There are 34 basic questions and 40 general questions.

In the questionnaire, the related requirements from the Data Quality Framework are given first, followed by the applicable questions. The section from the Data Quality Framework can be used for reference in answering the questions.

Note: In order to improve the readability of the Data Quality Framework, the complete self-declaration questionnaire is can be found in Annex 1 on page 50.

4.4.2. Preliminary questions on scope self-assessment Before answering the questionnaire, the organisational element which will be assessed is determined (section 4.2). In case this organisational element meets prerequisites, it will have the possibility to self-declare compliance to the standards in the protocol.

The following questions need to be answered to determine the scope.



■ Which organisational entity is assessed?

■ In which geographic region is this organisational element active?

■ What is the number of plants involved?

■ Which products or product category are produced at the locations that are assessed?

■ Which retailers will be informed in case of self-declaration?

■ How many roles within the organisation will be completing the questionnaire and/or doing the KPI measurements?

4.4.3. Scoring model The differentiation between basic and general questions has been taken into account in the scoring model. All basic questions are assigned a maximum of 8 points. Points assigned to general questions vary according to the question’s complexity and relevance. For all questions, there is a maximum number of points, given to the most advanced answer category (A). The other multiple choice answers (B, C, D, E) are awarded a percentage of the maximum number of points. Although there are 34 basic questions and 40 general questions, the importance of the basic questions is indicated by having 66% of the total of 410 points.

The target level is set on 80% of the total score (410 points), which is 328 points.

This overall target equals the sum of the targets set on: Target

a. Basic questions: 219 points b. General questions: 109 points

Total: 328 points

Thus in order to reach the level for self declaration, the targets on both basic and general questions have to be reached. That means at least 219 points have to be scored on the basic questions and 109 points on the general questions.

Number of

questions Maximum points Target Points Target%

Basic Questions 34 272 219 81%

General Questions 40 138 109 79%

Total 74 410 328 80%

Points per Section

Section 1 132

Section 2 178

Section 3 58

Section 4 42

Total 410

The scoring template for the questionnaire necessary to perform an assessment can be found on Annex 2.



5. DQMSR Certification System

5.1. Introduction This section is based on the ISO/IEC guide 65:1996 “General Requirements for bodies operating product certification systems”, and provides a framework for certification bodies to perform DQMSR audits. It sets qualification criteria for certification bodies and staff, and sets rules for the execution of audits and the issuing of certificates. In short it is the guideline for bodies issuing DQMSR certificates.

The certification system consists of the following three elements:

1. The Data Quality Management System Requirements (DQMSR section 3)

2. The DQMSR Certification System (this section)

3. The DQMSR certificate.

Alternatively, organisations may opt for self-declaration. In that case the elements 2 and 3 do not apply. The DQMSR can be applied to any type of data supplier (hereafter referred to as “organisation”). In order to facilitate the implementation of DQMSR, GS1 GDSN Inc. will:

■ Facilitate the issuing of DQMSR certificates on the basis of verification by approved certification bodies

■ Publicise the names of certified organisations and their scope of services

■ Check for misuse of the certificates by (certified) organisations

■ Cancel or withdraw certificates if appropriate.

To audit against the DQMSR, GS1 GDSN Inc. solely concludes agreements with accredited certification bodies. As regards to DQMSR it will allow certification bodies to audit against DQMSR on behalf of GS1 GDSN Inc. only if they hold accreditation for ISO 9001.

5.2. Organisation of the certification body auditing against DQMSR The organisation of the certification body is based on the ISO/IEC Guide 65:1996 and the associated International Accreditation Forum (IAF) Guidance.

The GS1 GDSN Inc. Steering Committee has the right to interpret this document and if necessary to supplement it.

5.2.1. Work area of the certification body The certification body shall:

■ Have a system for internal use for determining what competencies should be available to perform audits against DQMSR

■ Be able to perform a competence analysis

■ Be able to demonstrate that an analysis of the necessary competence was made

■ Be able to demonstrate that it is capable of analysing the data quality aspects for various organisations

5.2.2. Organisation of the certification body The impartiality of the certification body is an essential condition for trust in the certificate. It is the certification body’s responsibility to ensure that individuals working for the certification body’s auditing process have not been involved in any DQMSR consultancy activities for the organisation to be



certified, that may lead to a conflict of interest during the two years preceding the audit, based on the principle that an auditor should not audit his own work.

The certification body shall accept decisions from GS1 GDSN’s Steering Committee, through which interested parties can influence the audit method.

The function of the staff within the certification body is to review whether the certification body is able to carry out the audit for each applicant for a certificate. The staff shall also be able to select, train and prepare auditors. The staff shall be capable of implementing procedures for performing audits and re-audits.

The certification body shall have criteria for training of auditors and appointing auditors and ensure that the auditor meets the requirements set with respect to matters such as:

■ Understanding of data quality issues

■ Knowledge of companies and data systems to be audited

■ Knowledge of guidelines and regulations.

The audit shall be performed by a person or a team.

The certification body shall distinguish between the process of auditing and the decision about issuing and withdrawing certificates. This decision shall be taken impartially. The power of decision may be with an individual or with a group. The auditor shall prepare a report for the decision maker(s) at the certification body and for the organisation involved.

5.2.3. Competence of the personnel Competence requirements are applicable for auditors and individuals deciding on certification. An auditor shall have relevant practical experience in the following fields:

■ Data quality management aspects

■ Management systems and auditing methods

■ Techniques aimed at improving and controlling data accuracy

■ Knowledge of GS1 package measurement rules.

The ability of an auditor to act independently can be demonstrated, for example, by experience as lead auditor in the auditing of other management systems. The auditor is responsible for leading the auditing process in line with the set criteria.

The group or the individual who decides about issuing a certificate shall have the knowledge and expertise in all areas sufficient to evaluate the auditing process and the recommendations of the auditor. The certification body shall have written procedures available laying down these requirements. The certification body shall clearly document the qualified auditor’s input in the decision-making. Further the certification body shall have staff competent to set up and operate procedures for appeals, complaints, and disputes.

5.2.4. Other organisational facilities and procedures Important organisational facilities and procedures to be provided by the certification body are:

- A quality manual and associated procedures

- A guarantee of the confidentiality of information obtained

- The handling of appeals against decisions taken by a certification body

- Carrying out internal reviews within the certification body regarding compliance with these guidelines.



5.3. Document review, implementation review and certificate

5.3.1. The organisation The certification system for DQMSR can be applied to any type of organisation. The certification body shall first perform a contract review to determine if the activities of the organisation to be certified fit within the scope of the certification body. In line with ISO Guide/IAF Guidance the following aspects shall be clearly defined for any organisation to be certified:

■ The activities

■ The site

■ The managerial responsibilities

■ An implemented data quality management system in line with DQMSR.

5.3.2. Certification audit In line with ISO/IEC Guide 65, the initial audit consists of a phase 1 and a phase 2 audit.

Phase 1 audit

The objective of the phase 1 audit is to gain sufficient insight into the management system so as to aid the planning process for the phase 2 audit. The organisation’s preparedness for certification will be assessed. An investigation will be made of the degree to which:

■ The DQMSR management system was established in order to achieve the organisation’s data quality policy

■ Processes are in line with DQMSR requirements

■ The internal audit conforms to the DQMSR

■ The management reviews have taken, among other things, evaluation of the effectiveness of the DQMSR management system into account.

A review of documents is part of the phase 1 audit. The place where the phase 1 audit is performed can be decided in consultation with the organisation.

The certification body shall be able to demonstrate that review of all elements of the DQMSR has been part of the phase 1 audit. Non-conformities identified shall be addressed and solved before a certificate can be granted. The Phase 2 audit can be planned before all non-conformities are solved.

Phase 2 audit

The objectives of the phase 2 audit are:

■ To confirm that the organisation complies with its own policies and procedures

■ To confirm that the management system complies with all elements of the DQMSR and is capable of achieving the organisation’s policy objectives

■ To audit/inspect samples of product measurement and data generation

■ To audit the method for processing data for a number of products.

A DQMSR certificate means that data quality is managed.

Special attention shall be paid to:

■ Setting the criteria for data quality

■ Defining responsibilities for data quality processes



■ The resulting objectives and targets

■ The management of work activities

■ The measurement, monitoring, reporting and review of the results in relation to the objectives and targets

■ The identification and evaluation of non-conformities, and the effectiveness of corrective and preventive action

■ The internal audits and the management’s review of the effectiveness of the system

■ The management’s responsibilities for the data quality policy

■ The relations between policy, processes and results.

The phase 2 audit always takes place at the site of the organisation. The certification body shall base its plan for the performance of the phase 2 audit on the phase 1 audit.

5.3.3. Observation, improvement note and non-conformance Three levels of findings will be identified in the audit reports:

Observation

A finding requiring attention by the organisation, although not necessarily requiring remedial action.

Improvement note

An isolated or sporadic lapse in the content or implementation of procedures or records, which could reasonably lead to failure of the system if not corrected.

Non-conformity

The absence of one or more required DQMSR system elements or incomplete documentation and information, which raise significant doubt as to the capability of the data quality management practises to achieve the policy and objectives of the organisation.

All non-conformities must be eliminated before a certificate can be granted. Improvement notes are acceptable. If non-conformities cannot be closed or downgraded to improvement notes, the certificate will be withdrawn.

5.3.4. Report The audit report shall contain sufficient information to enable a decision on the issuing of a certificate including:

■ Information about the certified organisation

■ An account of the investigation (such as approach, subjects investigated, time spent, audit team)

■ The degree of compliance which the various requirements of DQMSR. Non- conformities shall be explained

■ A summary of the most important findings, both positive and negative, with respect to the implementation and effectiveness of the management system

■ A summary of the documentation from the phase 1 audit

■ The final evaluation by the audit team.

The report shall be sent to the organisation audited and filed at the certification body.



A DQMSR certificate will be issued if it can be demonstrated that the DQMSR have been evaluated and they have been met.

5.3.5. Time allocation A certification body shall have procedures for determining the time needed to perform an audit. The time for an audit is depending on:

■ The size of the organisation

■ The number of locations

■ Involved organisation staff

■ The number of products.

The certification body shall inform the organisation to be certified in advance of the required time of the audit(s).

5.3.6. Certificate A DQMSR certificate can be issued by a certification body. The certificate will be sent to the involved organisation as a statement of conformity with the DQMSR requirements. The certificate is based on an audit of a data quality management system.

5.3.7. Validity and renewal A DQMSR certificate is valid for a period of three years, provided yearly surveillance is performed. For surveillance the organisation shall provide written assurance that the DQMSR management system is still conforming to requirements. Particular attention will be paid to changes in the management system, changes in the standards to which compliance has been certified, or changes in the structure or management of the organisation, if relevant. Based on the written documentation the certification body shall determine whether further investigation is required. Non-conformance notes raised during a surveillance lead to a re-audit within three months

6. Data Inspection Procedure

6.1. Introduction Overall purpose of this section is to enhance data accuracy by applying this inspection procedure, for the benefit of the total industry.

The organisation is responsible for the data it synchronises through home data pools and it will assume full responsibility for providing high quality and standard compliant data.

Organisations can choose to use this inspection procedure to meet data verification requirements.

Using this section is voluntary (i.e. can be part of commercial negotiations) and it is not a prerequisite for using a GDSN certified data pool. It will facilitate acceptance of published data by data recipients. Organisations, which declare conformity with this inspection procedure, are required to fulfil all requirements that are part of this procedure.

Using this section is seen as a temporary solution to improve data accuracy. The implementation of an effective data quality management system is seen as a more effective and sustainable solution to the data quality challenge.



This Inspection Procedure is set up to verify the accuracy of data entered by data suppliers into the Global Data Synchronisation Network (GDSN). This procedure is managed by GS1 GDSN Inc. Further it is recognised that organisations benefit from feedback from data recipients.

Important: The KPI model for master data defined on section 4.3 are applicable to this inspection procedure as well, so it should be the goal of an organisation to achieve results equal or higher to minimum satisfactory marks established for the KPIs.

6.2. Inspection body selection If the organisation wishes to have a data inspection done within an audit or assessment of compliance, an appropriate body shall be appointed to perform inspection against this inspection procedure. An appropriate inspection body is either a qualified person or department with sufficient independence within the organisation or a third party inspection body that meets the following requirements:

■ Independent status / sufficient safeguards for objectivity

■ Inspection body was not involved in the original measurements

■ Individual inspector experience and qualifications in the field of inspections

■ Relevant inspection procedures and protocols

■ Monitoring and control system for the inspection processes

Such a body will inspect against this inspection procedure (or a scheme which incorporates all clauses of this inspection procedure) and will have demonstrated its competence.

If the organisation cannot or does not wish to establish an inspection body within its organisation it shall appoint a third party as inspection body. This inspection body shall be accredited against ISO/IEC 17020:1998 “General criteria for the operation of various types of bodies performing inspection”. It is the responsibility of the organisation to verify that these requirements are met.

6.3. Inspection body staff qualification and experience Lead inspectors must have in-depth understanding of the principle behind each measurement, measurement rules, and the required measuring equipment to perform proper inspections.

Inspectors must understand the inspection requirements, inspection procedures and protocols, use and limitations of measuring equipment, measurement rules, and those aspects that affect their work.

6.4. Scope of inspection The organisation is responsible for defining the number of different products subjected to an inspection (the sample). The size of this sample shall be based on the procedure laid down in Annex 3. During the inspection the inspection body will review the sample size and the actual products that are part of the sample.

The scope of the inspection shall be defined between the organisation and the inspection body. The scope shall be identified in any inspection report and certificate of inspection.

The organisation may contract the inspection body to inspect issues beyond the scope of this inspection procedure, but no relevant elements of this inspection procedure shall be omitted.



6.5. Inspection preparation Before the initial inspection, the organisation is required to review the inspection procedure and relevant reference documents.

Basis for the inspection is the data published by the organisation into the data pool and attributes as listed in the “List of GDSN Attributes to be included in data inspection” (annex 6). Therefore, the organisation should collect and make accessible all data relevant to the product sample that was published to the data pool and verify that upon inspection the most recently published data is available to the inspection body.

The data should be made available for quick reference during the inspection.

Prior to the inspection the organisation provides the inspection body with the pre-inspection documents, which include:

■ Sampling justification

■ Product data sheets with all product data as published into the data pool

■ The data supplier’s list of measuring equipment present at the inspection site

■ Inspection reports of previous inspections.

All documents should meet the requirements as mentioned in Annex 4.

The organisation and inspection body will agree on which appropriate measuring equipment is provided by the organisation to the inspection body.

It is the organisation’s responsibility to ensure that during the inspection the most up-to-date edition of the inspection procedure and relevant reference documents are used.

The organisation and the inspection body agree to a suitable inspection location.

The organisation ensures that the products for inspection are readily available and clearly tagged for identification, in the quantities required by the inspection procedure. The organisation provides safe and easy access to the products.

6.6. Inspection planning The inspection will take place on a mutually convenient date, with due consideration given to the amount of work to meet the inspection procedure.

Factors that might influence the amount of work:

■ The number of products

■ Types of products and packaging

■ The number of sites to visit

■ A large, widely dispersed site

■ The data supplier’s preparation.

6.7. Sample identification Each product sample must be identified with a GTIN, which must be used as a reference in inspection reports. For the aid of the inspection body additional descriptive information is provided which may be referenced in inspection reports.

If further information for sample identification is available, these identification codes must also be referenced in inspection reports.



Important: The sampling methodology prescribed by the Data Quality Framework (see Annex 3) considers that each item on the sample will have a different GTIN/GLN/Target Market combination.

Thus, the sample sizes and methodology do not refer to occasions where several instance of the same item (same GTIN/GLN/Target Market) are measured.

Companies may only obtain valid KPI measurements and data quality indications by applying the sampling methodology as prescribed by the Framework.

However, in cases where manufacturers may want to measure several instances of the same item (same GTIN/GLN/Target Market) -for instance, when measuring several instances of a product with a high variability rate in order to produce an median measurement to report- they are still encouraged to apply the steps and recommendations of the rest of the inspection procedure, in order to guarantee consistency and uniformity on the measurements.

6.8. Measuring equipment The inspection body shall use appropriate measuring equipment during the inspections. Where applicable the “GS1 Package Measurement Rules for Data Alignment” shall be followed.

Where necessary to ensure valid results, measuring equipment shall

■ Be calibrated or verified at specified intervals, or prior to use, against measurement standards traceable to international or national measurement standards; where no such standards exist, the basis used for calibration or verification shall be recorded

■ Be checked, adjusted or re-adjusted as necessary

■ Be identified to enable the calibration status to be determined

■ Be safeguarded from adjustments that would invalidate the measurement result

■ Be protected from damage and deterioration during handling, maintenance and storage.

It is the organisation’s responsibility to make sure that all equipment provided and used for inspection is well maintained and calibrated.

6.9. Inspection Inspection is performed by the inspection body in line with all reference documents as mentioned in this inspection procedure as well as principles of good practise.

Data is verified against the data published into the data pool.

Correct entry into the data pool is defined as: all product attribute values and measurements are found within tolerance (where applicable) ranges and in compliance with definitions from the GDSN Package Measurement Rules, GS1 Global Data Dictionary, and any other applicable documentation from the GS1 System.

Organisations should aim for ultimately obtaining 100% data accuracy, though initial goals can be set on achieving minimum levels agreed by trading partners. Trading partners should use the KPI model from section 4.3 as a means to measure accuracy.

Annex 7 proposes a general recommendation on acceptable levels of compliance with said KPIs.

6.10. Inspection reporting Following each inspection a written report shall be prepared in line with Annex 5.



It is appreciated that sections of the report may be shortened or lengthened to meet specific reporting needs.

The report contains the following sections:

■ Inspection summary

■ Inspection scope

■ Reference documents

■ Overview of inspection findings / results / performance.

Reports are produced and despatched to the organisation within an agreed timescale.

Further the inspection body provides a declaration of inspection, which the data supplier can provide to the data recipient to prove conformity.

6.11. Distribution of report Inspection reports shall remain the property of the organisation and shall not be released, in whole or in part, to a third party, unless the organisation has given prior consent, or is otherwise required to do so by law.

Consent may be given with a consent form, or may be part of a contract. The inspection body will retain a copy of the inspection report.

The inspection report shall be stored safely and securely until the next inspection report is issued, or for a period of five years if no further inspections take place.

6.12. Appeals procedure The inspection body shall have a documented procedure for consideration and resolution of appeals against results of inspections. Procedures shall be independent of the individual inspector and will be considered by senior management of the inspection body. Records of the review and actions arising from appeals shall be maintained.

If necessary the organisation shall facilitate additional inspections to verify and resolve the appeal.

6.13. Complaints The inspection body will have a documented procedure for dealing with complaints received from organisations and other relevant parties. Records of the review and actions arising from complaints shall be maintained.

6.14. Corrective measures The organisation must prove that the inspection findings are input for corrective measures. Inspection findings must result in data corrections in the data as sent to the pool.

7. Definitions Throughout this document, the following definitions apply:

Accreditation

Procedure by which an authoritative body gives formal recognition of the competence of a certification body to provide certification services, against an international standard.



Accreditation body

Agency having jurisdiction to formally recognise the competence of a certification body to provide certification services.

Audit

Systematic and functionally independent examination to determine whether activities and related results comply with a standard, whereby all the elements of this standard are covered by reviewing the data suppliers’ manual and related procedures, together with an inspection of the data and the applicable products.

Auditor

Person qualified to carry out audits for or on behalf of a certification body.

Certification

Procedure by which accredited certification bodies, based on an audit or an inspection, provide written or equivalent assurance that data and where applicable their management system and its implementation conform to requirements.

Certification body

Provider of certification services, accredited to do so by an accreditation body.

GLN

A Global Location Number is a unique numerical value used to identify a location either virtual or physical of an organisation, upon which there is a need to retrieve pre-defined information.

GPC

Global Product Classification is the standard taxonomy schema for products used in GDSN. It is composed of 4 levels, Segment, Family, Class and Brick. GPC is maintained by GS1.

GTIN

A particular Global Trade Item Number is unique numerical value used to identify a trade item. A trade item is any item (product or service) upon which there is a need to retrieve pre-defined information and that may be planned, priced, ordered, delivered and or invoiced at any point in any supply chain.

GSMP

The Global Standards Management Process (GSMP) was created by GS1 and GS1 US to support standards development activity for the GS1 System. The GSMP uses a global consensus process to develop supply chain standards that are based on business needs and user-input.

Inspection

Examination of data and the applicable products, in order to verify that they conform to requirements.



Organisation

Company, corporation, firm, enterprise, authority or institution, or part or combination thereof, whether incorporated or not, public or private, that has its own functions and administration.

Note: For organisations with more than one operating unit, a single operating unit may be defined as an organisation.

Self-assessment

Self-administered assessment of compliance to the requirements for a Data Quality Management System which is performed according to the procedure prescribed by the Data Quality Framework.

Self-declaration

A formal statement by an organisation in which the organisation declares that the product data published in the data pool meets the requirements of GDSN.

Target Market

Geographical region in which a particular product is intended to be sold, distributed and commercialised.

8. Reference Documents The most up-to-date version of the following documents shall be used for data quality management system audits, product measuring and inspection of data accuracy. The links to the repository where the most recently published versions of these documents can be found are listed next to the document entry:

■ Business Requirement Document For Data Synchronisation Data Model for Trade Item (Data Definition) http://www.gs1.org/docs/gsmp/gdsn/Data_Synchronization_Data_Model_for_Trade_Item.pdf

■ Global Data Dictionary http://gdd.gs1.org/GDD/public/default.asp

■ GDSN Package Measurement Rules for Data Alignment including Standards Tolerances for Data Accuracy http://www.gs1.org/docs/gsmp/gdsn/GDSN_Package_Measurement_Rules.pdf

■ GTIN Allocation Rules http://www.gs1.org/gtinrules/

■ GLN Allocation Rules http://www.gs1.org/glnrules/

■ GPC Published Standards http://www.gs1.org/productssolutions/gdsn/gpc/

■ Miscellaneous data quality support documentation at GS1 GDSN http://www.gs1.org/productssolutions/gdsn/dqf/index.html



A. Annex 1: The self-assessment questionnaire

A.1 Planning 1.1 Data quality management information The organisation shall have in place a documented structure that is designed and maintained to meet all of the requirements established under clause “3.2.1.1 General Requirements” of this protocol and to provide adequate support and information to the organisation for this. The structure shall include provision to support the development, implementation and achievement of the data quality management policy, strategy, risk identification, assessment and control, objectives, targets and plans. It shall also support all of the requirements related to implementation and operation, checking and corrective actions and the management review. The information shall be accessible to all relevant employees and other relevant third parties including contractors as appropriate. Questions 1.1.1 Does the organisation have a documented data quality management structure in place?

a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

1.1.2 Does the organisation have a data quality policy?


1.1.3 Does the documentation of this data quality management structure includes data quality management manual, objectives and targets?

a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No (if no, go to 1.1.6)

1.1.4 To what extent are the objectives on data quality management measurable?

a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

1.1.5 To what extent does the documentation of this data quality management structure contain the data

quality management action plans? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%



1.1.6 To what extent does the documentation of this data quality management structure contain the data

quality management risk identification, risk assessment, and risk control actions? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

1.1.7 Do you have a procedure implemented to facilitate changes to the data quality management structure?

Example: test routines a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

1.1.8 To what extent does the Data Management structure comply with the GS1 standards for packaging

measurements and tolerances? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

1.2 Data quality requirements The organisation shall establish and maintain a procedure for identifying and accessing the data synchronisation requirements and other (legal) requirements that are applicable to data management. The organisation shall keep this information up-to-date. It shall communicate relevant information on data quality and other related requirements to its employees and relevant third parties including contractors. Questions 1.2.1 To what extent has the organisation implemented the GDSN requirements for data synchronisation?


1.2.2 Is there a process in place to keep the organisation up-to-date regarding the GDSN requirements?

[implementation and internal communication] a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No



1.3 Data quality management processes The organisation shall plan and carry out all data quality management processes under controlled conditions. Controlled conditions shall include, as applicable: - The availability of information that describes the origin of the data - The availability of work instructions - The use of suitable equipment - The availability and use of monitoring and measuring processes and devices - The implementation of monitoring and measurement - The implementation of release, delivery and post delivery activities. Questions 1.3.1 Is the ownership of the data within the organisation defined and documented, and/or implemented? Example: RACI chart, master data catalogue


1.3.2 Does the organisation have work instructions and a available to support data quality management

processes? a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

1.3.3 Does the organisation make use of standardised monitoring and measuring processes?

Example: Auditing a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

1.3.4 To what extent does the organisation use equipment as recommended by GS1 in the ‘Best Practice

Guidelines for Standard Package Measurement Tolerances’ within all relevant data quality management processes for dimensions measurement?


1.3.5 Are the tools that require calibration being calibrated within your organisation (either by internal or

external certified service providers), according to requirements? a) Yes b) No



1.4 Product data database structure and IT infrastructure and safeguards The organisation shall determine, provide and maintain the product data database(s) and IT infrastructure needed to achieve conformity to data quality requirements. The database structure shall: - Secure integrity of the data in the database - Be suitably formatted for data processing and storage - Be accessible for review and verification purposes - Have access provisions and limitations - Ensure traceability of amendments - Be suitable for internal and external data exchange. Questions 1.4.1 Does the organisation make use of a single source of the truth for product master data to manage and

share data with trading partners? a) Yes b) No

1.4.2 To what extent does the database structure have access authorisation procedures? Attach: examples of security systems and tools that are used


1.4.3 Does the organisation have a structure in place to ensure the security of data from unauthorised

change? Example: Restrict update rights capability, access rights Example: IS backing up files (tapes available)

Example: Schedule review of security rights (right people entering data) a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

1.4.4 To what extent does the database structure ensure traceability of amendments (change history)? Attach: examples of security systems and tools that are used


1.4.5 Is there a process in place to identify and communicate changes/corrections?

Example: Consistency checking by the data manager, registration of change history a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No



A.2 Implementation and operation 2.1 Responsibilities Responsible management shall ensure that data quality management responsibilities and authorities are defined, documented and communicated within the organisation. Responsible management shall appoint a manager or managers who, irrespective of other responsibilities, shall have the responsibility and authority to: - Ensure that processes needed for the data quality management system are established, implemented

and maintained - Report to responsible management on the performance of the data quality management system and

any need for improvement - Ensure the promotion of awareness of data quality requirements throughout the organisation. If more than one manager is appointed, the division of responsibilities shall be recorded and communicated throughout the organisation. Responsible management shall ensure that the integrity of the data quality management system is maintained when changes to the data quality management system are planned and implemented. Questions 2.1.1 Has the organisation defined the data quality management roles and responsibilities? Example: data quality manager responsibilities


2.1.2 Do the manager(s) who are appointed have the responsibility and authority to ensure that processes

needed for the data quality management structure are established, implemented and maintained? Example: data quality manager responsibilities

a) Yes b) No (go to 2.2.1)

2.1.3 In case more than one manager is appointed: Has the division of responsibilities been recorded and

communicated throughout the organisation? a) Yes, or not applicable b) No

2.2 Reviews At suitable stages systematic reviews of processes, procedures, documents and product data shall be performed by responsible management in accordance with planned arrangements: - To evaluate the ability to meet data quality requirements - To identify any issues and propose necessary action. Participants in such reviews shall consist of representatives of functions concerned with data quality. Records of the results of the reviews and any necessary actions shall be maintained.



Questions 2.2.1 Does the organisation periodically audit the data quality management structure? Example: Include review of processes, procedures, document, product data

Audits: prove of adherence to procedures outlined, adherence to internal requirements a) Yes, yearly b) Yes, every two years c) Yes, every three to five years d) No, never

2.2.2 Are the results of these audits shared within the organisation? Audits: prove of adherence to procedures outlined, adherence to internal requirements Example: intranet / extranet / email

a) Yes b) No

2.2.3 Do the audits result in documented and communicated action plans, if required? Including feedback from auditors and clients (retailers) Example: training, change in equipment.

a) Yes b) No

2.3 Personnel, competence, skills and experience Personnel performing work that might affect data quality shall be competent on the basis of appropriate education, training, skills and experience. The organisation shall: - Determine the necessary competence for personnel performing work that might affect data quality - Provide training or take other actions to satisfy these needs - Evaluate the effectiveness of these actions - Ensure that its personnel are aware of the relevance and importance of their activities and how they

contribute to the achievement of the quality objectives - Maintain appropriate records of education, training, skills and experience. Questions 2.3.1 To what extent has the organisation identified what skills and talents are required in managing data

quality? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

2.3.2 To what extent do the people in place who to manage data quality have the right talents and skills set? Example: job descriptions, checked by HRM, QA management


2.3.3 To what extent are people working with master data part of an ongoing training program? Example: Training program




2.3.4 To what extent does the organisation maintain appropriate records of education, training, skills, and

experience? HR recording via personal file


2.3.5 To what extent does the organisation evaluate the effectiveness of the actions taken to increase the

competencies of personnel regarding data quality? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

2.4 Internal communication Responsible management shall ensure that appropriate communication processes are established within the organisation and that communication takes place regarding the importance of and performance on data quality. Questions 2.4.1 Is there an ongoing internal communication process on any aspect of data quality, to create awareness

within the organisation on the importance of providing highly accurate data? Example: Internal websites, email, newsletter, other tools


2.5 Operational control 2.5.1 Product measurement and data generation The organisation shall establish and maintain a procedure / procedures for product measurement and data generation in accordance with GS1 requirements. The organisation shall determine appropriate: - Methods for measuring product attributes - Measuring equipment - Measuring location and conditions - Personnel to perform the measurements - Method for the recording of measurement data. These inputs shall be reviewed for adequacy. The measurement output data shall be: - Stated in internationally accepted units of measurement - Suitably formatted for review and data processing.



Questions 2.5.1.1 Has the organisation got operational processes needed for product measuring and data generation (in

accordance GS1 requirements)? Example: Reporting structure, responsibilities, work instructions, work flow routines


2.5.1.2 Does the organisation have a specific process for generating and checking the data for new products, prior to first distribution of new products?

Example: finished product may vary from design – reality check a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

2.5.1.3 To what extent are the GDS definitions on attributes applied internally?


2.5.1.4 To what extent has the organisation determined appropriate methods for the recording of measurement

data? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

2.5.1.5 Is the output data in compliance with standards of the GS1 accepted units of

measure? Attach: GS1 standards

a) Yes b) No

2.5.1.6 Does the organisation have a GTIN, GPC and GLN allocation policy?

Example: GTIN: Global Trade Identification Number (attach documents) GPC: Global Product Clasification GLN; Global Location Number Example: (conditions under which change in product needs change in barcode)

a) Yes b) No

2.5.1.7 To what extent is the GTIN policy applied within the organisation?

Example: (GTIN: Global Trade Identification Number) a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%



2.5.1.8 Does the organisation have a process in place for checking product data during the product lifetime (ongoing check)?

a) Yes b) No

2.5.2 Product master data input into internal data systems The organisation shall establish and maintain procedures for data input and creation and shall review these for adequacy. The data input process shall ensure that received data is correctly entered into the internal (data supplier) database. Questions 2.5.2.1 Does the organisation have approved processes and procedures for data input?

Example: Procedures for data input (accuracy check of previous day, sampling of new data, spot check, and verification audits)


2.5.2.2 Does the organisation review the procedures for data input and creation for adequacy?

a) Yes, yearly b) Yes, every two years c) Yes, every three to five years d) No, never

2.5.2.3 Has the organisation established, maintained, and documented the operational processes needed for

internal data publishing? a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

2.5.3 External data publishing The organisation shall establish and maintain procedures to control the process of publishing product data into external data pools. The data publishing process shall include all necessary provisions to ensure that product data published into external data pools is accurate, based upon the actual product characteristics and that published data can be traced back to its origin. The data publishing procedure shall include: - Data publishing with sufficient safeguards for accuracy, integrity and completeness - Data verification prior to publishing where the resulting output cannot be verified by measurement - Data publishing co-ordination throughout the organisation and its production locations, business units,

divisions and departments - Appropriate authorisation - Traceability back to source for verification and correction - Adherence to GTIN/GPC/GLN-allocation rules. Responsible management shall appoint a manager or managers who, irrespective of other responsibilities, shall be made responsible for data publishing. If more than one manager is appointed the division of responsibilities shall be recorded and communicated throughout the organisation.



Questions 2.5.3.1 Have critical success factors (key elements that ensure a satisfactory performance) been established in

the processes for external data publishing? a) Yes b) No

2.5.3.2 Has the organisation established and maintained procedures to control the process of publishing

product data into external data pools? Example: organisational set-up, clear lines of responsibilities


2.5.3.3 Does the data publishing process include all necessary provisions to ensure that product changes

published into external data pools is based upon the most relevant version of the product? a) Yes b) No

2.5.3.4 To what extent does the data publishing process include all necessary provisions to ensure that product

data attributes published into external data pools can be traced back to its origin? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

2.5.3.5 Does the data publishing procedure include: appropriate authorisation?

a) Yes b) No

A.3 Measuring and monitoring 3.1 Monitoring processes and analysis The organisation shall apply suitable methods for monitoring the data quality management system processes and, where applicable, measure results. These methods shall demonstrate the ability of the processes to achieve policy objectives and shall include performance indicators defined at relevant functional levels within the organisation. At regular intervals the performance of the data quality management system shall be evaluated against these performance indicators. When planned results are not achieved, appropriate corrective action shall be taken to ensure conformity of the data quality management system. Questions 3.1.1 Which monitoring methods on master data management are used within the organisation to evaluate

and track the data quality management processes and procedures? Answer: internal/external auditing, process performance indicators, user feedback

3.1.2 Are performance indicators defined for each process in the data management structure?

Example: feedback from clients, data reports



a) Yes, always b) Yes, most of the time c) Yes, sometimes d) No, never

3.1.3 To what extent are these performance indicators tracked?


3.1.4 After recurrence of known failures, are steps taken to prevent recurring? a) Yes, always b) Yes, most of the time c) Yes, sometimes d) No, never

3.1.5 To what extent are all corrections suitable, made in both the product master data and the published data

(if relevant)? a) 90% or more b) From 50% to 90% c) From 10 % to 50% d) From 0% to 10%

3.1.6 Based on the results of the analysis of performance indicators, are the data quality management

structure processes changed or adapted? a) Yes, always b) Yes, most of the time c) Yes, sometimes d) No, never

3.1.7 Are the results on the performance indicators communicated within the organisation and if applicable to

3rd party service providers? Example: email, newsletter, internal website, etc


3.2 Customer feedback The organisation shall establish and maintain a documented procedure for dealing with user feedback (including complaints) received from data recipients and other relevant parties. This procedure shall include feedback analysis and a written response to the data recipient or other relevant party. Questions 3.2.1 Is a documented procedure in place for handling customer complaints concerning data quality?


3.2.2 Are improvement actions initiated based on the analysis of customer feedback?

a) Yes, always



b) Yes, most of the time c) Yes, sometimes d) No, never

3.2.3 Are written responses issued to customers in regards their data quality complaints?


3.3 Internal Audits The organisation shall conduct internal audits at planned intervals to determine whether the data quality management system conforms to the planned arrangements, the requirements of this section and the data quality management system requirements established by the organisation, and whether it is effectively implemented and maintained. Audit programmes shall be planned, established, implemented and maintained by the organisation, taking into consideration the importance of the data quality management system processes and the results of previous audits. The organisation shall establish and maintain a documented audit procedure that addresses: - Responsibilities and requirements for planning and conducting audits, reporting results and retaining

associated records, - Determination of audit criteria, scope, frequency and methods. The selection of auditors and the conduct of audits shall ensure objectivity and impartiality of the audit process. Questions 3.3.1 Is there a process for determining the criteria, scope, frequency and methods for executing internal

audits of the data quality management system? a) Yes, implemented, documented and regularly reviewed b) Yes, implemented and documented c) Yes, implemented d) Yes, documented e) No

A.4 Management review of system performance Responsible management shall review the organisation’s data quality management system and performance on data quality at planned intervals to ensure its continuing suitability, adequacy and effectiveness. This review shall include the assessment of opportunities for improvement and the need for changes to the data quality management system, including the data quality management policy and objectives. Records from management reviews shall be maintained. The Review input shall include: - Results of audits - Reports from data quality management inspections - Data user and stakeholder feedback - Process performance - Data accuracy - Status of preventive and corrective actions - Follow-up actions from previous management reviews - Changes that could affect the data quality management system



- Recommendations for improvement. The Review output shall include any decisions and actions related to: - Improvement of the effectiveness of the data quality management system and its processes to ensure

data quality and accuracy - Improvement of customer related requirements with respect to data quality management - Resource needs. Questions 4.1.1 Does the management periodically review the organisation’s data quality management structure and

performance on data quality? a) Yes, yearly b) Yes, every two years c) Yes, every three to five years d) No, never

4.1.2 To what extent does the review include assessing opportunities for improvement and the need for

changes to the data quality management structure, including the data quality management policy and objectives? Example: Description of functioning auditing organisation


4.1.3 Are records of the reviews kept?

a) Yes b) No

4.1.4 Does the review input include the results of audits?

a) Yes b) No

4.1.5 Does the review input include reports from data quality management inspections?

a) Yes b) No

4.1.6 Does the review input include data user and stakeholder feedback? a) Yes b) No

4.1.7 Does the review input include process performance? a) Yes b) No

4.1.8 Does the review input include status of preventive and corrective actions? a) Yes b) No

4.1.9 Does the review input include follow-up actions from previous management reviews? a) Yes b) No



4.1.10 Does the review input include changes that could affect the data quality management structure?

a) Yes b) No

4.1.11 Does the review input include recommendations for improvement? a) Yes b) No

4.1.12 Does the review input include the evaluation of the KPI results? a) Yes b) No

4.1.13 Does the review output include decisions and action related to improvement of the effectiveness of the

data quality management structure? a) Yes b) No

4.1.14 Does the review output include decisions and action related to improvement of the effectiveness of the

data quality processes to ensure data quality and accuracy? a) Yes b) No

4.1.15 Does the review output include decisions and action related to improvement of customer related

requirements with respect to data quality management? a) Yes b) No



B. Annex 2: Scoring model for the self-assessment questionnaire

Question Answer A B C D E

Points scored on basic questions

Points scored on general questions

1.1.1 B 8 7 6 1 0

1.1.2 B 8 7 6 1 0

1.1.3 B 8 7 6 1 0

1.1.4 6 4 2 0

1.1.5 4 3 1 0

1.1.6 4 3 1 0

1.1.7 4 3 3 1 0

1.1.8 B 8 6 2 0

1.2.1 B 8 6 2 0

1.2.2 B 8 7 6 1 0

1.3.1 B 8 7 6 1 0

1.3.2 4 3 3 1 0

1.3.3 6 5 4 1 0

1.3.4 B 8 6 2 0

1.3.5 2 0

1.4.1 B 8 0

1.4.2 B 8 6 2 0

1.4.3 B 8 7 6 1 0

1.4.4 B 8 6 2 0

1.4.5 6 5 4 1 0

Subtotal




Points scored on Basic Questions


2.1.1 B 8 7 6 1 0

2.1.2 6 0

2.1.3 4 0

2.2.1 B 8 6 4 0

2.2.2 4 0

2.2.3 4 0

2.3.1 B 8 6 2 0

2.3.2 B 8 6 2 0

2.3.3 B 8 6 2 0

2.3.4 2 1 1 0

2.3.5 2 1 1 0

2.4.1 B 8 7 6 1 0

2.5.1.1 B 8 7 6 1 0

2.5.1.2 B 8 7 6 1 0

2.5.1.3 4 3 1 0

2.5.1.4 6 4 2 0

2.5.1.5 B 8 0

2.5.1.6 B 8 0

2.5.1.7 B 8 0 0 0

2.5.1.8 4 0

2.5.2.1 B 8 7 6 1 0

2.5.2.2 2 2 1 0

2.5.2.3 B 8 7 6 1 0

2.5.3.1 6 0

2.5.3.2 B 8 7 6 1 0

2.5.3.3 6 0

2.5.3.4 B 8 6 2 0

2.5.3.5 B 8 0

Subtotal






3.1.1 0 0 0 0 0

3.1.2 B 8 6 4 0

3.1.3 4 3 1 0

3.1.4 B 8 6 4 0

3.1.5 4 3 1 0

3.1.6 4 3 2 0

3.1.7 4 3 2 0

3.2.1 B 8 7 6 1 0

3.2.2 B 8 6 4 0

3.2.3 4 3 2 0

3.3.1 6 5 4 1 0

Subtotal




4.1.1 B 8 6 4 0

4.1.2 B 8 6 2 0

4.1.3 2 0

4.1.4 2 0

4.1.5 2 0

4.1.6 2 0

4.1.7 2 0

4.1.8 2 0

4.1.9 2 0

4.1.10 2 0

4.1.11 2 0

4.1.12 2 0

4.1.13 2 0

4.1.14 2 0

4.1.15 2 0

Subtotal



Basic questions

General questions

Subtotal section 1

Subtotal section 2

Subtotal section 3

Subtotal section 4

Total Score

Self-declaration standard 219 109



C. Annex 3: Sampling

The following steps shall be used to determine sample sizes:

1. All trade items to which data applies which is published by the data supplier are made part of the sample population according to their GTIN.

2. The data supplier shall categorise the remaining trade items based on the following characteristics into sample groups:

a. Consumer (end user) trade items

i. Rigid packaging

ii. Flexible packaging

iii. Hanging items

iv. Cylindrical items

v. Multi-packs

b. Non-consumer trade items, no pallet

c. Non-consumer trade items, including a pallet

If more than one sample group is applicable, the data supplier shall select only one.

3. Within each sample group, a sample will be taken in accordance with the formula: [Sample = √n +

0.1n], where n = number of articles. All items that are identical1

count as one article in the sample.

Example of sample sizes in each sample group based on the sample formula.

N sample n sample

1 1 500 73

5 3 1000 132

10 5 1250 161

25 8 1750 217

50 13 2500 300

100 20 3500 410

250 41 4000 464

4. The data supplier will strive for the widest variation in trade items possible, based on dimensions

and the packaging material code. However, it is recommended to select different trade items from the same hierarchy as much as possible.

1

In the sense that they have a unique GTIN-GLN-Target Market combination



D. Annex 4 : Pre-inspection documentation requirements

Sample justification

- Total number of GTINs ‘live’ in the GDSN data pool

- Overview of GTINs considered like items

- Division of GTINs in sample groups (include Trade Item Description for reference purposes)

- Sample size for each sample group.

Product data sheet

Data sheet for each GTIN subject to inspection with all product data as published into the data pool.

Measuring equipment

Overview of measuring equipment with relevant specifications (type, brand, serial number, etc.)

Table to indicate which product attribute will be measured with what type of measuring equipment.

Previous inspection reports

If applicable, previous inspection reports shall be made available to the inspection body, for review.




E. Annex 5: Inspection report requirements The report contains the following sections:

1. Inspection summary

2. Inspection scope

3. Reference documents

4. Overview of inspection findings / results / performance

5. Action plan.

Inspection summary

Brief summary of the inspection which states at least: organisation reference data, number of inspected GTINs and statement on performance in % of inspected GTINs.

Inspection scope

- Organisation reference data (name, department/ business unit, address, contact person, etc.)

- Visited locations

- Number of GTINs.

Reference documents

References should be made to all documents used during the inspection, including version numbers and publication dates.

Overview of inspection findings / results / performance

- Overview of all findings listed per GTIN

- Summary / conclusion with aggregated results

- Statement on performance in % of inspected GTINs.

Action plan

- Overview of all inaccurate data for corrective measures by the organisation

- Other (additional) inspections planned to verify data accuracy.

Annexes

- Overview of inspected GTINs and inspected packaging levels.


F. Annex 6: List of GDSN ATTRIBUTES for product inspections Based on the EAN.UCC Business Requirement Document for Data Synchronisation Data Model for Trade Item, latest version (7.7.1, May 24, 2005). Where applicable, the most up-to-date definitions from the Global Data Dictionary (GDD, http://gdd.gs1.org/GDD/public/default.asp) are provided.

Definitions:

Source line1 Headword Definition

452 Trade item A Trade Item is any product or service upon which there is a need to retrieve pre-defined information and that may be priced, ordered or invoiced at any point in any supply chain.

UN/CEFACT Unit of measure Indication of the unit of measurement in which weight (mass), capacity, length, area, volume or other quantity is expressed.

Attributes:

Line1

Item name Definition/explanation Applicability2

Recorded result Category3 (KPI)

664 globalTradeItemNumber

EAN.UCC numbering structures will be used for the identification of trade items. All of them will be considered as 14-digit Global Trade Item Number (GTIN). Must be present to enable data to be presented to trade item catalogue. Must be submitted by the owner of the data (who may be the original manufacturer, the importer, the broker or the agent of the original manufacturer). This field is mandatory within the Global Data Synchronization work process.

All levels The individual GTIN, or Not Exist

Generic attributes

768 classificationCategoryCode4 Global EAN.UCC classification category code.

Unique, permanent 8-digit key. All levels Individual GPC code

Generic attributes

January 2008, Issue 2 - DRAFT All contents copyright © GS1 2008 of 66


Line1



1254 tradeItemDescription5

This field is automatically generated by the concatenation of the "brand"," sub-brand", "functional name" and "variant". Free form text field, this data element is repeatable for each language used and must be associated with a valid ISO language code . This is a derived attribute resulting from the concatenation of 4 other attributes. When implemented, these four attributes may be concatenated as appropriate. Item description is part of the set of core data that will be stored in the Registry.

All levels Description provided by the manufacturer

Generic attributes

4645 netContent The amount of the trade item contained by a package, as claimed on the label. Consumer unit

netContent Unit of Measure

Declared quantity or weight [pieces, g/lbs]

Generic attributes

4421 DepthThe measurement from front to back of the consumer trade item or the longest side of the base of the non-consumer trade item

6.

All levels

depth Unit of Measure

Depth (mm/in) Dimensions & weight

4767 WidthThe measurement from left to right of the consumer trade item or the shortest side of the base of the non-consumer trade item.

All levels

width Unit of Measure

Width (mm/in) Dimensions & weight

4567 Height

The measurement of the height of the trade item. The vertical dimension from the lowest extremity to the highest extremity, including packaging. At a pallet level the trade item height will include the height of the pallet itself.

All levels

height Unit of Measure

Height (mm/in) Dimensions & weight

4542 grossWeight

Used to identify the gross weight of the trade item. The gross weight includes all packaging materials of the trade item. At pallet level the trade item gross weight includes the weight of the pallet itself.

All levels

grossWeight Unit of Measure

Gross weight (kg/lb)

Dimensions & weight



Line1



492 totalQuantityOfNextLowerLevelTradeItem

This represents the Total quantity of next lower level trade items that this trade item contains.

All levels except consumer unit

Quantity Hierarchy attribute

4163 quantityOfTradeItemsPerPalletLayer

7

The number of trade items contained on a single layer of a pallet. Only used if the pallet has no GTIN. It indicates the number of trade items placed on a pallet layer according to supplier or retailer preferences.

Trade Unit levels (when pallet unit has no GTIN allocated)


4087 quantityOfLayersPerPallet7

The number of layers that a pallet contains. Only used if the pallet has no GTIN. It indicates the number of layers that a pallet contains, according to supplier or retailer preferences.



4137 quantityOfTradeItemsPerPallet7

The number of trade items contained in a pallet. Only used if the pallet has no GTIN. It indicates the number of trade items placed on a pallet according to supplier or retailer preferences.



4064 quantityOfCompleteLayersContainedInATradeItem

7

The number of complete layers contained in a higher packaging configuration. Used in hierarchical packaging structure of a trade item. Cannot be used for trade item base unit.

All levels except base unit


4113 quantityOfTradeItemsContainedInACompleteLayer

7

The number of trade items contained in a complete layer of a higher packaging configuration. Used in hierarchical packaging structure of a trade item. Cannot be used for trade item base unit.

All levels except base unit


4214 quantityOfNextLevelTrade ItemWithinInnerPack

7Indicates the number of next lower level trade items contained within the physical non-coded grouping (innerpack).

All applicable levels with inner-pack groupings



1From the EAN.UCC Business Requirement Document for Data Synchronisation Data Model for Trade Item, version 7.7.1, May 24, 2005

7 Please note that these hierarchy attributes do not apply at the same time to all items; please refer to the applicability of each attribute in order to determine their relevance and usability for a specific item.


y 2008, Issue 2 - DRAFT All contents copyright © GS1 2008 of 66

Note: The “GDSN Package Measurement Rules for Data Alignment” should be used for correct measurement.

2Applicability as to the levels of the trade item hierarchy, including inner packs.

4To be inspected for existence only, however code 99999999 is not allowed.

3This indicates in which type of KPI is a specific attributed considered

5For information purposes only. 6Also referred to as length.

Januar


G. Annex 7: Guidelines on KPIs targets for the Industry The following guidelines were developed by the Data Quality Steering Committee as a means to provide trading partners with a general indication over the expected accuracy in the information.

These guidelines however, are not a mandated assessment tool, since they are simply meant to provide trading partners some context to further work on improving data quality and data accuracy.

Organisations will always be free to define different KPI levels and objectives either internally or in collaboration with other trading partners. These recommendations will simply offer some orientation over the general perspective from the Industry in regards to data accuracy.

Important: The KPI levels below apply to all products types regardless of their packaging type or composition. Additionally, these KPI target levels are also applicable to all the different KPIs defined on section 4.3

KPI Scores and meaning:

Score Meaning

95% or higher

Reasonably good data; obtaining a score of 95% or higher on all or individual KPIs means that data is almost entirely reliable and that most trading partners are likely to accept the information. Organisations with a score in this range may choose to work closer to trading partners on specific opportunities in order to achieve 100% accuracy.

From 75% to 95%

Obtaining a score between 75% and 95% indicates that the information has significant problems, but that it is salvable data and could be improved if trading partners take the right course of action. An organisation obtaining a score in this range is encouraged to set immediate action to improve as well as committing to delivering results. Some trading partners may choose to still accept this data at their discretion.

Less than 75%

Poor quality data; obtaining less that 75% percent on all or individual KPIs means that the data is mostly unreliable and that most trading partners are unlikely to accept the data. Organisations with a score in this range are strongly advised to fully revise their data synchronisation and data quality programmes.


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