1. How to Measure Data Quality
Data Quality Management System (DQMS)
Self Assessment
Product Inspection & KPIs
Continuous Improvement
“Quality is a measurable, manageable business issue” John Guaspari

2. How to Measure Data Quality Key Elements of DQF
Data Quality Management System
Self-assessment Model
Product Inspection Model
Scorecard & KPIs
Monitoring & Continuous Improvement Guidelines
Lets go into more detail

3. Data Quality Management System (DQMS)
Plan your Data Quality strategy
Define goals and objectives
Data Quality Management System (DQMS)

4. How to Measure Data Quality Data Quality Management System (DQMS)
A strategic approach that enables enterprise-level governance and management of data quality to generate superior information assets.
Based upon best practices and capabilities that support increasing data quality. Organized by:
Functional Areas
Action Types
Flexible design and implementation depending on the unique requirements of an organisation
Scalable model that accommodates any size organization large or small

5. How to Measure Data Quality DQF DQMS - Capabilities
“Capability”
Ability to perform
Capacity, expertise, knowledge, or talent required to perform a task
A business capability is the expression of the ability, materials, and expertise an organization needs in order to perform core functions.
Quality
Data
People
Process
Technology

6. How to Measure Data Quality DQF DQMS – Functional Areas
Capabilities have been organised by Functional Area:
Organisational
What DQ enterprise level structure is required?
What DQ roles and responsibilities must be defined?
Policies and standards
What DQ guidelines are needed to provide governance and reference?
Business processes
What critical processes drive day-to-day operations?
Systems capabilities
What technology is necessary to support the business?

7. How to Measure Data Quality DQF DQMS – Activity Types
Within each functional area, there are four key elements or activity types:
Plan:
Strategy & Goals
Document
Policies, Process SOPs, Org, RACI
Execute
Communication, Training, Change Mgmt
Monitor/Control
Audits, Reporting
Life Cycle, Continuous Improvement

8. How to Measure Data Quality Example – DQF DQMS Capability Matrix
Organisational Capabilities
Policies & Standards
Business Process
System Capabilities
Plan
Executive sponsorship - Mission & vision; Accountable leadership; Staff roles & skill sets; Data owners & stakeholders; Data governance office.
Mission & vision; Goals & objectives; Guiding principles; Success measures; Action plans; Policy & standards management
Initial data entry & setup; Ongoing data maintenance; Processes involved in the information's life- cycle
Unified data repository; Design & architecture; Workflow, user interface; Data validations; Security, access controls; Revision/change history; External publication; Internal publication
Document
Governance organisational structure; Roles & responsibilities; Personal objectives; Reporting alignment
Mission, goals, principles and success measures; Governance model, decision process; Data definitions & standards; Security & use policy; Audit procedures; Documentation standards; Risk Management; Customer feedback policy
Operating procedures; Process flow diagrams; Job aids, work instructions; Performance metrics
System requirements; Operating procedures; Performance metrics
Execute
Education & awareness; Internal communication; Training
Education & awareness; Documentation management; Policies & standards management; Data issue management Training; Customer feedback resolution
Education & awareness; Performance management; Process issue management; Change management
*See note on section
Monitor
Organisational capability review; Review of personal objectives
Policy & standards review
Workflow controls; System validations; Performance reporting on service levels; Performance reporting on data quality; External & internal feedback; Process compliance audits; Product measurements; Review & reporting audit results; Monitor impact of erroneous data
Performance reporting on service levels

9. How to Measure Data Quality Example – DQF DQMS Documentation
Functional Area Activity Type Capability Name What – definition Why - rationale Recommendations - hints for implementation Practical Examples Self assessment - Questions linked to capability

10. How to Measure Data Quality DQF DQMS – Essential Steps
Gain top management commitment
Appoint responsible managers
Start data quality awareness programmes
Provide training
Create Data Quality Management Processes
Develop data quality management system documentation
Document controls
Implement and execute DQ program
Perform Internal data quality audit
Conduct management review
Conduct conformity assessment (Optional)
Perform continual improvement

11. Identify areas of opportunity
Assessment of capabilities and priorities
Self Assessment

12. How to Measure Data Quality Why Self-Assess?
Identify areas of improvement as part of a continuous DQ program
Engage in trading partner collaboration for process improvement
Although the final assessment results are discussed among trading partners, the execution of the self-assessments themselves is always performed by one organisation without interference or involvement from external parties.
Measure compliance with global standards or best practices
Benchmark DQ practices within organisation or external to the industry

13. How to Measure Data Quality Self-Assessment Tools
The following self-assessment tools are currently available as part of the DQF:
Questionnaire
The questionnaire contains 73 questions that relate to an organisation’s data quality management capabilities and their deployment level within the organisation. This is the core component of the self-assessment process.
Scoring Model
Indication of how many of the recommended best practices for a DQMS are in place within a given organisation.
Master Data KPIs
Performance indicators based on the monitoring and inspection of key GDSN attributes.

14. How to Measure Data Quality Self-Assessment Tools
Questionnaire KPIs, Scoring model

15. How to Measure Data Quality Self-Assessment – Scope
In defining the scope consider the following:
How will the results of this assessment support the organisation’s goals?
Factors impacting the assessment:
Product categories
Product life-cycle (e.g. new introductions vs. line items)
Brands
Specific DQ process and it’s complexity
Type of change in the data/Attributes to be assessed
Manufacturing facilities/locations
Timing
Performance goals
Greatest areas of market interest
Scope must be clearly defined, documented, and well communicated
Scope definition will determine the size and complexity of the effort – start small

16. How to Measure Data Quality Self-Assessment – Considerations
A Self-Assessment…
Is a snapshot in time
Should include a Capabilities Assessment and Process Review at a minimum
Is not be the ultimate goal; it should lead to further improvements
Needs to be carefully conducted to ensure the results are reliable
the DQ Framework and Implementation Guide provide a detailed process flow for the execution of a self-assessment (see chapter 3 in both)*
Additional implementation training is available on the DQF

17. How to Measure Data Quality Self-Assessment – Essential Steps
Decide to self-assess
Define the scope of the self-assessment
Appoint an assessment leader
Form an assessment team
Educate the organisation about the self-assessment
Baseline deployed capabilities
Apply the self-assessment questionnaire
Consolidate and analyse results
Communicate results to the organisation
Communicate results to trading partners (optional)

18. Product Inspection & KPI Model
Identify areas of opportunity
Assessment of capabilities and priorities
Product Inspection & KPI Model

19. How to Measure Data Quality Product Inspection – What is it?
Use of a standardised methodology for the physical inspection of a product to:
Verify, objectively, electronic data in comparison with a physical product or global standard
Ensure the results of the inspection are consistent and reliable
Monitor KPIs developed to give a more granular indication of the quality of an organisation’s data output
Track or Audit performance of an organisation and its data quality management system

20. How to Measure Data Quality Product Inspection – Scope
Goals and objectives of inspection
Document accuracy of the data sample
Snapshot of DQMS process
Methodology
Sample size
Information sources to be compared
Attributes to be reviewed
Product type
Category
Location of production
Target Market

21. How to Measure Data Quality Product Inspection – Essential Steps
Select inspection body
Prepare for inspection
Define scope of inspection
Identify sample
Gather documents for inspectors
KPI Model & Scorecard
GS1 Package Measurement Rules and related standards
Calibrate and Secure measuring equipment
Perform inspection
Report results
Launch appeals procedure
Document complaints
Apply corrective measures

22. Identify areas of opportunity
Assessment of capabilities and priorities
KPI Model & Scorecard
“Not everything that can be counted counts."
Albert Einstein

23. How to Measure Data Quality KPI Model – What is it?
A list of Key Performance Indicators (KPIs)
Key:
Measures a critical business processes
Supports DQ strategy
Performance Indicator: an objective measure
Internally focused and may be proprietary
Short-term
Long-term
Based on DQ Dimensions
DQ dimensions list is unique to enterprise
Accuracy - the degree in which the (electronic) product information stored in a repository is consistent with the physically observable characteristics of the trade item.

24. How to Measure Data Quality DQF KPI Model
DQF KPI model was defined to:
Provide trading partners with a neutral, common set of KPIs for data accuracy
Cover the most commonly synchronised attributes across all regions
Offer a basic structure to validate the effectiveness of data quality management systems deployed within an organisation
DQF KPI model includes the following:
Overall item accuracy
Generic attribute accuracy
Dimension and weight accuracy
Hierarchy accuracy
Active/Orderable
Target % is a business decision

25. Data quality KPI scorecard

26. How to Measure Data Quality DQF KPI Model – When to use it
The DQF KPI model may be used for any of the following scenarios:
As a means to report the results of product audits:
the KPI model is a good way to offer structured results of product audits as it proposes a logical way to group related attributes.
As a benchmark:
the KPI model may be used as a reference to compare the accuracy of the data performance of two different entities.
To track progress on improvements:
the KPI model can be also used to compare the progression of data accuracy within the organisation by striving always to improve the results obtained every time the KPIs are measured.

27. How to Measure Data Quality DQF KPI Model – How to use it
Customise KPI model
Select KPI categories relevant to organisation
Add or remove attributes to/from the categories
Add specific performance targets
Conduct product inspection
Analyse using the KPI Scorecard
Report results

28. Implement improvements
Execute, document and take action
Implementation plan

29. How to Measure Data Quality Product Inspection – Results
Communicate results
Prioritize actions
Identify the data issues causing the most negative impact on the organisation
Identify and adjust the processes and capabilities required to correct the negative impact
Develop process and capacities to prevent reoccurrence
Define realistic timeframes for execution
Dependent on the extent and complexity of the changes
Communicate Action Plan

30. How to Measure Data Quality DQF Phased Implementation
Organisational Capabilities
Policies & Standards
Business Process
System Capabilities
Plan
Executive sponsorship - Mission & vision; Accountable leadership; Staff roles & skill sets; Data owners & stakeholders; Data governance office.
Mission & vision; Goals & objectives; Guiding principles; Success measures; Action plans; Policy & standards management
Initial data entry & setup; Ongoing data maintenance; Processes involved in the information's life-cycle
Unified data repository; Design & architecture; Workflow, user interface; Data validations; Security, access controls; Revision/change history; External publication; Internal publication
Document
Governance organisational structure; Roles & responsibilities; Personal objectives; Reporting alignment
Mission, goals, principles and success measures; Governance model, decision process; Data definitions & standards; Security & use policy; Audit procedures; Documentation standards; Risk Management; Customer feedback policy
Operating procedures; Process flow diagrams; Job aids, work instructions; Performance metrics
System requirements; Operating procedures; Performance metrics
Execute
Education & awareness; Internal communication; Training
Education & awareness; Documentation management; Policies & standards management; Data issue management Training; Customer feedback resolution
Education & awareness; Performance management; Process issue management; Change management
*See note on section
Monitor
Organisational capability review; Review of personal objectives
Policy & standards review
Workflow controls; System validations; Performance reporting on service levels; Performance reporting on data quality; External & internal feedback; Process compliance audits; Product measurements; Review & reporting audit results; Monitor impact of erroneous data
Performance reporting on service levels

31. Continuous Improvement
Monitor changes and plan further improvements
Start a cycle of continuous improvement
Continuous Improvement

32. How to Measure Data Quality Monitoring & Continuous Improvement
Ensuring DQ is a continuous, dynamic, day-to-day activity.
Must be Integrated with the product life-cycle
Create, Syndicate, Maintain, Archive, Purge
does not end when a product is published to trading partners
data must be maintained and updated
Periodical audits (both of processes and data) are necessary to monitor progress.
Most importantly, it needs to ensure that there is good control and monitoring on performance in order to ensure processes remain in optimal condition.
Best Practice - “GDSN Trade Item Implementation Guide” Chapter 11: Item Futurisation
and sustain the corrective action
That is why ongoing monitoring is the key to creating sustainability and ensuring a continuous level of quality.

33. How to Measure Data Quality Summary – Elevator Pitch
The DQF is adaptable and scalable and may be applied whole or in part – as required by the organisations goals & objectives
DQF as part of a continuous cycle:
Planning & Strategy
Opportunity identification
Corrective Action & Implementation
Continuous Monitoring
DQF Components
Elements for Data Quality Management System
Model for a self-assessment
Model for a product inspection
Model KPIs & Scorecard
Recommendations for Monitoring & Continuous Improvement

34. The Data Quality Framework PACKAGE is publically available
All you need to use the Framework in one package
Includes:
The Data Quality Framework v3.0
Implementation Guides (user’s manual!)
Automated scorecard for self-assessment
Automated scorecard for KPIs
Data Quality Introductory Presentation
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