This document discusses data quality management. It states that data quality is critical for organizational change management and poor data quality results in inaccurate information and poor business performance. It also notes that data quality is a long-term program, not a short-term project. The document provides examples of data models and interfaces and discusses reasons for lack of quality in models like cost, timelines, and culture. It also lists dimensions of data quality such as accuracy, completeness, consistency and validity.

1. Copyright 2014 by EPI-USE Data Services
Data Quality for Data Modellers
Sue Geuens CDMP, MDQM
October 2014

2. Data Quality Management is a critical support
process in organisational change management
Data Quality is synonymous with information
quality, since poor data quality results in
inaccurate information and poor business
performance
Data Quality is a LONG TERM
Program, not a SHORT TERM project
Copyright 2014 by EPI-USE Data Services

3. Data Quality is … and isn’t …
Copyright 2014 by EPI-USE Data Services
• Supposed to improve your
data
• Required to ensure that reports
have appropriate output
• Needs to enable your
executives to make the correct
decisions
• Must be assessed before any
migration/ integration project
• DOCUMENTED
• A once off instance of
cleansing a piece of data
• Supposed to fix the errors
created by incorrect data
modelling
• Going to improve without
concerted effort
• GUNG HO effort that dies

4. Interface Examples
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8. What does Dilbert say?
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9. Data Model Examples
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13. Reasons for No Quality in Models
• Cost
• Timelines
• Access to Data
• Culture
• Metadata
• Over Optimistic on current model
• Measures
• Business Process does not require Quality
• Data Flows
• Not in Your Scope
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14. What is your Data Quality Maturity Rating?
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Dimensions of Quality
• Accuracy
 Degree to which data correctly represents “real-life” entities
• Completeness
 Level of assigned data values that are required by business, system, application
• Consistency
 Applies to ensuring data sets across systems are consistent and/ or not in conflict
• Currency
 How “fresh” is the data compared to length of time last refreshed
• Precision
 Level of detail in the data element requiring specific accuracy
• Privacy
 Need for access control and usage monitoring
• Reasonableness
 Consider consistency expectations in systems and applications
• Referential Integrity
 Level to which data is related across database tables and columns
• Timeliness
 Availability of data for use and ease of accessibility
• Uniqueness
 The level to which the data entity is unique in the data set
• Validity
 Conformance to data element attributes, may be specific to database, system and/ or application
 Permissable Purpose