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
Copyright 2014 by EPI-USE Data Services

5.
Copyright 2014 by EPI-USE Data Services

6.
Copyright 2014 by EPI-USE Data Services

7.
Copyright 2014 by EPI-USE Data Services

8.
What does Dilbert say?
Copyright 2014 by EPI-USE Data Services

9.
Data Model Examples
Copyright 2014 by EPI-USE Data Services

10.
Copyright 2014 by EPI-USE Data Services

11.
Copyright 2014 by EPI-USE Data Services

12.
Copyright 2014 by EPI-USE Data Services

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
Copyright 2014 by EPI-USE Data Services

14.
What is your Data Quality Maturity Rating?
Copyright 2014 by EPI-USE Data Services

15.
Copyright 2014 by EPI-USE Data Services
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