Enterprise Data World Webinars: Data Quality for Data Modelers
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Transcript of Enterprise Data World Webinars: Data Quality for Data Modelers
Copyright 2014 by EPI-USE Data Services
October 2014
Data Quality for Data ModellersSue Geuens CDMP, MDQM
Copyright 2014 by EPI-USE Data Services
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
Data Quality is … and isn’t …
• 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
Copyright 2014 by EPI-USE Data Services
Interface Examples
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
What does Dilbert say?
Copyright 2014 by EPI-USE Data Services
Data Model Examples
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
Copyright 2014 by EPI-USE Data Services
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
What is your Data Quality Maturity Rating?
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