Enterprise Data World Webinars: Data Quality for Data Modelers

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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.

Technologie

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

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 …
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

Interface Examples
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

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

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

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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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

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

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

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