Book Image

Practical Data Quality

By : Robert Hawker
Book Image

Practical Data Quality

By: Robert Hawker

Overview of this book

Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating. Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives. By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.
Table of Contents (16 chapters)
Part 1 – Getting Started
Part 2 – Understanding and Monitoring the Data That Matters
Part 3 – Improving Data Quality for the Long Term


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


architectural review board (ARB) 238

artificial intelligence (AI)-driven large language models (LLMs) 277

Attacama DQ Analyzer tool

reference link 143

Azure Active Directory group 156


bad data

detailed definition 5, 6

versus perfect data 6, 7

bad data, causes 16

lack, of data culture 16

merger and acquisition scenarios 18

process speed, prioritizing over data governance 16, 17

bad data quality, impact 7

analytics impact 12, 13

compliance impacts 13-15

data differentiation impacts 15

efficiency impact 9-11

process impact 9-11

qualitative 9

quantification 8, 9

reporting impact 12, 13

best practices 257, 270, 271

data quality, including in organization-wide education program 264, 265

data quality, managing at source 258-260...