Practical Data Quality : Learn Practical, Real-World Strategies to Transform the Quality of Data in Your Organization / Robert Hawker and Nicola Askham.

Format:

Book

Author/Creator:

Hawker, Robert, author.

Askham, Nicola, author.

Language:

English

Subjects (All):

Database management.

Database design.

Electronic data processing--Management.

Electronic data processing.

Information technology--Management.

Information technology.

Physical Description:

1 online resource (318 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Summary:

Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-making Key Features Get a practical explanation of data quality concepts and the imperative for change when data is poor Gain insights into linking business objectives and data to drive the right data quality priorities Explore the data quality lifecycle and accelerate improvement with the help of real-world examples Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Explore data quality and see how it fits within a data management programme Differentiate your organization from its peers through data quality improvement Create a business case and get support for your data quality initiative Find out how business strategy can be linked to processes, analytics, and data to derive only the most important data quality rules Monitor data through engaging, business-friendly data quality dashboards Integrate data quality into everyday business activities to help achieve goals Avoid common mistakes when implementing data quality practices Who this book is for This book is for data analysts, data engineers, and chief data officers looking to understand data quality practices and their implementation in their organization. This book will also be helpful for business leaders who see data adversely affecting their success and data teams that want to optimize their data quality approach. No prior knowledge of data quality basics is required.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Foreword

Contributors

Table of Contents

Preface

Part 1 - Getting Started

Chapter 1: The Impact of Data Quality on Organizations

The value of this book

Importance of executive support

Detailed definition of bad data

Bad data versus perfect data

Impact of bad data quality

Quantification of the impact of bad data

Impacts of bad data in depth

Process and efficiency impacts

Reporting and analytics impacts

Compliance impacts

Data differentiation impacts

Causes of bad data

Lack of a data culture

Prioritizing process speed over data governance

Mergers and acquisitions

Summary

References

Chapter 2: The Principles of Data Quality

Data quality in the wider context of data governance

Data governance as a discipline

Data governance tools and MDM

How data quality fits into data governance and MDM

Generally accepted principles and terminology of data quality

The basic terms of data quality defined

Data quality dimensions

Stakeholders in data quality initiatives

Different stakeholder types and their roles

The data quality improvement cycle

Business case

Data discovery

Rule development

Monitoring

Remediation

Embedding into BAU

Chapter 3: The Business Case for Data Quality

Activities, components, and costs

Activities in a data quality initiative

Early phases

Planning and business case phase

Developing quantitative benefit estimates

Example - the difficulty of calculating quantitative benefits

Strategies for quantification

Developing qualitative benefits

Surveys and focus groups

Outlining data quality qualitative risks in depth

Anticipating leadership challenges

The "Excel will do the job" challenge.

Ownership of ongoing costs challenge

The excessive cost challenge

The "Why do we need a data quality tool?" challenge

Chapter 4: Getting Started with a Data Quality Initiative

The first few weeks after budget approval

Key activities in those early weeks

Understanding data quality workstreams

Workstreams required early on

Identifying the right people for your team

Mapping resources to the workstreams

Part 2 - Understanding and Monitoring the Data That Matters

Chapter 5: Data Discovery

An overview of the data discovery process

Understanding business strategy, objectives, and challenges

Approaches to stakeholder identification

Content of stakeholder conversations

The hierarchy of strategy, objectives, processes, analytics, and data

Prioritizing using strategy

Linking challenges to processes, data, and reporting

Basics of data profiling

Typical tool data profiling capabilities

Using these capabilities

Connecting to data

Chapter 6: Data Quality Rules

An introduction to data quality rules

Rule scope

The key features of data quality rules

Rule weightings

Rule dimensions

Rule priorities

Rule thresholds

Cost per failure

Implementing data quality rules

Designing rules

Building data quality rules

Testing data quality rules

Chapter 7: Monitoring Data Against Rules

Introduction to data quality reporting

Different levels of reporting

Data security considerations

Designing a high-level data quality dashboard

Dimensions and filters

Designing a Rule Results Report

Typical features of the Rule Results Report

Designing Failed Data Reports

Typical features of the Failed Data Reports

Re-using Failed Data Reports

Multiple Failed Data Reports

Exporting Failed Data Reports.

Managing inactive and duplicate data

Managing inactive data

Managing duplicate data

Detecting duplicates

Presenting findings to stakeholders

Launching data quality reporting successfully

Embedding reports into governance

Part 3 - Improving Data Quality for the Long Term

Chapter 8: Data Quality Remediation

Overall remediation process

Prioritizing remediation activities

Revisiting benefits

Approach to determining priorities

Identifying the approach to remediation

Typical remediation approaches

Matching issues to the correct approach

Moving remediation to business as usual

Understanding the effort and cost

Types of cost in remediation

Governing remediation activities

Key governance activities

Tracking benefits

Quantitative example

Qualitative benefit tracking

Chapter 9: Embedding Data Quality in Organizations

Preventing issue re-occurrence

Methods to prevent re-occurrence

The ongoing impact of human error

Short-horizon reporting

Ongoing data quality rule improvement

Strategies to identify rule changes

Updating data quality rules

Transitioning to day-to-day remediation

Requirements for success

Planning for a successful transition

Indications that the transition has been successful

Continuing the data quality journey

Roadmap of data quality initiatives

Identifying the next initiative

Obtaining support

What if no further initiative is sanctioned?

Chapter 10: Best Practices and Common Mistakes

Best practices

Selecting the best practices

Manage data quality primarily at the source

Implementing supporting governance meetings

Including data quality in an organization-wide education program

Leveraging the data steward and producer relationship

Best practices throughout this book.

Common mistakes

Failure to implement best practices

A lack of practicality

Technically driven data quality rules

One-off remediation activity

Restricting access to data quality results

Avoid silos in data quality work

The future of data quality work

LLMs

Greater emphasis on high-quality data in organizations

Index

About Packt

Other Books You May Enjoy.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781804619438

1804619434

OCLC:

1401094982