Preface

Practical Data Quality is about how to take your organization from a basic awareness of a data quality problem to a position of having data good enough to truly underpin success.

The book begins by explaining how bad data can affect an organization’s process efficiency, decision-making, and ability to remain compliant. It then establishes the key concepts you need to understand to be successful with data quality and the end-to-end process I have used to transform data throughout my career.

The book goes on to explain each step of the data quality journey, starting with creating a business case and managing the hectic period at the start of an initiative. Then the book establishes the typical stakeholders you will need to engage with through the process, how to work with them to identify which data to focus on, and the specific rules that the data should comply with.

Next, it shows how to monitor data against the rules that have been established and how to actually start correcting the data.

To close, the book explains how to embed good data quality practices into the day-to-day activities of your organization and outlines best practices and challenges to be avoided in your work.

By the end of the book, you will have a complete outline of how you can transform data quality in your organization, armed with examples to catch the interest of your stakeholders, and templates to accelerate your work.

Who this book is for

The book is aimed at anyone intending to improve data quality in their organization. The book outlines the basics of data quality for people new to the topic, but provides insights into every step of the data quality life cycle, using real-world examples and templates to accelerate progress. Typical readers are business leaders, such as chief operating officers or chief executive officers, who see data adversely affecting their success and data teams, such as analytics or governance teams who want to optimize their data quality approach.

What this book covers

Chapter 1, The Impact of Data Quality on Organizations, explains the importance of data quality and defines what is meant by bad data.

Chapter 2, The Basics of Data Quality, explains key data quality concepts, including the typical roles involved, the data quality improvement cycle, and the overall fit of a data quality initiative into a wider data management program and organization.

Chapter 3, The Business Case for Data Quality, explains how to calculate the costs and benefits of a data quality initiative, combining these with qualitative matters into a compelling business case for funding.

Chapter 4, Getting Started With a Data Quality Initiative, identifies the activities which are required immediately after a business case approval, such as supplier and tool selection, hiring, early remediation activities and planning. It provides a framework to ensure that all these activities make progress at the required rate early on.

Chapter 5, Data Discovery, explains how to understand business strategy and how it links to data, processes, and analytics. Once this is understood, the chapter explains how to perform a data profile and interpret the results to derive the first data quality rules.

Chapter 6, Data Quality Rules, explains how to derive a full set of business data quality rules, covering all the key elements including defining rule scope, thresholds, dimensions, and weightings. Well developed rules identify the data which does not meet the required standard efficiently and in a repeatable fashion.

Chapter 7, Monitoring Data Against Rules, outlines the various dashboards and reports required to efficiently and effectively monitor data quality against business rules.

Chapter 8, Data Quality Remediation, explains how to use the data quality dashboards and reports to prioritize and then deliver data quality improvement activities.

Chapter 9, Embedding Data Quality into Organizations, describes how to ensure that data quality improvement does not finish when the active initiative ends, , by ensuring it becomes part of day-to-day business practices.

Chapter 10, Best Practices and Common Mistakes, outlines the key best practices for a successful data quality initiative and the common mistakes that reduce the effectiveness of the work. The book ends with an analysis of how new technology such as generative AI will impact work in this field.

To get the most out of this book

You should have a basic understanding of how businesses operate, including the following:

• Awareness of how organizations are structured, including different departments and organizational practices
• Awareness of key processes in organizations such as procure to pay or order to cash
• Awareness of key systems in organizations such as ERP systems and CRM systems
• Awareness of data management concepts such as master data management, data ownership, and stewardship

Templates and diagrams

We have made some templates and diagrams available in the book’s GitHub repository here: https://github.com/PacktPublishing/Data-Quality-in-Practice.

The content included is as follows:

File name	Description
Chapter 1 – Data Governance versus Process Speed Diagram (Figure 1.3	A diagram used in the book that people may wish to tailor to their own presentations.
Chapter 2 – Business Case Template (Figure 2.3)	A template created for the book to show how you can provide quantitative calculations for your data quality initiative.
Chapter 2 – Typical One-Page Plan (Figure 2.1)	A one-page plan template that could be used as a starting point.
Chapter 6 – Report Hierarchy Diagram (Figure 6.2)	A diagram used to show how the various data quality dashboards relate to one another. This could be used in a presentation to generate ideas and feedback.
Data Quality Dashboards v2	Power BI reports developed to support the book in the monitoring chapter. To open this file, you will need to download Power BI Desktop from Microsoft (for free). Please note, in Power Query, the path to the source data file was removed for security reasons. Please do not apply changes (that is, use Apply Later when you open the report). If you use Apply Now, the data will disappear from the report and it will no longer be possible to explore it.
Data Quality Remediation Prioritization v	Another Power BI report – this time showing the prioritization work for the remediation chapter. The same notes apply as for Data Quality Dashboards v2 – the source file link was removed, so changes should not be applied.

Tips or important notes

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Get in touch

Feedback from our readers is always welcome.

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