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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Devashish Bharti Dec 02, 2023

In this AI centric world, people often forget that data quality needs to be spot on to add true business value. This book not only provides a solid foundation but also gives real life examples along the way to keep the story real. Kudos to Rob for adding real flavour to this topic!

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NB Dec 30, 2023

I have had the pleasure of working with Rob. What stands out is his energy and passion. That energy and passion are evident in this book. I have been in the Information Technology field for 35 years. Data quality has been a topic in all of those years. The focus has always been around finding the data, ingesting the data, and presenting the data. There is an old saying that is still relevant today “Garbage in, garbage out”. In this book, Rob addresses this issue with the focus being on good quality data and the governance to keep that data in good quality shape. I unfortunately have seen a lot of bad business decisions made based on poor data. Rob talks about this as well and has sections focused on data rules. What I loved to see in this book was sections on stakeholder engagement amd buy-in. I really enjoyed the section on “Excel”. A must read. I have witnessed more data quality mistakes made in Excel than any other software. This book is both an easy read as well as a relevant one. I would recommend it to those just getting started on their data quality journey as well as those who have been on that journey and are still struggling.

Kiwik Dec 04, 2023

This book is an excellent guide to understand the practical aspects of data quality and the criticality of it in organizations. For many years “big data” and now generative AI promise major insights for organizations, yet too often the return on investment has been less than hoped for, often driven by a lack of data quality. Leaders need to understand how challenging the journey to clean and useful is and not expect it to “just be done”. This book illuminates practical considerations on how to align on this journey and invest more wisely to this end. The book is both well organized and its content imparted by the author in a manner that is engaging and down-to-earth. Highly recommended.

Chazzak Dec 02, 2023

I have worked with some people that refuse to store files with names in year-month-day format, let alone duplicating data left, right and centre - I find it mind blowingly frustrating to the point that my old boss bought me an "I <3 clean data" t-shirt!This book highlights the importance of good data for every business, and how that starts with a data culture that abides by data rules decided upon and set by the organisation. It explains how best to report your data quality findings to stakeholders and then how to implent the fix and adhere to best practices going forwards so as to maintain this higher level of quality.It does make me wish I had more sway further up the chain in my company, but for now at least I can lead by example in my small area and shove this data bible under the nose of those that defy it, but really, the resultant quality of reporting speaks for itself.

Peter O'Kelly Oct 22, 2023

Practical Data Quality provides an in-depth review of the data quality domain, including how the domain overlaps with data governance, data catalogs, and master data management. The book provides detailed guidance on structuring and executing data quality programs, including definitions of related roles and responsibilities. It also includes extensive examples of data quality assessments based on widely used tools such as Microsoft Power BI, and provides examples of leading products for automating and monitoring data quality concerns.The book delivers on its title assertion of being practical in several respects. It provides, for example, detailed examples of building a business case for data quality initiatives along with cost estimation models. It also provides guidance on formulating and executing data quality remediation programs, which are likely to be a pragmatic necessity for most large organizations.The ideal target audience for the book is people focused on data quality starting in enterprise app categories such as CRM and ERP (SAP-based scenarios, for example, are used in several sections). The book does not directly provide guidance on more modern data stack-focused data quality topics such as data quality testing in dbt, but most of the guidance in the book is broadly applicable, especially the final chapter on best practices and common mistakes. The final chapter also offers perspectives on the future of data quality tools and activities, including the emerging role for the use of large language models in data quality-related tools.

Practical Data Quality: Learn practical, real-world strategies to transform the quality of data in your organization

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