Book Image

Data Governance Handbook

By : Wendy S. Batchelder
Book Image

Data Governance Handbook

By: Wendy S. Batchelder

Overview of this book

2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders.
Table of Contents (24 chapters)
1
Part 1:Designing the Path to Trusted Data
7
Part 2:Data Governance Capabilities Deep Dive
14
Part 3:Building Trust through Value-Based Delivery
20
Part 4:Case Study

Core capabilities

Data architecture needs to be more flexible and dynamic than ever before. Companies are evolving quickly, and the speed at which generative AI (GenAI) has emerged is a perfect example of the ever-changing needs of corporations. You will note that the capabilities presented next are not specifying which technologies you need to deploy. That is up to you and your stakeholders to decide. The following capabilities tell what you need to define as a part of your data architecture program.

Establishing a data architecture program

Ideally, data architects should report to your chief data and analytics office. In some organizations, you will see the data architecture function report to the chief architect; however, in most cases, this results in data architects being less intimately familiar with the objectives of the company’s data strategy and results in less data-friendly models. It can work well when your chief architect understands data complexities well...