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

How to determine which type of automation to use

There is a fairly straightforward process to identify your needs, select the right tools and techniques, and implement them effectively. We will walk through exactly how to determine which type to use and how to execute the process for your company.

Step 1 – Identify your goals

Start by exploring the problem. A few key questions to ask include the following:

  • What are you trying to achieve?
  • What is working well and needs to be retained?
  • What are the current challenges?
  • What outcomes would indicate a successful solution?

These exploratory questions can help you determine if you need to increase efficiency, improve data quality, or if you are looking for a deeper result, such as finding deeper insights or meeting regulatory expectations. Perhaps you uncover that you are spending far too much of your team members time on low-value work, such as manual data cleaning, or you are struggling to identify...