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

What is a data management maturity model?

A data management maturity model is a measurement framework used to assess the overall maturation of an organization’s management of its data. Said another way, it measures how well the company is managing data. The assessment provides a score by category, provides the aggregate maturity level of the company, and identifies areas for improvement. The maturity assessment is subjective. There are a series of ways to minimize the degree of subjectivity, which I will outline over the next several pages. Data management maturity models are broken down into different categories, and further broken down into levels of maturity against each of the categories, which helps minimize, but does not eliminate, the subjectivity.

A very simple (non-data) example would be to measure how grey something is:

Figure 4.1 – Visual example of the degree of grey

Figure 4.1 – Visual example of the degree of grey

In this example, none of these colors are wrong, they are...