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

Data architecture defined

Data architecture refers to the design and structure of an organization’s data systems, including databases, data storage, data integration, and data management processes. It is a critical component of an organization’s overall IT infrastructure and plays a crucial role in how data is collected, stored, processed, and utilized within the organization. When building a home, blueprints serve as a plan for how the home is built. Similarly, in data governance, the data architecture serves as a blueprint for how data is built. Data architecture encompasses the design, development, and governance of data systems and processes.

Simple wins

When designing a data architecture, it is important to keep the guiding principle of simplicity in mind. A complex data architecture can be difficult to maintain and manage and can make it difficult to derive insights from data. A simple data architecture, on the other hand, is easier to understand and use and...