Best books for Data Governance Managers

Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program

by John Ladley · 2012

~310 pages~8 hrs to read

A full walkthrough of setting up a governance programme: roles, policies, operating model and how to get it funded. The best single place to learn what the job involves.

DAMA-DMBOK: Data Management Body of Knowledge

by DAMA International · 2017

~590 pages~15 hrs to read

The reference text for the data management profession, with chapters on governance, quality, metadata, privacy and architecture. Read it in sections to learn the shared vocabulary of the field.

Getting in Front on Data: Who Does What

by Thomas C. Redman · 2016

~190 pages~5 hrs to read

A short, practical argument that data quality problems are fixed at the source by clear responsibilities. Useful when you are defining data owner and steward roles.

Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness

by Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown · 2021

Covers governance across the data lifecycle: classification, lineage, quality, access control and compliance, with cloud tooling in mind. Good when you are moving from policy documents to controls in real platforms.

Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

by Barr Moses, Lior Gavish, Molly Vorwerck · 2022

Explains how to measure, monitor and fix data quality across pipelines, including observability and incident response. Fits the part of the role that decides whether AI training and input data can be trusted.

Data Privacy: A Runbook for Engineers

by Nishant Bhajaria · 2022

Shows how to classify personal data, build privacy controls into systems and handle deletion and access requests at scale. Read it when you need to turn privacy requirements into work engineering teams can ship.

Practical Data Privacy: Enhancing Privacy and Security in Data

by Katharine Jarmul · 2023

Introduces privacy-enhancing techniques such as anonymization, differential privacy and encrypted computation, with an eye to machine learning. Helps you judge which protections are realistic for AI training data.

Fundamentals of Data Engineering: Plan and Build Robust Data Systems

by Joe Reis, Matt Housley · 2022

A tour of how data is generated, stored, transformed and served, with security and data management treated as part of the lifecycle. Gives governance staff enough technical grounding to work with data engineers.

Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric

by Piethein Strengholt · 2020

~400 pages~10 hrs to read

Looks at governance, metadata, lineage and ownership in large organisations with many data domains. Suited to someone designing a federated model across business units.

Data Mesh: Delivering Data-Driven Value at Scale

by Zhamak Dehghani · 2022

~410 pages~10 hrs to read

Sets out an approach that gives domain teams ownership of data products under shared governance rules. Read it when you must decide how much to centralise and how much to push to teams.

Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence

by Bernard Marr · 2017

~200 pages~5 hrs to read

Connects data assets to business strategy, including collection, analysis and the ethics of use. Useful when you present governance as an enabler to executives rather than a cost.