Book Image

Driving Data Quality with Data Contracts

By : Andrew Jones
Book Image

Driving Data Quality with Data Contracts

By: Andrew Jones

Overview of this book

Despite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.
Table of Contents (16 chapters)
1
Part 1: Why Data Contracts?
4
Part 2: Driving Data Culture Change with Data Contracts
8
Part 3: Designing and Implementing a Data Architecture Based on Data Contracts

Assigning responsibility for data governance

To implement effective data governance in our organization, we need to be clear on the roles and responsibilities involved. In this section, we’ll define those roles and responsibilities and how they work together.

We will cover this in the following subsections:

  • Responsibilities of the data generators
  • Introducing a data architecture council
  • Working together to implement federated data governance

Responsibilities of the data generators

By using a data contracts-backed architecture, we promote a more decentralized operating model. We give data generators the autonomy and responsibility to own and manage their data, supported by the right self-served tooling and guardrails.

Consequently, we need to rethink our approach to data governance. We don’t want to create a central team to try to take control of the data. These teams become a bottleneck, slowing down access to and use of data. They also...