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

Who is a consumer, and who is a generator?

We’ve spoken a lot about the consumers and generators of data in this book, but what exactly do people in these roles do? What do they care about? What are their requirements, and what are their expectations?

In the following subsections, we’ll look at both roles in more detail, starting with the data consumers.

Data consumers

A data consumer is a person, a team, or a service that consumes data to inform and/or take some action. Typically, we think of data consumers as a data practitioner – for example, a data engineer, a BI analyst, or a data scientist. Their primary tasks require them to consume and work with data, and as such, they are highly reliant on the quality and dependability of that data.

However, they are not the only data consumers in your organization. There are an increasing number of people who are not data practitioners but are data literate. They are comfortable using a data analysis tool...