Book Image

Data Quality in the Age of AI

By : Andrew Jones
Book Image

Data Quality in the Age of AI

By: Andrew Jones

Overview of this book

As organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.
Table of Contents (13 chapters)

Assigning roles and responsibilities

It’s only by being clear about roles and responsibilities that diverse groups of people can work together effectively and efficiently to realize the goal of extracting the most business value from data. Let’s define the roles of the data generator and the data consumer.

Data consumers

Often, people only think of data consumers as a data practitioner, for example, a data engineer, a BI analyst, or a data scientist. The primary tasks of these professionals require them to consume and work with data, and as such, they are highly reliant on the quality and reliability of that data. But they are not the only data consumers in your organization.

While data consumers cannot be responsible for the quality of the data they use, they do play a major role in shaping that data. They need to be able to articulate their requirements to the data producers and demonstrate the value they can generate through the application of data. They...