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)

Defining data quality

The question of quality data, and the consequences of using it, is a topic as old as data itself. And yet, as old a problem as it is, data quality is often hard to define. There are many definitions being used, often interchangeably. Some emphasize different dimensions of the data itself, like its completeness, accuracy, and timeliness. But these dimensions are not sufficient to tell whether the data is of good quality.

Other definitions consider whether data is fit for purpose—which is rather broad—but it’s a better definition and the one that’s naturally used every time one looks at a dataset. When determining whether data is fit for purpose, one should also ask whether they trust the data enough to make a decision, take an action, or build on top of it.

Organizations today face significant challenges in extracting business value due to poor data quality. By being deliberate about how the business creates, manages, and provides...