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)

Incentivizing data producers

For data producers to take on the responsibility of providing better quality data—and to do it well—they need to have incentives that align with their work. There are many ways to do this. Every organization that has grown beyond a start-up needs a way of incentivizing multiple teams to work together to build something of value for the business.

One effective approach is a top-down strategy. By aligning strategic objectives that rely on the creation and utilization of high-quality data, organizations can optimize their structure to facilitate collaboration among relevant teams. Additionally, employing key performance indicators (KPIs) and other prioritization methods can ensure that teams are held responsible for their contributions toward achieving this goal.

For example, if you’re tracking data incidents, you could create KPIs around the number of incidents, their severity, and how common the root causes are. While you will...