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)

Adopting a product mindset

Encouraging data producers to adopt a product mindset is key to ensuring high-quality data. While data products are often meant for internal use, the principles of product thinking are applicable across various domains, such as developer tooling, which your organization might already be familiar with.

Before creating a data product, it’s essential to understand why your organization requires it, who will benefit from it, and what they need and expect from it. These insights stem from data consumers who hold a clear understanding of the business challenges they aim to address through the data product. Articulating these needs to the data producers is vital. Knowing these needs, data producers can appreciate the business value they create by providing this product, giving them a sense of ownership of that value.

These data products should be useful by themselves for driving data-driven applications and services, for example, dashboards and analytics...