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)

High cost of poor data quality

There have been many surveys and reports that try to quantify the cost of poor data quality to an organization. Quintly estimates poor data costs an average loss of $15 million per year, while IBM estimated it cost US businesses $3.1 trillion in 2016 alone.6 Meanwhile, in a recent survey commissioned by Monte Carlo, more than half of the respondents reported that poor data quality is impacting 25% or more of their revenue, while the average from all respondents was 31%.7

$15 million Average losses incurred due to poor data quality

25% Or more revenue impacted by poor data quality

These findings outline both the direct and indirect ramifications of poor data quality. Direct costs are those that have an impact in the short term and include the breakdown of an operational process, the downtime of a data-driven product, and the loss of productivity for employees. Indirect costs are those that have an impact over the long term, impacting decision...