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)

Improving data quality at the source

An Experian report conducted in 2021 found that 95% of business leaders reported a negative impact on their business due to poor quality data.10 This underscores the necessity for proactive measures to improve the quality of the data.

95% business leaders report negative impact to business due to poor data quality

Data quality can only be improved at source. If the data source fails to capture information accurately, rectifying it later becomes futile. Similarly, inaccessible data sources can affect user access. If data is delivered infrequently, its timeliness cannot be retroactively improved. Likewise, if data sets are incomplete at the source, there’s nothing you can do to make them complete later.

You can try to work around some of these data quality issues downstream, typically in your data pipelines. For example, you could impute missing values using averages, the most common values, or machine learning algorithms, but...