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)

Unlocking AI’s potential with data

Recent advances in AI and increased accessibility to machine learning models that power them have got many organizations excited about how they can be applied to create more value. In fact, in a recent survey by Dataiku and Databricks, 64% of respondents said that they were “likely” or “very likely” to use generative AI for their business over the next year,2 while a report from Segment found that 92% companies are using AI-driven personalization to drive business growth.3

However, AI is only as good as the data behind it. No amount of tuning a model will help if the data is of poor quality, which is why the same survey from Dataiku and Databricks identified lack of quality data as the primary obstacle to generating value. An inferior model with superior data will always outperform a superior model with inferior data.

There are other data issues that can affect...