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)

DoorDash

Today, DoorDash is the largest food delivery platform in the US, but that hasn’t always been the case. In January 2018, DoorDash had just 17% share of a super-competitive market, competing against many well-funded competitors.

Food delivery is a low-margin business, so not only did they need to increase market share, but they also needed to increase profitability on every order.

By October 2020, DoorDash had achieved 50% market share, and much of their success has been attributed to their investment in data quality, data platforms, and AI.

DoorDash has invested heavily in a data platform11 with a focus on:

  • Reliability, quality, and SLAs: DoorDash has recognized the importance of detecting and monitoring the quality of their data and catching any problems as early as possible. This reduces the time to recovery and the associated costs—particularly when dealing with data at scale.
  • Prioritizing trust in data: DoorDash sees...