Data Quality in the Age of AI Building a foundation for AI strategy and data culture

Key Features
Gain a profound understanding of the interplay between data quality and AI
Explore strategies to improve data quality with practical implementation and real-world results
Acquire the skills to measure and evaluate data quality, empowering data-driven decisions
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
Discover actionable steps to establish data quality as the foundation of your data culture
Enhance data quality directly at its source with effective strategies and best practices
Elevate data quality standards and enhance data literacy within your organization
Identify and measure data quality within the dataset
Adopt a product mindset to address data quality challenges
Explore emerging architectural patterns like data mesh and data contracts
Assign roles, responsibilities, and incentives for data generators
Gain insights from real-world case studies
Who this book is for
This report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Table of Contents
Executive Summary
Introduction: Understanding data quality
Unlocking AI's potential with Data
Improving data quality at the source
Case studies: Positive impact of data quality
Cultivating a data culture that values quality
Conclusion: Embracing a quality-driven data culture

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