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Data Quality in the Age of AI
Data Quality in the Age of AI

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

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Profile Icon Andrew Jones
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₱2142.99 ₱3061.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
eBook Aug 2024 50 pages 1st Edition
eBook
₱2142.99 ₱3061.99
Paperback
₱3827.99
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Arrow left icon
Profile Icon Andrew Jones
Arrow right icon
₱2142.99 ₱3061.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
eBook Aug 2024 50 pages 1st Edition
eBook
₱2142.99 ₱3061.99
Paperback
₱3827.99
Subscription
Free Trial
eBook
₱2142.99 ₱3061.99
Paperback
₱3827.99
Subscription
Free Trial

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Data Quality in the Age of AI

Executive summary

Organizations worldwide are eager to capitalize on recent advancements in AI and harness its newly available capabilities. However, even the most sophisticated models require high-quality data to be effective. This report provides practical and actionable steps to enhance your data quality, a crucial factor in the age of AI.

This definitive resource delves into the following key areas:

  • Understanding data quality: Learn to define, assess, and measure your data quality, providing a clear view of your current state and areas for improvement.
  • Improving data quality at source: Gain practical advice for enhancing data quality during its creation, where it is most cost-effective and impactful.
  • Case studies: Explore real-world examples that demonstrate the significant positive impact of focusing on data quality within organizations.
  • A culture that values quality: Foster a data culture that prioritizes quality, treats data as a product, and embeds data...

Understanding data quality

Organizations everywhere are looking to refresh their data strategies to take advantage of the recent advances in AI and position themselves to capitalize on the newly available capabilities. However, no matter how advanced AI models get, they’ll rely on quality data to be effective. New research from Informatica concluded that 42% of data leaders cite data quality as their top obstacle, while 40% highlight data privacy and governance, and 38% point to AI ethics as significant challenges.1

The quality of your data is not going to improve without a strategy that clearly articulates why doing so is critical for your organization to achieve its AI goals. This report gives you everything you need to clearly articulate why you need to focus on the quality of your data as a key part of your overall data strategy. You will also discover some direct and applicable actions you can take to make data quality the foundation of your data culture.

After...

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 01, 2024
Length: 50 pages
Edition : 1st
Language : English
ISBN-13 : 9781835088562
Category :

What do you get with eBook?

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Product Details

Publication date : Aug 01, 2024
Length: 50 pages
Edition : 1st
Language : English
ISBN-13 : 9781835088562
Category :

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Table of Contents

13 Chapters
Data Quality in the Age of AI Chevron down icon Chevron up icon
Executive summary Chevron down icon Chevron up icon
Understanding data quality Chevron down icon Chevron up icon
Unlocking AI’s potential with data Chevron down icon Chevron up icon
Improving data quality at the source Chevron down icon Chevron up icon
Case studies: Positive impact of data quality Chevron down icon Chevron up icon
Cultivating a data culture that values quality Chevron down icon Chevron up icon
Conclusion: Embracing a quality-driven data culture Chevron down icon Chevron up icon
About the author Chevron down icon Chevron up icon
About the technical reviewers Chevron down icon Chevron up icon
Additional reading Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Bibliography Chevron down icon Chevron up icon

Customer reviews

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Brian Oct 26, 2024
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Somewhat light in content. The main axes to consider are clearly laid out, but lots of details or complexities of implementation are not presented, even in the case studies.
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