Data Quality’s Role in Building a Data Culture: Why it’s the most critical part of your data and AI strategy, and how to make it the foundation of your data culture
by 作者: Andrew Jones (Author)
Publisher Finelybook 出版社: Packt Publishing – ebooks Account
Publication Date 出版日期: 2024-06-11
Language 语言: English
Pages 页数: 40 pages
ISBN-10 书号: 180512143X
ISBN-13 书号: 9781805121435
Book Description
Unlock the power of your data with expert insights on data quality and AI. Discover methods to enhance data quality, maximize AI’s potential, and establish a data-centric culture
Key Features
- Explore the relationship between data quality and AI, gaining a profound understanding of their interplay
- Discover actionable strategies to improve data quality, emphasizing practical implementation and real-world results
- Acquire the skills to measure and evaluate data quality, empowering data leaders to make data-driven decisions
Book Description
Organizations globally seek to revamp their data strategies to leverage recent AI advancements and position themselves to benefit from newfound capabilities. However, the effectiveness of advanced models hinges on quality data. Thus, data quality is now more crucial to your strategy than ever before. To enhance data quality, a clear strategy is imperative, outlining its significance in achieving organizational goals.
This report elucidates the critical role of data quality in your overall data strategy, providing actionable steps to establish it as the cornerstone of your data culture. By doing so, you’ll be well-positioned to capitalize on AI advancements, maximizing the value derived from your data.
The report empowers teams and organizations with knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for utilizing data quality as a catalyst for AI advancements. This enables organizations to unlock their data’s full potential, leading to improved performance, reduced costs, increased revenue, and better-informed strategic decisions.
What you will learn
- Enhance data quality at its source with effective strategies and best practices
- Identify and measure data quality within the datasets
- 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.
- Maintain high data quality standards with the help of Data Governance
Who this book is for
This report is tailored for data leaders responsible for shaping their organization’s data strategy to maximize data value, especially those looking to harness recent AI advancements.
Table of
Contents
- Data Quality: Why it’s the most critical part of your data & AI strategy, and how to make it the foundation of your data culture
About the Author
Andrew Jones is a principal engineer at GoCardless, one of Europe’s leading Fintech’s. He has over 15 years experience in the industry, with the first half primarily as a software engineer, before he moved into the data infrastructure and data engineering space. Joining GoCardless as its first data engineer, he led his team to build their data platform from scratch. After initially following a typical data architecture and getting frustrated with facing the same old challenges he’d faced for years, he started thinking there must be a better way, which led to him coining and defining the ideas around data contracts. Andrew is a regular speaker and writer, and he is passionate about helping organizations get maximum value from data.
