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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