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77% of data and analytics professionals say data-driven decision-making is the top goal for their dataprograms. Data-driven decision-making and initiatives are certainly in demand, but their success hinges on … well, the data that supports them. More specifically, the quality and integrity of that data.
Key Takeaways: Dataintegrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top dataintegrity challenges, and priorities. AI drives the demand for dataintegrity.
Key Takeaways: Dataintegrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top dataintegrity challenges, and priorities. AI drives the demand for dataintegrity.
When you delve into the intricacies of data quality, however, these two important pieces of the puzzle are distinctly different. Knowing the distinction can help you to better understand the bigger picture of data quality. What Is DataValidation? Read What Is Data Verification, and How Does It Differ from Validation?
But what do you do with all that data? According to the 2023 DataIntegrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, 77% of data and analytics professionals say data-driven decision-making is the top goal of their dataprograms.
Read our eBook Validation and Enrichment: Harnessing Insights from Raw Data In this ebook, we delve into the crucial datavalidation and enrichment process, uncovering the challenges organizations face and presenting solutions to simplify and enhance these processes. Read Trend 3.
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