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Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

Precisely

77% of data and analytics professionals say data-driven decision-making is the top goal for their data programs. 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.

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Validation vs. Verification: What’s the Difference?

Precisely

When you delve into the intricacies of data quality, however, these two important pieces of the puzzle are distinctly different. Most of those same leaders don’t fully trust their organization’s data. Yet fewer than half rate their ability to trust the data used for decision-making as “high” or “very high.”

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Spatial Analytics 101: Benefits, Use Cases, and Solutions

Precisely

According to the 2023 Data Integrity 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 data programs.

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Data Scientist vs Data Engineer: Differences and Why You Need Both

AltexSoft

Regardless of the structure they eventually build, it’s usually composed of two types of specialists: builders, who use data in production, and analysts, who know how to make sense of data. Distinction between data scientists and engineers is similar. Data scientist’s responsibilities — Datasets and Models.