Remove Data Programming Remove Datasets Remove High Quality Data
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Data Integrity vs. Data Quality: How Are They Different?

Precisely

) If data is to be considered as having quality, it must be: Complete: The data present is a large percentage of the total amount of data needed. Unique: Unique datasets are free of redundant or extraneous entries. Valid: Data conforms to the syntax and structure defined by the business requirements.

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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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Data Quality Trends for 2024

Precisely

This was made resoundingly clear in the 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, which surveyed over 450 data and analytics professionals globally. 70% who struggle to trust their data say data quality is the biggest issue.

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Data Governance Trends for 2024

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To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.