Data Quality

Data quality ensures the accuracy, completeness, and consistency of datasets for reliable analysis. We cover tools and their features, like smart classification, shadow data detection, and cloud-native capabilities, as well as the role of data quality in AI.

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Knowledge Management Quality Assurance

Data Quality in AI: Challenges, Importance & Best Practices

Data QualitySep 24

Poor data quality hinders the successful deployment of AI and ML projects. Even the most advanced AI algorithms can yield flawed results if the underlying data is of low quality. We explain the importance of data quality in AI, the challenges organizations encounter, and the best practices for ensuring high-quality data.

Data QualityJul 25

Top 10+ Data Classification Tools Compared

At its core, data classification software help organizations locate and label sensitive data (e.g. customer information) across endpoints, cloud services, databases. These systems are offered either as standalone solutions or modules within larger platforms, like DSPM, DLP or cloud data security software.

Data QualityJul 9

Data Federation vs. Data Virtualization

In this article, we compare data federation and data virtualization. We provide insights when data federation should be chosen over data virtualization and vice versa.