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

AI Data Quality in 2026: Challenges & Best Practices

Data QualityJan 23

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 9

Data Federation vs. Data Virtualization in 2026

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.