The true measure of an effective data warehouse is how much key business stakeholders trust the data that is stored within. To achieve certain levels of data trustworthiness, data quality strategies ...

An article recently published in Nature proposes a new way to evaluate data quality for artificial intelligence used in healthcare. Several documentation efforts and frameworks already exist to ...

Artificial Intelligence (AI) stands at a pivotal juncture, facing a critical decision: continue down the path of large-scale data accumulation with its inherent challenges, or embrace more focused, ...

It’s no longer how good your model is, it’s how good your data is. Why privacy-preserving synthetic data is key to scaling AI. The potential of generative AI has captivated both businesses and ...

To establish a consistent approach to assess, manage and improve data quality across the data lifecycle, covering a wide spectrum of data types, and taking into account the blurred line between data ...

We’re just starting to tap the potential of what AI can do. But amid all the breakthroughs, one thing is fundamental: AI is only as good as the data it was trained on. Unlike people, who can draw on ...

Data-driven decisions require data that is trustworthy, available, and timely. Upping the dataops game is a worthwhile way to offer business leaders reliable insights. Measuring quality of any kind ...

Enhance your data strategy with effective data quality and data governance practices. Learn their differences and how to integrate the strategies successfully. Image: Dmitry/Adobe Stock Data quality ...

Observability by definition is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. In other words, a system’s behavior is determined from its ...