In February, Informatica issued a report that found data quality to be the number one issue preventing companies from succeeding with generative AI (GenAI) initiatives. A pair of data observability ...

Learn the definition of data quality and discover best practices for maintaining accurate and reliable data.

Precisely, the pioneer in data integrity, announced its strategy to expand the Precisely Data Integrity Suite with a new ...

Informatica provides a comprehensive suite of data quality products that include data profiling, data cleansing, data monitoring, and data governance capabilities.

Since AI models learn from data differently than humans do, the tools we use to prepare data for humans aren’t good enough for AI applications.

Data quality monitoring, data quality remediation, data profiling and quality scoring, and data policies and control rounded out the top challenges with which organizations are currently grappling.

By combining architectural finesse, prompt engineering, and rigorous validation, this framework sets a new benchmark for AI-enabled metadata management.

Key findings show organizations averaging just 42/100 on data trust maturity, with the lowest scores in areas such as remediation workflows, policy enforcement, and reference/master data quality.

Generative AI introduces new risks, challenges, and opportunities for how organizations source and use data. Here are four ways data governance teams are rising to the occasion.

Data mapping, transformation tools, data capture, data profiling, and data quality are essential components of data integration.

Avellino adds data profiling to the mixLast quarter Trillium’s chief rival, Ascential Software upgraded its Data Integration Suite to include data profiling, data integration and data quality in ...