Forbes contributors publish independent expert analyses and insights. Gary Drenik is a writer covering AI, analytics and innovation. We are almost three years past the fanfare of ChatGPT’s big debut ...

Many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Here are common causes of bad data quality: Multiple data sources: ...

SAN FRANCISCO--(BUSINESS WIRE)--Monte Carlo, the data and AI observability company, today announced a series of product enhancements and new capabilities at its annual IMPACT Data Observability Summit ...

DQM is becoming a core capability for organizations that need to make better decisions with data. What are the responsibilities of different roles in DQM? Image: WrightStudio/Adobe Stock Data quality ...

Data quality management efforts — tied to disrupting innovations, rapid market shifts and regulation pressures — will continue to grow in 2023 and take on a more dominant role in the data management ...

Kore Integrate is the perfect platform to improve Data Quality Management, said Maxwell Dallinga in a recent blog post by Kore Tech. Data quality management is a process to ensure that data is ...

The conversation about artificial intelligence typically orbits around model architecture, parameter counts, and computational power. But according to Sushant Mehta, a Senior Research Engineer at ...

In most conversations, data and AI are inextricably linked. The narrative tends to be that organizations are not using AI well if they don’t have quality data from the field feeding into AI models.

Health systems are entering an era of intelligent data management where real-time validation, data quality scoring and robust governance are paramount, says Kevin Ritter of Altera Digital Health.

Universities must tighten the quality of the data entered into AI models to improve the output generated by tools such as chatbots. Universities have been cautious adopters of artificial intelligence.