I'll explore data-related challenges, the increasing importance of a robust data strategy and considerations for businesses ...

Without high-quality, well-governed data, every downstream AI initiative becomes fragile, expensive, inaccurate or downright ...

The rapid advancement of Internet of Things (IoT) technologies is revolutionising water quality monitoring systems by enabling the continuous, real‐time assessment of critical water parameters. Modern ...

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 ...

When Daniel Graham, an associate professor in the University of Virginia School of Data Science, talks about the future of ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...

The programs that were developed with the help of data systems had been created to transmit data elsewhere in a consistent ...

The biggest challenge to AI initiatives is the data they rely on. More powerful computing and higher-capacity storage at lower cost has created a flood of information, and not all of it is clean. It ...

Learn the definition of data quality and discover best practices for maintaining accurate and reliable data. Data quality refers to the reliability, accuracy, consistency, and validity of your data.

Gone are the days when quality is owned by a single department or measured on a distant dashboard. Today, clinical quality is woven into the fabric of nearly every aspect of health system operations, ...