For clients, data quality is fast becoming a pressing issue. Some are starting to take a tougher approach. Liam Kay-McClean ...

Poor data governance can lead to a myriad of issues that include data interpretation inconsistencies, security ...

The data wilderness hits major technology initiatives like AI projects when obstacles are created by data accuracy, quality, ...

Modern manufacturing operates in complex environments where traditional management approaches are no longer enough. This ...

Unlocking the potential of AI to add value, deliver efficiencies and insight, is reliant on the quality of data fed into AI tools. Inaccurate data leads to AI ‘hallucinations’ with unreliable ...

Opinion: Akerman's Melissa Koch explains why the quality of data in legal artificial intelligence matters more than the ...

The MDA process typically involves five main steps: data preparation, data analysis, evaluation and interpretation of results, and implementation of results into manufacturing systems. Unique issues ...

The HealthTech industry is a by-product of the digital transformation currently underway in the health and wellness sector.

Implementing AI in healthcare isn’t just about choosing the right tools—it’s about making them work in the real world.

A clear majority of economists polled by Reuters are concerned about the quality of official British economic data, the ...

Enterprise data management (EDM) is the framework organizations use to manage data across systems, teams and workflows. It ...

Identifying false positives is almost as important as detecting genuine concerns during quality control (QC) processes.