Organizations addressing data quality issues as a strategic operation are positioned to adapt to latest trends and scale their businesses ...

This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...

Informatica from Salesforce, a leader in enterprise AI-powered cloud data management, today announced that it has been recognized once again as a Leader in the 2026 Gartner® Magic Quadrant™ for ...

Despite all our technological advancements, data quality remains the grit in the gears of the modern marketing engine. It’s the silent killer of campaigns, the drain on ROI, and the reason your ...

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 ...

While the datasets are useful tools for training AI models, they do come with their own risks, from regulatory risks to ...

In 2026, data leaders must focus on enhancing data foundations rather than feature expansion. That means prioritizing data ...

Meta Platforms is ramping CapEx, with 2026 guidance of $115–135 billion, fueling concerns over near-term ROIC compression.

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.

AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...

In some ways, data and its quality can seem strange to people used to assessing the quality of software. There’s often no observable behaviour to check and little in the way of structure to help you ...

In today's data-driven healthcare landscape, medical imaging stands at the forefront of diagnosis and treatment planning. From X-rays and MRIs to CT scans and ultrasounds, these images provide crucial ...