Today’s “big data stack” includes databases, data management software and data analytics tools – all critical components of an effective operational or analytical data system. But all those ...

Real-time data processing has become essential as organizations demand faster insights. Integration with artificial ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. As AI reshapes industries, most businesses still depend on data platforms built for a ...

The NFL's critique of Nielsen's audience measurement highlights shifting dynamics in the ad-supported streaming era.

Big Data refers to a vast and diverse collection of structured, unstructured and semi-structured data that inundates ...

Managing, moving, transforming and governing data for business applications and data analytics purposes has always been an important part of IT operations. But those chores have taken on a new level ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. As businesses continue to harness Big Data to drive innovation, customer engagement and ...

Big data can help make Americans healthier, and the Trump Administration has stated—in its recently released Make America Healthy Again report and elsewhere—that building a national big-data platform ...

To fulfill the promise of big data, you need to abstract data from its underpinnings -- at both the data and infrastructure layers Big data in the cloud has so many potential functional service layers ...

The big data companies focus on collecting, storing, and analyzing huge amounts of data that businesses use to make smarter decisions. This data comes from many sources, like websites, sensors, social ...

BE'ER SHEVA, Israel--(BUSINESS WIRE)--MDClone, a leading healthcare data analytics company, today announced that its MDClone ADAMS Platform was selected as the winner of the “Best Healthcare Big Data ...

Big data refers to large and diverse collections of data that cannot be managed by traditional data processing tools. Although the need to manage large data sets goes back to the 1960s and 1970s, it ...