AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

I’m confident 2020 will be remembered as the year DataOps came of age, as companies are discovering the need to maximize the inherent business value of their data. DataOps adds the observations that ...

In this data-driven world, it is not the one who has the most data that wins, but the one who best organizes and uses it. That is why, as IT leaders, it's time to make the shift from IOPS to DataOps.

DataOps, an adaptation of what’s traditionally known as DevOps, has evolved into an essential component of modern business operations. DataOps applies the concepts that have fostered more agility and ...

DataOps, a relatively new concept, currently has a wide variety of definitions. However, the term DataOps (data operations) was first coined in 2014 by journalist Lenny Liebmann. He described DataOps ...

When failure is expensive, managers avoid it at all costs. When there is an aversion to failure, team members play it safe, take fewer risks and cling to formulaic ...

Today’s organizations are committed to collecting and analyzing as much data as possible from sources new, old and evolving. But they continue to have variable levels of success distilling and ...

Today’s north star is the autonomous digital enterprise, characterized by three traits: business agility, customer centricity and the ability to drive decisions with actionable insights – three traits ...

One of the biggest analytics stumbling blocks for biomanufacturers is the need to prepare data in a way that makes it accessible to analytic systems and valuable to end users. Implementing a DataOps ...

Ashish Thusoo and Joydeep Sen Sarma know a thing or two about big data. They led the team that built Facebook's data infrastructure, and they are also the co-authors of the Apache Hive project and ...