It’s a generally accepted maxim that the business community’s fascination with big data, which started in the mid-2000s, ran out of steam about five years ago. But that’s only partly true. While the ...

Last week’s Informatica World 2016 brought out a lot of talk involving data quality, real-time live data and the automation of ingesting and analyzing data in order to turn it into something ...

You often hear that data is the new oil. This valuable, ever-changing commodity has begun to play a starring role in many cloud-native applications. Yet, according to a number of DevOps teams, data ...

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key Data scientists have some practices and needs in ...

Overcoming DevOps obstacles—such as slow, manual, poor-quality test data—is key toward accelerating pipelines. With speed being a central success factor for DevOps pipelines, increasing velocity ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Much has been written about struggles of deploying machine learning ...

It’s sad but true, most attempts by companies to leverage data as a strategic asset fail. The challenge of both managing vast amounts of disparate data and then distributing it to those who can use it ...

While DevOps drives innovation and simplifies collaboration, it also comes with its own set of risks and vulnerabilities. Developers rely on Git-based platforms like GitHub, Azure DevOps, Bitbucket, ...

Data science and machine learning are often associated with mathematics, statistics, algorithms and data wrangling. While these skills are core to the success of implementing machine learning in an ...

Data governance is an umbrella term encompassing several different disciplines and practices, and the priorities often depend on who is driving the effort. Chief data officers, privacy officers, ...