The findings conclude that organizations with mature DevOps practices* are more successful in scaling AI than those with immature practices. In short, DevOps has not failed; incomplete DevOps has.

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 ...

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 ...

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 ...

Over the past decade, the push for digital transformation has touched nearly every industry and has changed the game for BI. Now, every system and device has a digital trail, with data varying in ...

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 ...

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 ...

eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More. From a focus on improving software delivery to discussing ...

DevOps combines the information technology and software development teams and increases communication and collaboration between the two groups. With DevOps, then, it becomes possible to adopt an ...