AI-assisted tools are now integrated across the delivery lifecycle-accelerating code generation, improving test coverage, and enhancing observability and incident response. As AI transforms how ...

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 ...

It may be a stretch to call data science commonplace, but the question “what’s next” is often heard with regard to analytics. And then the conversation often turns straight to Artificial Intelligence ...

In 2026, the teams that win prioritise signal depth, operational integration, and contextual engagement over raw contact volume.

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 ...

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 ...

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 ...

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 ...

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, ...