For the third year in a row, we were recognized for our "ability to execute" and "completeness of vision.".

The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

We are now well into 2022 and the megatrends that drove the last decade in data—The Apache Software Foundation as a primary innovation vehicle for big data, the arrival of cloud computing, and the debut of cheap distributed storage—have now converged and offer clear patterns for competitive advantage for vendors and value for customers.

Without automated data integration, broad trust in AI and the right expertise, organizations will struggle to mature their AI capabilities.