Companies have had only mixed results in their decades-long quest to make better decisions by harnessing enterprise data. But as a new generation of technologies make it easier than ever to unlock the value of business information, change is coming. We’ve already reaped gains at Hitachi Vantara, where I run a global IT team that supports 11,000 employees and helps more than 10,000 customers rapidly scale digital businesses.

I’m starting to hear questions like: “What comes next?” “Do things go back to the way they were?” “Are some of the changes wrought by the pandemic here to stay?” I think we all know part of the answer: there is no going (all the way) back. In the analog world, sure, we need some things to revert to bounce back. We need to revitalize retail, tourism and hospitality to get our economies moving again.

Companies today are faced with an analytics conundrum. On one hand, there’s a higher demand than ever for actionable business insights, but on the other there’s limited resources to deliver BI content to on-technical business end-users. To fill this gap, the industry is increasingly turning to the next generation of self-service analytics tools. These tools reduce time to insight, speed up insight to action, and also allow BI teams to focus on more strategic analytics work.

Many of you rely on Dataflow to build and operate mission critical streaming analytics pipelines. A key goal for us, the Dataflow team, is to make the technology work for users rather than the other way around. Autotuning, as a fundamental value proposition Dataflow offers, is a key part of making that goal a reality - it helps you focus on your use cases by eliminating the cost and burden of having to constantly tune and re-tune your applications as circumstances change.

Since the release of Cloudera Data Visualization (DV) back in Oct 2020, our primary mission has been to expand access to data analytics and predictive insights across enterprise businesses.

Recommendation systems have become a cornerstone of modern life, spanning sectors that include online retail, music and video streaming, and even content publishing. These systems help us navigate the sheer volume of content on the internet, allowing us to discover what’s interesting or important to us. The classic modeling approaches to recommendation systems can be broadly categorized as content-based, as collaborative filtering-based, or as hybrid approaches that combine aspects of the two.

Data and analytics have become second nature to most businesses, but merely having access to the vast volumes of data from these devices will no longer suffice. Leading enterprises realize that the speed of data presents a new frontier for competitive differentiation. It is imperative for organizations to reduce time-to-insights to gain a competitive advantage by responding decisively to competitors, fine-tuning operations, and serving fickle customers.