Every new decade sees businesses split into winners and losers as technology evolves, competitiveness tightens, and new market entrants challenge the incumbents.

inReality provides an analytics platform that leverages IoT sensor data (for example, visual technologies) to bring operational excellence and exceptional customer experiences to all types of venues. The company’s clients range from public schools to major telecommunication companies with the goal being to make their spaces more secure and efficient, to solve problems, and to create better experiences for their patrons.

Financial services institutions need the ability to analyze and act on massive volumes of data from diverse sources in order to monitor, model, and manage risk across the enterprise. They need a comprehensive data and analytics platform to model risk exposures on-demand. Cloudera is that platform. I am pleased to announce that Cloudera was just named the Risk Data Repository and Data Management Product of the Year in the Risk Markets Technology Awards 2021.

You may have read about Snowflake’s IPO last year. But you probably didn’t hear about all the work that the Snowflake security team did in preparation. Our corporate security program went through a security analytics review to ensure that it satisfied the new security policy requirements resulting from the IPO. Here are a few lessons that we learned when setting up automated security control validation on our Snowflake security data lake.

Artificial intelligence (AI), automation and machine learning (ML) are rapidly transforming the analytical experience for everyday business users in 2021. Whether it’s automated visualizations, continuous analysis, or reduced time-to-insight, there are many practical benefits of augmented analytics that are well documented and fully realized today.

Thanks for all those who enthusiastically responded to my first blog post on Qlik analytics with Peloton! Now, onward brave souls as we learn HOW I was able to create the analytics I wrote about earlier!

Businesses today have a growing demand for real-time data integration, analysis, and action. More often than not, the valuable data driving these actions—transactional and operational data—is stored either on-prem or in public clouds in traditional relational databases that aren’t suitable for continuous analytics.

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.

Innovative organizations need DataOps and new technologies because old-school data integration is no longer sufficient. The traditional approach creates monolithic, set-in-concrete data pipelines that can’t convert data into insights quickly enough to keep pace with business. The following trends are driving the adoption of Hitachi’s Lumada DataOps Suite.