Data can deliver value informationally or operationally, and the difference is key to understanding your team’s output.
Machine learning is used across industries and user communities for a wide variety of predictive analytics needs – use cases ranging from sales forecasting to churn reduction, customer lifetime value, inventory optimization, capital allocation and more.
Summary: Sometimes the insight you’re shown isn’t the one you were expecting. Unravel DataOps observability provides the right, and actionable, insights to unlock the full value and potential of your Spark application. One of the key features of Unravel is our automated insights. This is the feature where Unravel analyzes the finished Spark job and then presents its findings to the user. Sometimes those findings can be layered and not exactly what you expect.
Over the past week or so, I’ve been working on updating our Developer Workshop content. One of the trickiest parts of running workshops is the differences in local environment configuration: some attendees have a Mac, others windows, some with admin permissions, and some without. So much depends on what your company provides and how they manage their systems. To make things easier, I’ve been relying on CodeSandbox to eliminate a lot of the unknown.
Well, that was interesting! I just finished the Show Floor Showdown for Business Intelligence at Gartner Data & Analytics Summit with team ThoughtSpot. (Reminder: ThoughtSpot was named a Visionary in Gartner’s 2022 Magic Quadrant™ for Analytics and BI Platforms.) For the setup, we were asked to play the roles of sustainability experts, government officials, and business leaders working to understand the impact of sustainability goals on economic, environmental, and social outcomes.
Qlik’s entry at the Gartner Analytics and BI Bake-Off 2022 looked to address the big questions around clean energy and climate change and found some surprising insights.
