Data Race News Today

As financial firms allocate resources to use AI, the technologies stand to provide clients with a more personal experience.

By Bryan Kirschner, Vice President, Strategy at DataStax - Overall, developers in organizations with both AI and ML widely deployed were 15 percentage points more likely than those in organizations where AI and ML are in “the early days” of deployment to say that “tech is more exciting than ever.”

By Bryan Kirschner, Vice President, Strategy at DataStax - Successful AI companies — think Apple, Netflix, Google, Uber, or FedEx — build innovative applications and, as they scale, start the flywheel of data, growth, and improvement spinning by gathering ever-growing amounts of real-time data, accessing it instantly, and tuning their predictions. User experiences become more personal and intuitive; key decisions can be made nearly instantaneously; and predictions can occur in real-t...

By Bryan Kirschner, Vice President, Strategy at DataStax - One of the most painful – and pained – statements I’ve heard in the last two years was from an IT leader who said, “my team is struggling to find ways that our company’s data could be valuable to the business.”

By Bryan Kirschner, Vice President, Strategy at DataStax - Not too long ago, many CIOs might have had to wrestle with a chief financial officer who viewed enterprise IT as a utility. To use a household metaphor, a CFO might be sorely tempted to save money if budgets were tight by lowering the thermostat on cold days.

By Bryan Kirschner, Vice President, Strategy at DataStax - From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries.

By Thomas Been - Winning enterprises take data, process it, and use it to deliver in-the-moment experiences to customers. Starbucks, Netflix, The Home Depot, and countless other organizations large and small have built great success based on this understanding. But what does that success look like, and what are the challenges faced by organizations that use real-time data?

Where does your organization stand in the data race? Are you keeping up, ahead of the pack, or further behind the competition than you think?