WHAT’S CHANGING?
Real-time analytics are taking the whole analytics space to a new level. “While OLAP capabilities for large data volumes have been around for some time now, the cost of compute resources has dropped, allowing for more complex analyses and the integration of larger and larger datasets,” said Rolo. “LLMs have also changed the insights landscape, providing outputs that go beyond traditional dashboards. These models can interpret and analyze data in ways that were previously unattainable, offering more in-depth and more actionable insights.”
In addition, analytics models are growing more powerful. “There are many analytics models on the market right now that are flexible enough and can act quickly enough to give users real-time insights,” said Prakash. “Many analytics packages are using AI and GenAI [generative AI] to make it easier for users to use natural language and ask different questions that aren’t just pre-built into a dashboard.”
The proliferation of cloud-native services has almost automatically plugged real-time analytics into many workflows. “This has lowered the barrier to entry significantly. Organizations can now ingest and analyze data in real time without having to build out complex and expensive infrastructure and tooling,” said Prakash.
The rise of GenAI and now, agentic AI, requires access to vast amounts of real-time data. In particular, agentic AI necessitates the creation of datasets “at a pace that is an order of magnitude larger than before,” said Srinivasan. “These systems have speed and scale requirements that are even more stringent than the earlier AI and ML-based inference.”
Lately, the world now expects real-time responsiveness. “Customers want what they want, and they want it now,” said Ian Clayton, chief product officer at Redpoint Global. With the AI boom, he noted, “Everyone is racing to deploy their own AI initiatives, and they need more and more data, and it needs to be as close to real time as possible to be the most effective.”