Why pair real-time analytics with historical data views?
The main goal of real-time analytics is to act on new input as soon as it arrives. We aim to process vast volumes of data from multiple sources and send back relevant responses within seconds. But how do you keep your real-time responses relevant for years if the data you receive constantly changes?
Real-time analytics doesn’t work alone: historical analytics complements it by providing valuable insights for improving output over time. For instance, if you want to prevent fraudulent financial transactions, real-time analytics helps detect and stop fraud as it happens, while historical data helps AI models learn and recognize fraud patterns better over time. That’s why effective big data architectures incorporate both real-time and historical data processing to ensure high analytics accuracy even as the data landscape evolves and unseen scenarios emerge.
