Leading enterprises are starting to weave advanced analytics into their core operational systems. The goal is to use the output from predictive models dynamically for decision-making. This requires access to multiple data stores of varying types and the use of many data processing techniques. Innovative applications simultaneously blend transaction processing and analytics to deliver business value while avoiding IT complexity, driving the need for a data platform based on a multi-model, multi-workload database. Through consolidation into a single data platform, businesses generate new value by delivering insight at transaction time while preventing yet another technology silo and minimizing data lag.
With the availability of new enterprise data sources to use as predictive measures, IT organizations are being asked to store an increasing volume of data in a variety of formats that may or may not be used in future projects. Different types of data have been traditionally managed using separate data stores. As a result, big data and analytics are commonly deployed separately from transaction processing systems, introducing time delays between when insights can be applied to affect business transactions. Hybrid applications combining transaction processing and analytics components with flexible access to multiple data models can help enterprises offer new value creation opportunities at an organizational scale—without the added burden on IT.