Operational leading indicator (oli) management using in-memory database

Abstract

Embodiments leverage capabilities of an in-memory database to manage measurement and modeling of Operational Leading Indicators (OLIs). An OLI template may comprise four model components: ratio calculation; factor calculation; predictive modeling; and cost estimation. Each model component is motivated and explained in terms of information sources, and analytical or statistical modelling tasks used in its definition. Embodiments combine analytical and statistical modelling utilizing in-memory computing, to process large amounts of unmodified source data, calculate cost measures rapidly without preaggregation, and/or run linear regression models on the same data set and in the same memory space without a need for separate hardware. An engine in communication with the in-memory database that comprises a large volume of available data, is configured to receive values for OLI factors as inputs. In response, the engine is configured to process these inputs according to the modeling template to provide corresponding cost measures as outputs.