Interactive, flexible, and generic what-if analyses using in-memory column stores

Abstract

One well established method of measuring the success of companies are key performance indicators, whose inter-dependencies can be represented by mathematical models, such as value driver trees. While such models have commonly agreed semantics, they lack the right tool support for business simulations, because a flexible implementation that supports multi-dimensional and hierarchical structures on large data sets is complex and computationally challenging. However, in-memory column stores as the backbone of enterprise applications provide incredible performance that enables to calculate flexible simulation scenarios interactively even on large sets of enterprise data. In this paper, we present the HPI Business Simulator as a tool to model and run generic what-if analyses in an interactive mode that allows the exploration of scenarios backed by the full enterprise database on the finest level of granularity. The tool comprises a meta-model to describe the dependencies of key performance indicators as a graph, a method to define data bindings for nodes, and a framework to specify rules that describe how to calculate simulation scenarios.