Analysis and prediction server with column store database — A case study in telecom churn

One of the major concepts in business analytics is to identify the anomalies over time, also called as trend analysis. This can be easily done in pivot tables by using time as one of the dimensions, usually across columns. However, the trending information itself is insufficient to make any quick and insightful observations. Ranking the time series to identify the similar units of information can accelerate the analysis process. Similarly projection of the time series to the future will help the decision maker to proactively build alternate plans for differing scenarios. For doing such an analysis and modeling it becomes necessary to have aggregated data on demand. Current breed of row store database have limited capabilities to provide the response time required for such an analysis and modeling. Hence, column store database are expected to be better alternatives, for these types of problems. In this work the attempts made by the authors to develop such a system named as `rePivot' are presented. The proposed frame work consists of three modules namely - a column store database to provide quick access to data, a time series ranking module and a probabilistic forecasting module. A case study of the proposed frame work in churn analysis and modeling in telecom has been carried out to test the suitability of framework for industrial applications. Application of the framework has shown promising results. Work is under progress to develop additional modules for survival analytics of individual entities in the database.