System and method for applying predictive metric analysis for a business monitoring subsystem

Abstract

Predictive metric analysis for business management is divided into build time, corresponding to the business owner view of the enterprise, and run time, corresponding to the information technology view of the enterprise. The build time consists of a predictive model and a monitoring model. These models go through transformation processes to the components of the run time. The run time components are a Metric Value Prediction Service (MVPS), which receives as input predictive model transformation and outputs predicted metric values, and a monitoring engine, which receives as input monitoring model transformation, the predicted metric values and business events from the business process. Various analytical engines can be plugged in to provide the predictive capabilities. Input is provided to a framework from various business systems which results in predicting the value of the metrics across the future time horizons.

Description

BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The present application generally relates to business monitoring systems and, more particularly, to a model driven approach to enhance existing business performance models with predictive modeling capabilities. [0003] 2. Background Description [0004] For an enterprise to be competitive, the ability to perform predictive analysis on large amount of data is very important to analyze a trend, discover the paint points, and / or discover new opportunities. Most companies today implement various Business Performance Management solutions including Business Intelligence techniques that help determine the current state of the business. This is achieved by defining metrics or key performance indicators organized in a hierarchy through the various vertical and horizontal silos of the organization. Data and events received in real time are persisted in a data mart and are used to provide historical analysis summarizing what has ...

AI Technical Summary

Benefits of technology

[0009] For example, an insurance company looking to improve healthcare and reduce costs would implement a data mining based predictive model which examines claims data and predicts individual usage of healthcare services over the next insured period. The metrics involved in the analysis and historical data provided usually don't have any fixed patterns and therefore an important component of the prediction would be to devise a pattern from the historical claims data. The predictive metrics in this scenario would be claim type (i.e. dental, medical, pharmaceutical, disability etc) and costs for every member. The trigger would be tied to every claim being submitted by the particular member which implies that the predictive model will be executed every time a member files a claim with the insurance company. The MVPS would be responsible for interfacing with the data mining based predictive model to generate the prediction of the claim types and costs the results of which would be sent back to the monitoring engine and warehouse for further analysis.

[0010] Another example where predictive analysis would be useful is in the electronics industry where the predictions of customer ordering behavior can significantly allow responding in ways to avoid deleterious outcomes, i.e., migrating supply to upward trending demand before pending orders emerge. For this purpose, a time series based forecasting system would be applicable that would factor in seasonality, order skews, product life-cycles and

Problems solved by technology

Most of the existing business performance meta models do not consider time as a first class element and therefore allow for modeling the current state of the business but not future predictions of any of the metrics.

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