An examination of BI-based Modeling and Optimization, the modeling of BI data to arrive at the optimal course of action for business challenges.
At the apex of Business Intelligence is BI-based Modeling and Optimization (BIMO), a set of technologies for delivering better decision-making options to executives and managers. BI-based Modeling and Optimization combines the rich processed information provided from BI tools and data marts and feeds them into modeling and optimization tools to arrive at the best possible options or courses of action in a problem situation. These decision situations can be operational -- determining the best way to run a refinery given record breaking oil prices, for example. Or the problem can be strategic -- determining the best portfolio of capital projects given BI-based market projections and costing estimates.
The key attractions of BIMO are that it can deliver outstanding return on investment both in day-to-day operations as well as one-time decisions. In addition, BIMO methods really help to refine and discipline the thinking and processes around decision making. In fact, BIMO tends to produce better returns in high-flux, high-change periods both operationally and strategically. Unfortunately, many BIMO systems are quietly hidden away, as corporate executives do not want to reveal their competitive advantage-producing systems. Think of the optimizers in supply chain and production scheduling packages: In some cases the optimizing tools are so elegantly embedded that staff do not know the power of the tool harnessed for their benefit. Rightfully, users view the tool as a total BI-based optimizing system.
Traditionally, there have been three major risks associated with delivering consistent, high-quality BIMO results. First, the underlying disciplines of linear programming, network modeling and non-linear optimization are not only difficult and complex but also different in methodology among themselves. Second, getting expertise can be expensive because, as often as not, linear programming experts cannot help with non-linear math optimization problems. Sometimes the data and algorithms can be shared; but the underlying models and solution methods are distinct, even disjointed. (Methods used for linear programming are very different among themselves as well as from those used for non-linear optimization.)
But the most challenging problem for BIMO is marshaling together all the data sources necessary to support the models. What can make BIMO data-handling even more demanding is the fact that BIMO inputs frequently have to go through several stages of processing as they filter up the BI stack: ETL, consolidation, analysis and forecasting. Fortunately, the quiet revolution in BI -- over the past 10 years BI tools have become easier to use, better integrated, and broadly available -- has provided improved data warehousing, ETL, and analytics at lower cost and with greater ease of use. In addition, new technologies such as XML, business process management and Web services further ease the data-marshaling burden. The result is that BIMO weathered the dot-com-induced IT slump with a solid 3-9% annual growth rate and diversified new offerings. So let's take a look at the BIMO vendors.
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