With the exponential explosion of business data and the accelerated market dynamics, more decisions must be made in compressed time frames. Data mining and its sibling - predictive analytics - now provide a potential avenue to meet this pressurized demand for real-time decision making. Although data mining has been a defined solution space since the 1990s, it is only during the last two years that the data mining process has been enhanced to create embedded predictive analytics. Predictive analytics builds on the data mining multistep process and statistical modeling techniques to add a layer of automation and self-directed built-in intelligence. Business users (and not just Ph.D. statisticians) can now analyze large amounts of customer, supplier, employee and product data for patterns and trends.
Depending on who you talk to, the time of day and the product space being promoted, the predictive analytics definition can be as narrow as "understanding why a variance occurs in real time and what to do about it moving forward" or as broad as "delivering the right insight to the right people in real time for making decisions." In our situation, we are focused on the relationship of the dependent key performance indicators (KPIs) and the associated independent causal variables. It is all about the ability to automatically discover KPI variances and determine the root causes so that improved predictors of a company's business performance can be developed, measured and managed. Predictive analytics is the automated process of sifting through large amounts of data using statistical algorithms and neural nets to identify data relationships between KPIs and critical measures that facilitate prediction of critical success factors.
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