James Taylor's Decision Management

While predictive analytics and data mining both apply sophisticated mathematics to data in order to solve business problems, data mining is focused on exploring while predictive analytics answers "What next?". Data mining involves using an analytic toolset that automatically searches for useful patterns in large data sets. Predictive analytics is usually an analyst-guided discipline that uses data patterns to make forward-looking predictions - to turn uncertainty about the future into probabilities by analyzing historical data. If you like, data mining searches for clues while predictive analytics delivers an answer in a specific circumstance.

Data mining is often one stage in developing a predictive model. Automated data mining techniques can isolate the most valuable data variables within a vast field of possibilities. The analyst uses those variables, and the patterns those represent, to build a mathematical model that "formalizes" these relationships and predicts future behavior consistently. Data mining is also often used to find the right business rules - the rules that have worked in the past.

Traditional business intelligence (BI) tools extract relevant data in a structured way, aggregate it and present it in formats such as dashboards and reports. Like data mining, BI tools are more exploratory than action-oriented, but the exploration is more likely driven by a business user than an analyst. BI helps businesses understand business performance and trends. Whereas BI focuses on past performance, predictive analytics forecasts behavior and results in order to guide specific decisions. If BI tells you what’s happened, predictive analytics tells you what to do. Both are important to making better business decisions.

Predictive analytics also focuses on distilling insight from data, but its main purpose is to explicitly direct individual decisions.