Moving from business intelligence to advanced analytics

Though BI provides many advantages, it is limited in its ability to predict, forecast and make inferences on unknown facts and relationships – for instance, predicting customer behavior, the probability of fraud, or suggesting the next best offer during an online transaction.

For these reasons, most companies are enhancing their BI practices to include predictive analytics and data mining. This combines the best of strategic reporting and basic forecasting with additional operational intelligence and decision-making functions. By developing the capability to move from insight to action, leading businesses are combining historical and predictive analysis to determine what immediate actions to take. In a three-part series we wrote that recently appeared in Information Management, we map out a plan for organizations to move from business intelligence to advanced analytics.

Among the points we outline:

  • Analytics is not something that business users beg the IT department to perform for them, but rather something the IT department facilitates so business users can explore, analyze, predict and forecast, and automate key processes.
  • Effective companies turn business analytics from a craft practiced by a few analysts and decision-makers into an established scientific approach that dramatically contributes day-to-day to the company’s bottom line.
  • There is a lot of buzz around the idea of automating analytics. Companies, weary of investing huge amounts of time and money into a project that yields a one-time boost in sales or profits, are lured by the idea of automated analysis that solves problems without the need for teams of specialized experts or high-priced consultants. But there is no magic button – accounting software didn’t replace accountants, and automating analytic functions doesn’t replace modelers and analysts. The initial processes still need to be built and automated.
  • What automation can do is power huge efficiency gains and allow a company to cost-effectively explore and test models to find the right customers for a specific offer or the optimal way to flag suspect claims. The combination of a well-designed data warehouse and high-powered analytics helps to automate scoring, validation and tuning, leaving the business users more time to create and explore. It allows companies to work with large volumes of data quickly and efficiently.
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