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Taking Business Intelligence To The Next Level By Applying Predictive Analytics

by Lyndsay WiseWednesday, May 27, 2009

Predictive analytics exemplifies one of the most beneficial applications of business intelligence.  When discussing the benefits BI offers to organizations, the three that get the most attention are expanding BI to the masses, using operational intelligence, and getting a clear view of an organization’s performance. The other key advantage that is now starting to be applied more in organizations is the use of predictive analytics.  This includes activities like predictive modeling to help with planning and forecasting - and the ability to apply what-if analyses to current situations to identify what might happen in the future and to develop strategies surrounding potential business scenarios.

Understanding how predictive analytics is applied within organizations can help companies decide whether using business intelligence as a proactive and forward-looking tool will benefit their company.  In general, though, the application of BI for what-if analysis is a mature use of these types of solutions and organizations looking at BI for the first time normally look at sales or financial performance as a starting point.  For organizations that do want to utilize predictive analytics, many common current uses can govern potential starting points. This article looks at some common applications of predictive analytics and the business value of such applications.

Financial And Sales Planning

Looking at forecasting provides good insight into how organizations use BI and predictive analytics.  A number of years ago, organizations may have projected what they thought they would sell based on past performance and set targets.  This enabled sales and marketing departments to plan for events to target goals and help the organization set up parameters to meet these goals.  Unfortunately, without taking into account external factors (such as competitor performance, environmental factors, consumer perceptions, etc.), these forecasts are incomplete.  It is virtually impossible to develop a business and to increase market presence by managing the business in a bubble.  Consequently, the use of predictive analytics enables companies to use models to identify factors that may affect future success.

Financial analysis and forecasting, and sales planning provide initial insight into how organizations apply predictive analytics to their organizations.  Whether looking at five-year projections and planning events that will lead to this growth, algorithms and statistical models exist to enable organizations to plan initiatives that support growth.  Even though alternative ways exist to get this type of information, predictive analytics identifies the easiest way.  This is because statistical modeling, etc. is built in to these solutions.  Consequently, organizations can tie predictive analytics to their current initiatives and tie this to trends identification based on historical information collected over time. 

With the added ability to tie in semi-structured and unstructured data to the mix, organizations can include sentiment analysis based on customer experience centers, what is being said on the internet, what people are saying about the competition and how that compares to the organization, etc.  All of this information can then be collected and transformed into data that can be analyzed and applied to sales and marketing initiatives.  Within financial planning activities, this information may be used to identify inhibitors or differentiators to help plan for future growth.

Fighting Fraud

Unfortunately, the topic of fraud and the use of analytics to predict potential fraud before it happens or alternatively, to identify fraud after it occurs to rectify the situation is so broad that it could cover several books on its own.  The general use of predictive analytics within the realm of fraud is to identify patterns in data that are suspicious.  Within insurance or banking, it may be forms that are similar, for government assistance, it may be similar information across multiple addresses, or multiple people collecting financial assistance at one location, identity theft, health insurance being wrongly claimed, etc.  The list is endless and over time, more types of activities occur that raise the bar and require organizations within these industries to apply robust predictive analytics tools to either lessen the incidents of fraudulent activities or to identify fraud after it occurs and collect the funds and punish the culprit.

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