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Using Analytics
A High-Level Look At Practical Applications For OLAP

by Lyndsay Wise, President, WiseAnalyticsMonday, May 31, 2010

When companies think of using OLAP, they may conjure up images of statistical or other analysts frantically manipulating data and trying to identify the reasons behind various performance mysteries.  Luckily, the use of analytics helps organizations transition from dealing with problems in a reactive manner towards identifying discrepancies before they become issues affecting the overall performance of the business negatively.  The use of OLAP specifically, helps organize disparate data sources into one cohesive look at a business unit or problem while taking into account the external factors that may affect performance.  Instead of developing a set of ad hoc reports or metrics-driven dashboards, OLAP lets people interact with data on a deeper level.

Obviously, not everyone using business intelligence requires OLAP to do their job, but for those who do, interactive analytics can increase data visibility and make it easier to identify the reasons behind why specific things are happening within their departments.  This article looks at various departmental and business unit OLAP applications to provide examples of how organizations are currently applying OLAP within their businesses.

Sales And Marketing

The use of OLAP for sales and marketing data is common.  Aside from looking at daily sales figures, organizations want to identify trends over time and examine the causal relationships between product or service sales, regions, sales reps, external forces, and marketing campaign success.  Combining all of this information together can be very time consuming when done independently and may not provide accurate results.  With OLAP, businesses can define their own parameters and help identify successful marketing campaigns and delve into the reasons behind sales discrepancies within various regions, or among sales representatives.

The ability to measure the success of specific marketing campaigns and to look at what works best is more of an art than a science.  When combining various data sources – both internal and external – marketers can get a full picture of what is happening within the industry to gain a deeper understanding of performance trends and why one campaign may be more successful in comparison with another.  Designing an OLAP cube helps make this type of analysis simple so that marketers can look at information over time to identify correlations between marketing initiatives and sales success to help allocate funds more efficiently.

Identifying Risk

Fraud, lack of compliance, and wasteful management are all areas that companies constantly struggle with.  How to identify potentially fraudulent activities before they occur, make sure that business and financial processes can be accurately audited, and save costs are important aspects to managing any organization.  Without a robust analytics tool to identify discrepancies and flag potential risk before activities occur, it can be nearly impossible to successfully develop and maintain a risk-management practice.  OLAP enables risk analysts to put all of the information they require into one bucket and slice and dice that data in multiple ways to go beyond flagging suspicious acts towards pattern identification and looking at ways to limit future negligent acts.

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