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The Six Components of a Successful Analytics Approach

  • By Nilly Essaides
  • Published: 7/1/2015

Whether your company is engaged in analysis depends on your definition of analytics, experts agree. “I don’t think that true analytics are used well by many people,” said Michael Coveney, a business performance expert whose latest book was published in February 2014. “I meet a lot of companies that talk about analytics, but when I ask what they’re actually doing, it turns out they’re just producing reports from the G/L. Although we hear about companies that run advanced, real-time models, the majority of people in finance are still at a basic level.”

Robert Kugel, senior vice president and research director at Ventana Research, said his firm’s research shows that “FP&A is doing at least an okay job of executing standard financial analysis.” However, past the standard ratio analysis the results are not so good. For example, he said, only 10 percent to 15 percent use predictive analytics.

To succeed, an analytics approach must have the following six components:

  • Data. You need to collect a lot of it and in different formats, e.g., structured, unstructured.
  • Context. Explain what the data represents within a business model, e.g., to what location, product, month, or territory does this relate?
  • Correlation. Connect the model to an analytics engine that can summarize the data and create correlations between nominated variables or factors.
  • Easily understood. Develop a hypotheses that can be tested by the engine and reported to end users in a format that makes sense to them.
  • Actionable. Check that the results lead to actions that are within the business strategy, e.g., you want to expand in that country or sell more of a particular widget.
  • Forecast. Run multiple scenarios on any decisions to be made in order to predict what may happen in the future.

According to Nigel Geary, BI specialist at British Gas, who spoke at a recent AFP London FP&A Advisory Board meeting, more companies of every size are making progress in harnessing data to drive business decisions. So far, he noted, “What I see is very basic. Nobody has even thought about predictive forecasting. “[Predictive forecasting] is what the trend tells us, for example, how a new product will affect sales and R&D. The first stage is getting the house in order and figuring out how to add value to management and performance.”

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