In previous articles we discussed aspects of data consumption that involved either the past (review), the present (monitoring), or elements of both (producing and investigating). Unlike reviewing or monitoring, extrapolation attempts to predict future data. This usually takes one of two forms: forecasting (the estimation of key data values in the future based on current values and trends), and predicting outcomes (using statistical probabilities of various events).
The goal of extrapolation activity is to either identify opportunities before they become known to other market participants or to identify and counter future threats before they occur. An example of the former would be an attempt to project which customers are most likely to respond to a marketing campaign in order to most effectively target advertising. An example of the latter would be to attempt to determine which of your best customers is most likely to take his or her business elsewhere; armed with this knowledge one could prevent the impending defection and retain the customer.
Extrapolation entails more risk than other data consumption activities because one must make assumptions about future conditions. Because conditions are subject to change between the time of extrapolation and the future data or conditions to be extrapolated, extrapolations are frequently expressed as scenarios. Different scenarios are created based on differing assumptions about how current trends will play out, and decision makers choose the scenario they believe is most likely to occur and use the chosen scenario to guide the decision process. Other scenarios can be taken into account by the decision makers in creating contingency plans.
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