Data Mining: Three Steps To Mining Unstructured Data

This article presents some informative perspectives on effective ways to capture and integrate non-static, or "unstructured" data. The term unstructured data is often applied to information regarding an individuals preferences or tastes. This information is viewed as more susceptible to variation and more difficult to predict, yet powerful as a predictor.

One field of non-static data in prospect research may be rating an individuals willingness to give during a campaign. This rating may be tied to a series of factors which often have uncorrelated relationships to each other (affinity for the institution and stock market performance for example) and can change independently. However if they are appropriately aggregated and correctly understood, can be dynamic informers towards predicting the likelihood of giving at all levels.

In our journey of discovery, we have seen one mistake made repeatedly. We have seen static business models and static data models try to be used to model inherently dynamic business processes, particularly at the point of interaction. For example, virtually every customer relationship management system we have come across has a manual classification scheme (or taxonomy) that is meant to be used by the service agent to classify the nature of the customer interaction. This approach has two major flaws.

First, as soon as the classification scheme is published, it is out of date, because interactions with your customers are unpredictable and continually changing. Second, even if the classification scheme was representative of your customer interactions, it is unreasonable to expect any number of service agents to classify their interactions with their customers in a consistent way and with high quality. This very often makes such classification data completely useless, or, more dangerously, misleading. This issue is true throughout the business ecosystem where unstructured information exists.

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