It was really an eye opener for me to have a client describe "chargeback" as his toughest business intelligence (BI) problem. While at first tempted to dismiss the statement as an outlier, further consideration showed this firm's situation to be common, very common. Many enterprises would be able to extend their BI success (and, for example, use more BI products or use them better) were it not for a clumsy chargeback system that artificially inflates costs and thereby discourages use. The situation is so intractable that most firms have given up trying to deal with it. The pain is hidden, denied. But what if there was a way out of the dilemma? This two-part series aims to show the way by developing some alternatives for a rational approach to chargeback for the data warehouse.
Chargeback for the use of computing resources in the mainframe data center is arguably the gold standard for optimizing the use of computing resources. However, the results are less positive when this standard is applied to BI, data warehousing and ad hoc market research environments. Obstacles arise. It does not work. It is too expensive, actually discourages use of the computing resource, and often results in billing disputes and organizational conflicts. Expense, complexity, disputes and conflicts are clear symptoms that the process is broken. What are other models for optimizing use of the resource in the ad hoc decision support ("research") environment typical of BI? In order to conceptualize a different method of chargeback, it will be useful to understand the differences between the traditional data center environment and the exploratory data warehousing (research) one.
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