Kimball University: Extreme Status Tracking For Real Time Customer Analysis - InformationWeek
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05:41 PM

Kimball University: Extreme Status Tracking For Real Time Customer Analysis

Customer interactions create a wealth of timely data that marketing departments are eager to exploit. The customer status fact table provides a central switchboard for using this fast-moving data.

Ralph Kimball
We live in a world of extreme status tracking, where our customer-facing processes are capable of producing continuous updates on the transactions, locations, online gestures, and even the heartbeats of customers. Marketing folks and operational folks love this data because real-time decisions can be made to communicate with the customer. They expect these communications to be driven by a hybrid combination of traditional data warehouse history and up-to-the-second status tracking. Typical communications decisions include whether to recommend a product or service, or judge the legitimacy of a support request, or contact the customer with a warning.

As designers of integrated enterprise data warehouses (EDWs) with many customer-facing processes, we must deal with a variety of source operational applications that provide status indicators or data-mining-based behavioral scores we would like to have as part of the overall customer profile. These indicators and scores can be generated frequently, maybe even many times per day; we want a complete history that may stretch back months or even years. Though these rapidly changing status indicators and behavior scores are logically part of a single customer dimension, it is impractical to embed these attributes in a Type 2 slowly changing dimension. Remember that Type 2 perfectly captures history, and requires you to issue a new customer record each time any attribute in the dimension changes. Kimball Group has have long pointed out this practical conflict by calling this situation a "rapidly changing monster dimension." The solution is to reduce the pressure on the primary customer dimension by spawning one or more "mini-dimensions" that contain the rapidly changing status or behavioral attributes. We have talked about such mini-dimensions for at least a decade.

In our real-time, extreme status tracking world, we can refine the tried-and-true mini-dimension design by adding the following requirements. We want a "customer status fact table" that is...

  • a single source that exposes the complete, unbroken time series of all changes to customer descriptions, behavior, and status;
  • minutely time-stamped to the second or even the millisecond for all such changes;
  • scalable, to allow new transaction types, new behavior tags, and new status types to be added constantly, and scalable to allow a growing list of millions of customers each with a history of thousands of status changes;
  • accessible, to allow fetching the current, complete description of a customer and then quickly exposing that customer's extended history of transactions, behavior and status; and
  • usable as the master source of customer status for all fact tables in the EDW.

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