With the advent of data warehousing came the realization of an inherent information value chain. While that value chain has been around for some time now, a dramatic shift needs to occur in it for organizations to remain competitive in today's business climate. The chain starts with legacy systems that either capture customer transactions or perform back-office accounting for them. Detail data is then scrubbed and transformed on its way to the data warehouse, where it actually becomes information (see Figure 1).
At this point in the information chain, the value-added processing occurs. Now that the detail data from many disparate legacy systems is integrated in the warehouse, we begin to add some value to it. If we are in the communications or financial services industry, for example, we may "household" our customers (determine customers that transact together as a household). Our organizations segment our customers based on their preferences, buying history and demographics, and determine what a customer's next purchase is likely to be. We ascertain customer profitability based on cross-system account/product relationships. These activities add tremendous value to the data that already resides in the data warehouse as it finds its way into data marts. Sometimes the value-added data resides only in the mart; in other cases, it is added back into the data warehouse as well. In only a few instances does value-added information find its way back into the legacy environment.
The information chain has pretty much looked like Figure 1 since data warehousing began. If we add an element of time to the value chain, we see that warehouses, by their very nature as a snapshot in time, are updated weekly, monthly or sometimes even quarterly. After the warehouse is updated, it can take anywhere from one to 30 days or more to apply the value-added processes and load the resulting information into the data mart. In fact, it can take from 30 to 120 days after a transaction occurs in the legacy environment before information from the value-added processes is available for the organization to utilize within its transactional environment (if indeed it ever finds its way there). Up until now, that was satisfactory since in many cases it was all we had.
Now, however, things are different. Competitive business demands are forcing us to know our customers and their behaviors much earlier in the information chain. It's no longer acceptable to wait until customers are householded, segmented and analyzed after they hit the data warehouse to understand their profitability or what their next purchase is likely to be. The Web has pushed us to instead know real-time to which segment a customer belongs, whether the transaction they just generated improved their overall profitability and whether, based on their recent behavior and the behavior of other customers like them, they are likely to leave our company and go elsewhere.
The need for value-added information is movin' on up the information value chain.
We now need to find ways to perform these value-added processes sooner. We need to move these processes out of the back end of the information chain and up into the front end, closer to where the transactions actually occur. Operational data stores that are updated in real-time with messages from the transactional systems can help provide the foundation for zero-latency processes that add value to information. Fortunately, several products are also emerging that perform real-time analysis of transactional data with the intent to suggest or recommend appropriate customer retention strategies, or cross-selling strategies, for example. Net Perceptions has software that leverages e-commerce purchase data and customer activity data to bring merchandising messages to customers at the right moment in time. RightPoint Software (formerly DataMind Corporation) offers products for developing, targeting, executing and evaluating real-time marketing campaigns for call centers and Web sites. TriVida Corporation offers products that help make each customer interaction as effective as possible by facilitating real-time data analysis and action-taking, such as identifying customers likely to defect and suggesting strategies to help retain them.
And there will be more products entering the market soon. As with any new technology, there are risks associated with utilizing these new real-time analysis engines. However, there may be bigger risks in not doing so. Business pressures will force all of our organizations to reduce the cycle time of our information processes. It's just a question of when. Leading organizations will improve the speed of business quickly. Followers will still have to make the process shift to remain competitive or possibly risk going out of business. Companies that figure out this shift will find themselves not only "movin' on up" the information value chain, but "movin' on up" in their business markets as well.
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