In the world of marketing systems, no cliché is more popular than "360- degree view of the customer." This refers to assembling a complete set of data from all systems that record customer interactions. The underlying assumption is that this comprehensive picture will allow highly tailored sales and service treatments that ultimately return higher profits.
Of course, reality is a bit more complicated. Not all data is equally valuable nor is it equally accessible. Therefore, determining exactly which data to gather requires balancing business, technical and political factors. The result is something less than a complete view of the customer, although hopefully still broad enough to be useful.
Traditionally, this merged data has been placed in a central repository such as a data warehouse, where it is used for analysis and reporting. Feedback to operational systems is mostly through batch transfers such as lists of customers with segment codes. When fresher data is needed (in particular to react in real time or near real time to customer activities), a layer such as an operational data store is added to consolidate new transactions as they occur and, in the most advanced systems, perform analytics and select appropriate reactions.
Yet these real- time layers are still basically appendages of the underlying data warehouse. Given the difficulty of merging data from different sources, it makes sense for the real- time system to leverage the consolidation functions already built for the warehouse, rather than recreating them separately.
However, not every firm has a data warehouse, and not every warehouse can be effectively adapted to support real-time interactions. At the same time, providing operational systems with a shared (if not necessarily complete) customer view often yields tangible benefits. As a result, many firms are looking for a solution that produces this shared view without the foundation of a traditional data warehouse.
Companies that focus specifically on providing a real-time view of current, consolidated customer data include DWL, Journee, Nimaya and Siperian. Because sharing customer data is technically similar to sharing other types of data, these firms also compete with less specialized data integration and synchronization vendors such as GoldenGate, MetaMatrix, Ascential and DataMirror.
The general approach of the customer data sharing systems is to present a single resource that operational systems can access when looking for customer data. This resource may be a physical data store (DWL and Siperian) or it may be a data model that is mapped directly to the actual source systems (Nimaya and Journee).
The obvious advantage of building a physical record is that it provides quick, consistent access times: the data is already assembled in one place, so there are no problems with slow or inconsistent response times from the original sources. This approach also makes it easier to deploy sophisticated matching and reconciliation schemes because these processes take place while data is being loaded in the background rather than when an operational system is impatiently waiting for a response. Updating the customer record whenever source data changes also lets these systems generate alerts or kick-off business processes in response to specified events. This allows proactive customer treatment rules to be built directly into the data sharing system rather than requiring a separate process that scans for significant changes.
The mapping approach, which lets operational systems read customer data directly from source systems, has its own advantages. Reading the source systems clearly ensures that the most current data is presented. This is particularly important when managing real-time interactions, where knowledge of the customer's latest activity is essential. The on-demand approach may also reduce the total amount of processing because source data is extracted only when needed. Thus, information that changes frequently but is rarely used such as a bank account balance is posted much less often. Both Journee and Nimaya include options to store customer data internally when on- demand access is inappropriate.
Determination of the architecture that makes the most sense will naturally depend on the circumstances. The fundamental advantage of these products making it easier for multiple systems to access unified customer data is the same. Compared with general- purpose data integration tools, the specialized products provide some features aimed at the particular problems of managing customer data, such as selecting among conflicting values for the same element and maintaining hierarchies of relationships among individuals, households and businesses. Perhaps surprisingly, none of these systems appear to have their own fuzzy matching engine to identify related customer records from different systems.
The long-term prospects of specialized customer data sharing systems are unclear. Buyers may find their features worthwhile or may choose to apply general-purpose data integration products; or, they may even make one operational system the primary customer data repository. However, for companies with immediate specific requirements, these products provide an option that's worth a look.
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