|
Why Customer Data Integration (CDI) Projects Fail - Data Model Inflexibility
|
Spreadsheets have been around since the late 1970s. They were an instant sensation. Over the years, spreadsheets have evolved and matured, but the basic form and substance of spreadsheets has hardly changed. While new features and capabilities continue to be added to spreadsheets, for the most part spreadsheet technology has reached a plateau. This is typical of a highly successful product. However, it is becoming clear that new approaches and paradigms should and are beginning to emerge.
|
|
In a world where customer acquisition costs are soaring and customer retention - particularly retaining profitable ones - is getting harder for every enterprise, “knowing your customer” is not just a slogan but a business mandate. However, in order to truly know its customers, an enterprise needs to create a unified and comprehensive customer view from all its disparate data sources, including customer databases and customer information files, financial and order management systems, product catalogs, and external data services. Once integrated, these unified customer views provide the entire enterprise with the ability to drive customer-centric strategies. However, using existing technology platforms to build and manage such unified customer views—across disparate data sources, applications and channels—can be a complex and costly exercise and often achieves only limited success.
One of the primary culprits driving the astronomical expense, and often failure, of customer master data hub projects is the lack of flexibility in the underlying data model of the solution. This is especially true for CDI vendors who have anchored their solution around existing applications such as Customer Relationship Management applications or around a fixed industry data model such as retail banking or insurance. This lack of flexibility becomes their Achilles heel, as significant customization is often required to enable the data model to reflect the existing systems and business requirements. With a heavily modified data model, the business services that sit on top of it break, along with any management logic that is bound to the fixed data structures. Further, with no flexibility in limiting the scope of customer data types that must be modeled within the customer hub, the project becomes much larger than necessary, which adds to the overall risk of failure.
Therefore, it is critical for companies to review the data model flexibility of various CDI solutions to ensure success of their projects both in the short and long term. The following provides some of the key factors to consider when determining if a data model is flexible:
Data Model: Brittle or Extensible?
Since a customer hub is highly specific to each enterprise – in fact to each business unit within the enterprise – the critical question is not whether an out-of-the-box data model is “rich”. In fact, like a pre-fixed menu at an expensive restaurant, it may well be too rich with extraneous attributes not appropriate for a scalable master hub. The essential question is: how readily does the solution permit the data model to be modified? Most CDI solutions offer a fixed albeit rich data model developed from their unique application perspective. When these solutions attempt to adapt this to the true needs of a business outside their original design framework, the model either cannot provide the entity and relationship structures necessary or it requires extensive and expensive customization. Also, once complete, the resulting data model is highly inefficient due to the processing burdens associated with combining the existing application logic and the modified model.
|
|
|
Oracle #1 in Business Analytics According to IDC Research
|
The Business Intelligence Search Engine has all the answers.
|
Find all you need on The Business Intelligence Search Engine.
|
|
|
|
|
|
|
|
Other
Articles by this Author
|
|
|
