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The Do's and Don'ts in Building a Customer-Centric Data Warehouse

Published
  • January 01 1999, 1:00am EST

 

The importance of customer-centric data warehouses is accelerating with greater business focus on customer relationship management (CRM). Here are some guidelines to follow in implementing your customer data warehouse:

 

Do's Don'ts

Do implement a customer data management architecture. Set up a central customer data repository to feed multiple analytical applications and to tie in operational CRM applications. This is the best way to ensure the quality and consistency of results while ultimately saving time and budget.

Don't underestimate the importance of data quality, especially with customer data. With customer-centric data warehouses, data quality is critical to ensure that you are correctly identifying the complete customer relationship.

Do include strong name and address parsing, correction and standardization capabilities to dramatically improve the accuracy in customer matching across different source systems.

Don't assume that the tools you have used for product or financial data warehouses or data marts will work well for customer-centric projects. The integration and cleansing intensive requirements as well as the significantly increased data volumes generally demand specialized tools.

Do use business rules to define the customer matching and merging parameters to ensure the creation of the best possible integrated customer view.

Don't rely on customer ID, SSN, date of birth or telephone numbers alone to consolidate customer data across different sources. The data is often incomplete and unreliable.

Do plan for the update process as well as the initial load. Be sure you can incrementally update your existing customer data repository to preserve the historical information and meet your batch-processing windows.

Don't select cleansing tools that were designed for batch-only mailing applications. These tools were not designed for relational access, update or meta data requirements of data warehouses.

Make sure you select tools that are designed to scale for extraction, integration and delivery. Customer-centric data warehouses, because of the data granularity, typically have 10 to 20 times more data volume than product-centric data warehouses.

Don't underestimate the importance of meta data in keeping pace with the rapidly evolving requirements of customer-centric data warehouses.

Do expect to pre-summarize data for commonly asked questions. The increased data volume of customer data warehouses has a big impact on query performance.

Don't underestimate the future possibility of distributing analytical results to the operations systems to support closed-loop marketing.

Do benefit from an integrated transformation and cleansing tool in order to minimize licensing, integration, support and maintenance costs.

Don't overlook future requirements such as the need to include additional internal data sources or external demographic data.

Do design a data warehouse architecture that will scale from pilot to enterprise deployment.

Don't expect sales force automation software or other CRM operational packages to solve the customer warehouse integration issues for you.

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