At fiscal year end, many business executives will be musing over the proposed IT budget and find proposals for electronic commerce, customer relationship management and data warehousing. Electronic commerce provides new ways for customers to buy from the company, and customer relationship management supports a corporate initiative and gets us closer to the customers. It may be difficult to delineate a clear voice of data warehousing from the general background noises of these projects with seemingly more impact on the company mission. If your data warehouse program has not done a good job at proving its value to the organization, it may come under scrutiny. The longer you go without proving the value, the harder it will be to continue its justification.

Regardless, the reality is that initiatives such as CRM and e-commerce cannot properly be executed for long-term efficiency without a data warehouse.

Many vendor products provide their own embedded run- time analytical DBMS; and if you feed this DBMS from your operational systems, it will have just enough data to execute the function it was meant to perform. This may even be called a data warehouse.

Where will CRM systems get their data? Is it from a system that encompasses a total enterprise view with executive committee sponsorship or is it just sponsored by the executive who has been afforded stewardship of this subject? If not, what we have are data marts providing a proprietary view of the customer, probably fed direct from the operational system(s) usually with data quality warts intact. This is the continuance of a non-integrated architecture. Imagine the targeted marketing application the account planning application, and the customer profit reporting application all having radically different views of the customer.

Usually what happens is numerous, long-term unarchitected data marts put further distance between you and an enterprise data warehouse architecture in which can reside the corporate view of customers, products, sales, etc., with all the benefits of being able to feed this view to future CRM applications.

It's the data warehouse architecture that needs to envelop these disparate decision support data stores. The architecture contains footprint data stores (staging, ODS, the data warehouse) for, at a minimum, reconciling the corporate view of the subject areas.

How does a data warehouse architecture support CRM?

  • By providing CRM vendor and in-house applications with the enterprise view of the subject matter that it needs. This may mean direct reads of the data warehouse, an existing data mart or a new data mart for the application.
  • By receiving analytical data that the applications generate that is interesting at the enterprise level.

Architect CRM applications with the data warehouse architecture as the backbone of data storage. Don't take the shortcut approaches, propagated by the application vendors which makes the application work for short-term success only. Likewise, no data should get into the data warehouse without the enterprise blessing from corporate management, thus removing the possibility that data has entered the warehouse that cannot be fully leveraged. Downstream projects can take advantage of the subject area having been defined in the data warehouse.
Develop the data warehouse in a series of iterations that support business projects, build on each other and provide fast ROI within a scalable architecture. Figure 1 shows an example of such an iteration plan. Future applications can take advantage of the subject areas already residing in the data warehouse that are useful to them. The focus can then be on the application itself. By supporting the analytical information needs of CRM applications and receiving analytical data out of these environments, the data warehouse team builds the enterprise data warehouse iteratively.

Iteration Subject Area Addition Application(s)
1 Implement data warehouse architecture
Targeted Marketing
2 Sales & Time Customer Profitability Analysis
3 Product Product Profitability Analysis
4 Raw Materials Procurement Systems
Demand Planning
Supply Chain Planning
5 Promotion Promotion Effectiveness

Figure 1

Keep measurably improving the data warehouse in the organization so that at budget review time the data warehouse will come under less scrutiny and it can lead the way in being the data store for CRM. Don't run a hobby shop. Encompass CRM application data within the architecture of the data warehouse.

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