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Data Warehousing Lessons Learned:
Unmet Requirements Drive Data Warehousing Spending Expectations

  Column published in DM Review Magazine
September 2004 Issue
 
  By Lou Agosta

Data Warehousing Spending Expectations are Strong

The cruel cost economy of the past two and a half years has struggled forward to a steady drum beat of cut costs, cut costs, cut costs. During this time, unmet end-user expectations for data warehousing have built up. This is driving an increase in spending expectations.

According to the February 2004 Data Warehousing Institute (TDWI)-Forrester Quarterly Technology Survey, 41 percent of practitioners expect spending on data warehousing to increase by 11 percent or more; and 25 percent expect an increase of more than 20 percent during the next budget period. This increased from 27 percent in May 2003, when 33 percent of respondents expected spending to increase by less than 10 percent and a whopping 38 percent were simply too unsure to make a prediction.1 In contrast, approximately 7 percent expect data warehousing spending to decline, and the number who are uncertain has declined to 24 percent (see Figure 1).


Figure 1: Data Warehousing Spending Changes

In a separate survey conducted via e-mail in April 2004, Forrester drilled down and inquired about the reason for the spending increase or decline. We learned:

  • Unmet data warehouse end-user requirements drive growth. Forty percent of respondents who report a spending increase cite increased end-user requirements as the catalyst.2 Clearly, repressed end-user demand is pushing its way to the surface. Enterprises that are using data warehouses to reduce costs through inventory reduction are building customer data warehouses to promote customer cross-selling and up-selling, and vice versa.
  • Infrastructure spending drives growth. A significant number of respondents - 37 percent - are increasing spending on databases and infrastructure (17 percent), data integration technology such as extract, transform and load (ETL) (13 percent), or query and reporting and OLAP (7 percent).
  • Consolidation drives savings, freeing funds for other priorities.3 In the case of the 7 percent of respondents with declining spending, the reasons cited were spending on other priorities and across-the-board budget cuts. In some cases, albeit a minority, enterprises are pocketing the savings from a positive data mart consolidation program.

The strong spending expectations are a powerful statement of direction and guidance from the practice of end-user enterprises.

First-generation data warehouses answered the basic business question of what customers were buying or using which product or service, and when and where they were doing so. Second-generation data warehouses took this processing up a level. By drilling down on the product dimension and customer dimensions (respectively), second- generation data warehouses enabled demand planning, forecasting and procurement warehouses as well as cross- and up-selling for incremental revenue. Third-generation data warehouses go beyond active data warehousing with its high volumes, large numbers of users and complex workloads to close the loop from the data warehouse to the transactional system. This bidirectional process results in integrated business intelligence and entails several recommendations:

  • In validating data warehousing spending, use unsatisfied requirements to justify building data warehousing infrastructure where the development will produce specific business advantages such as reduced costs in carrying inventory, improved operating efficiencies in forecasting and the generation of incremental revenue through customer cross- and up-selling.
  • Those firms without a data warehouse should adopt the technology in order to track market trends and answer questions such as what customers are buying or using which products or services, and when and where are they doing so. This applies to those firms already operating a first- or second-generation data warehouse and addressing decision support issues such as inventory reduction on the product side or lifetime customer value on the customer side. They should invest in third-generation active data warehousing to attain integrated business intelligence.
  • Integrated business intelligence should be enabled by using the data warehouse to optimize transactional processing. If you have a demand planning data warehouse, close the loop back to the transactional system and source the demand. If you have a customer hub data warehouse, use it to capture the results of the promotion and marketing campaign and optimize the allocation of customer resources. As many experienced data warehouse practitioners have learned, this sounds simple, but it requires a significant piece of design work and represents an advanced application.

References:

  1. These numbers were from the Data Warehousing Institute Quarterly Technology Survey of May 2003. Base: 120 respondents
  2. Source: Forrester Data Warehousing Email Survey, February-April 15, 2004. Base: 43 respondents.
  3. Source: Forrester Data Warehousing Email Survey, February-April 15, 2004. Base: 43 respondents.
...............................................................................

For more information on related topics visit the following related portals...
DW Administration, Mgmt., Performance and DW Basics.

Lou Agosta is the lead industry analyst at Forrester Research, Inc. in data warehousing, data quality and predictive analytics (data mining), and the author of The Essential Guide to Data Warehousing (Prentice Hall PTR, 2000). Please send comments or questions to lagosta@acm.org.

 

 

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