RE: RE: A Two Day Workshop for Software Architects
> From: Bullard, Claude L (Len) [mailto:clbullar@i...] <snip/> > A data warehouse solves the problem by creating a central server > for the storage of common data in an agreed upon common format that > is optimal for querying and doing common tasks. A good example > is analysis. Many enterprises contribute data to a statistical > system used to make forecasts about business conditions and needs > for resources. This warehouse would not be useful for actually > doing the business of the business but is for managing the business. > Many can't see the difference or understand that the warehouse > may not be a common repository for all business information, that > in fact, multiple warehouses may be needed. VITAL used a much richer model for guiding data warehousing efforts than those that have become the norm. It was really about a hub and spoke model for sharing shared information, not a single monolithic repository for querying and analysis. VITAL used manufacturing and commerce as an analogy. Factories produce goods that are exchanged with distributors, who use central warehouses for inventory. The distributors provide the goods to retail outlets, who naturally may get goods from other sources, as well. Consumers shop at the retail outlets, not at the distributor or factory. In an enterprise, you tend to have information producers and information consumers. VITAL said, define that information that must be shared across the enterprise. Define who are the legitimate producers of that data. HR, for instance, would be the owners of data pertinent to an employee's status as an employee. You don't want a free-for-all where anyone can define that info. The data warehouse is a central distribution center for shared information, but individual groups will need info beyond that, and they may even need a different view of that data. So they pull it into their domain, transform it as needed, relate to other data they own, but they don't create data that they don't legitimately own. That leads to data redundancies, which leads to disconnects between systems, which leads to inconsistent answers that can negatively impact decision making, or lead to a costly foul-up in a business process. I witnessed some of those first-hand. I specifically remember a VP going on a tirade threatening that "heads would roll" because he got two different reports from two different IT groups that directly contradicted each other on sales figures, and no one could tell him why. It's an interesting -- and powerful -- model. But of course, in the real world, things aren't all black and white. You tackle what you can and don't fret that you haven't achieved perfection.
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