Building BI

Course taught by David Wells

Notice that the title doesn’t say “Choosing the Right Data Warehousing Architecture?”  After some brief definitions to get everyone on the same page, the first order of business recognizing that an approach to data warehousing is bilateral.  An approach must account for (1) the Data Architecture, and (2) the Project Architecture, both existing within the context of a company’s environment and culture.

Data Architecture speaks to “What is being built” while Project Architecture is about “How it gets built.”  Project teams and sponsors often confuse the two architectures or don’t recognize the distinction between them.  These aren’t completely independent aspects of building a data warehouse, though.  Certain data architectures are best implemented with specific project architectures.  The “how” has to be compatible with the “what.”  Data architectures based on a high degree of integration are best developed and maintained with project methodologies that can drive standardization and agreement across the enterprise.

I’ve seen this at my own company – our data architecture is based on Inmon’s Corporate Information Factory.  We have high levels of integration before the data ever leaves our “hub” for the downstream marts and extracts.  When efforts to standardize data or definitions for that integration effort are not facilitated “top-down”, we flounder and end up with eight slightly different versions of the same field just to keep all of the departments and end-users happy.  We are much more successful when the standardization is driven by senior executives at the corporation, we reach that single version of the truth.

Forgotten or disregarded even more often than Project Architecture are the constraints coming from the environment.  This is where a company’s culture, values, and organizational structure influence project execution.  Again, from my own experience in a company with a very decentralized and segmented organizational structure, getting to the standards needed for true enterprise capabilities is a struggle.  More on the influence of culture and organizational identity on project execution can be found at Stanford’s Advanced Project Management series.

I was only in this session until mid-day, so I missed the second half of the course.  Before wrapping up for lunch, we covered 16 critical factors to consider when determining your data warehousing approach.  Covering topics like integration levels, metadata needs, latency, costs to deploy and support, and scalability, these metrics are also great for evaluating where your current weaknesses and evaluating where your data warehouse program needs to go.  Great session – learned a lot, remembered a lot.