Today I ran across some discussion about Agile Development in Data Warehousing, and note that we talk about this in the context of the DW development, but not in relation to the Business. I believe there is a need to discriminate some of these processes quite differently. Most simply put - One is applying Agile to DW development; the other is applying Agile to Business Analysis.

Core DW foundations involve modeling root components of business data needs and implementing a data model which allows for flexibility to answer questions of the data - a concept I call "Designing for the Unknown". The more renormalization and change from the source system, typically the more transformation logic and less flexibility, and ergo higher cost and less organizational agility.

Effective Agile development of the DW infrastructure itself involves delineating the methodologies which can be used for what types of development functions. For example, creating a core or "root key" entity in the data model

Over the last year I have spent a good amount of time thinking about the cost of analytics, and a few things worry me about our industry and how vendors price in this industry.  In the Data Warehousing and BI industry, we're starting to see pricing models based on data volume or size. I know of one vendor which prices by specint, so - get a bigger system or virtualize your systems - get a big bill.

 The problem with these licensing schemes is that they actually make the cost of doing analytics more expensive over time.  If I am a good technology user, over time I'm driving down the costs per unit of data volume to create and maintain the data flowing into my DW.  In the simple case you could say that the cost to generate data is declining at the inverse rate of Moore's law, particularly for a web business where most of the cost is in hardware.

Back to the point, using Moores Law as the example, the cost of data generation declines per unit of volume at a rate of ~50% every 18 months.