The choice between different formats of cloud computing (IaaS, SaaS mostly) and their comparison to internal IT business service deployment must be based on objective criteria. But this is mostly uncharted territory in IT. Many organizations have difficulties implementing a realistic chargeback solution, and the real cost of business services is often an elusive target. We all agree that IT needs a better form of financial management, even though 80% of organizations will consider it primarily as a means for understanding where to cut costs rather than a strategy to drive a better IT organization.

Financial management will help IT understand better its cost structure in all dimensions, but this is not enough to make an informed choice between a business service internal or external deployment. I think that the problem of which deployment model to choose from requires a new methodology that will get data from financial management. As I often do, I turned to manufacturing to see how they deal with this type of analysis and cost optimization. The starting point is of course an architectural model of the “product”, and this effectively shows how valuable these models are in IT. The two types of analysis, FAST (Function Analysis System Technique) and QFD (Quality Function Deployment), combine into a “Value Analysis Matrix” that lists the customer requirements against the way these requirements are answered by the “product” (or business service) components. Each of these components has a weight (derived from its correlation with the customer requirements) and a cost associated to it. Analyzing several models (for example a SaaS model against an internal deployment) would lead to not only an informed decision but also would open the door to an optimization of the service cost.

I think that such a methodology would complement a financial management product and help IT become more efficient.

For more details about value analysis: a very informative site on Value Analysis (http://www.npd-solutions.com/va.html).

Regards,

JP Garbani