Harden 'Soft ROI' Projections with Research, Prototyping


Business cases for large projects require a lot of work. Yesterday we looked at a 66-page sample business case for a data warehouse project. Finances are, of course, fundamental to any project. They are also quantifiable, which can't always be said about improvements in business processes and operations.


The sample business case, provided by our partners at gantthead.com, also illustrates the importance of developing functioning models and meaningful projections about business process impact. IT Business Edge members can access the free business case sample here in the IT Downloads library.


As we mentioned yesterday, the data warehouse project being evaluated in the sample business case is proposed to support three distinct initiatives:


  • Waste Water Treatment Analysis
  • Treatment Process Regulatory Requirements Analysis
  • Maintenance Scheduling Decision Support


To create a priority schedule for the three initiatives, the project team created stakeholder focus groups and surveyed them about various upsides - the report dubs them "Soft ROI-Intangible Business Benefits" - such as the ability to readily research process improvements. Focus group members ranked each of the three projects against these "soft ROI" categories, and the results were tabulated and added to a matrix along with hard ROI data on hardware and the like. It's not hard science, but it is more tangible than "executives expect to see improvements in ," which often passes for a value statement in business cases.


The document then goes on to present detailed business cases for each of the three candidate initiatives, all of which include a prototype project based on high-level business requirements derived from the focus groups. Not all projects need a prototype, but again, a data warehouse build-out certainly merits the expense. The prototype for the Waste Water Treatment Analysis initiative, for example, discovered specific flaws in existing data that made it impossible to do some desired analysis - waste flow data was not tied to units, so analysts didn't know if they were talking gallons or cubic yards. That's a far more meaningful assertion than "we think the data quality is poor."


For the Maintenance Scheduling decision support candidate, prototyping resulted in solid analysis of how the company could save money by optimizing its equipment refresh cycle, as you can see in the image below.



All of these data points can create compelling arguments in support of a project.


The sample business case goes on to lay out data models and business processes in enormous graphical detail. It's a very impressive bit of work. Even if you are not considering a data warehousing project, be sure to check it out as an example of detailed justification research done well.

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