The business case you deliver must score high in credibility, accuracy, and practical value
Business Case Risk: Will We Really See The Predicted Results?
Deliver Case Results and Business Case Risk Comes With the Turf.
The business case predicts financial results, but these predictions come with business case risk.
Those proposing business investments and actions rely on robust business case analysis (BCA) to predict the likely outcomes. However, business case risk is always present.
Everyone knows, however, that not even the most rigorous BCA cannot eliminate business case risk—uncertainty about the results of business decisions. Through BCA can, however, cut uncertainty, measure what remains, and deliver tools for managing risk as the action goes forward.
Senior business managers say, increasingly, that the margin of tolerance for management error is shrinking, visibly and tangibly. In the current business climate, therefore, there is urgency to questions like these:
How do we know that we’re going to see the results your case projects?
Are we sure this is the best business decision?
How can I prove, later, that I’m acting responsibly now?
Most people know that business case analysis attempts to answer questions like these, firmly and finally. That is what decision support is all about, after all. Nevertheless, decision makers with business case results in front of them, still ask the fundamental question:
Is there any certainty possible in the business world?
Business Case Risk Analysis: The Underlying Principles Are Simple
There is no way to avoid the reality that credible, practical answers to questions like those above call for rigorous quantitative risk analysis. The subjective “heat maps” that many proposal authors give as “risk analysis” just will not do. In fact, the kind of certainty described here comes from the world of elementary statistics.
Unfortunately, merely mentioning “statistical thinking” raises a roadblock for many. As a result, they make proposals hoping a subjective risk assessment is an adequate substitute for credible quantitative analysis. It is no surprise when these investments and actions fail.
Fortunately, applying what follows below calls only for the analyst to understand a few fundamental probability concepts from a basic introductory statistics course—the one that schools offer for “non-statistical people”. No one needs to go beyond that.
Moreover, most of the leading risk analysis software comes with excellent user guides that do not even assume that much background.