Do you struggle with coming up with good Leap-of-Faith Assumptions (LOFAs) when applying FastWorks? That is, do you have a hard time articulating the testable hypothesis (a statement proposing some relationship between two or more variables that can be tested) ?
The best article I’ve seen on the topic really helped me some time back. It’s from the go-to thinker on applying analytics in business contexts, Prof. Tom Davenport. In this classic Harvard Business Review article from 2009 titled “How to Design Smart Business Experiments”, Prof Davenport outlines one of the most difficult things to wrap one’s head around: what makes a smart business experiment. Here’s a key quotation from the article, “Formalized testing can provide a level of understanding about what really works that puts more intuitive approaches to shame.” For me, this article helped clarify how to clarify what could be testable, how to test it quickly, and how to learn from the results.
The image below (attached) adds an additional and important point on the same: how to make sure that the experiments run are shared and thus encourage a culture of testing hypotheses and learning from them.
The article is incredibly practical, offering some good rules of thumb, especially for those of us looking to apply FastWorks as a manager. Of course, FastWorks is a lot more than hypotheses and experiments. But the first step is understanding how to move quickly, testing assumptions that have a high impact to success if wrong and are quick to test.
Do you have anything you’ve done to get to testable hypotheses more quickly?