There is only so much you can do, particularly when it comes to other people. You can't make them learn. You can't make them happy. You can't make them change behaviors that they are unwilling or unable to do.
There is only so much you can do, particularly when it comes to other people. You can't make them learn. You can't make them happy. You can't make them change behaviors that they are unwilling or unable to do. You can't make people into entrepreneurs. We should call a moratorium on business plan competitions. You can't predict job performance based on interview performance. Yet, many of us waste a lot of time, effort and money trying. People have to do these things on their own.
Another thing most people can't do is pick technology, product or business winners. Yet, particularly in digital health, many seem to think they know how to do it. Professional stock pickers can’t beat the indices, so why would you think you could?
Perhaps another way is to let people do it themselves and call their own babies ugly. How about using the scientific method? The science community relies on investigators to create hypotheses, design experiments to generate data, analyze the data and decide whether the findings are significant enough to accept or deny the null hypothesis i.e. (in a statistical test) the hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error. If the hypothesis is rejected, then the paper is submitted for review by a panel of peers who decides whether it should be published. The peers don't create the data, validate it and write the paper, the scientists do.
Here are some ways to create a flawed business model by misapplying experimental methods:
1. They offer products, models and services they should not
2. They don't offer products, models and services they should
3. They think the market is smaller that it really is
4. They think the market is larger than it really is
5. They design experiments incorrectly so they get misleading data
6. They misinterpret the data obtained from well designed experiments
7. They test for things that are interesting but not important
8. They use testing techniques that bias the results
9. They are blind to substitutes or other environmental threats that makes the experiments irrelevant
10. They take too long and spend too much money doing the experiments.
Likewise, we need to give innovators the tools they need to decide whether to accept or reject the null hypotheses that underlie their business model and that will eventually determine their failure or success. We need to encourage them to be euthanators and kill ideas before their technologies or models grow up to be mutants and we have to retract the paper.