A couple of things define these requests:
- They're looking for some "data magic"... essentially some large scale statistical model to figure out the problem, define a path to solution, implement some kind of decisioning, or at the very least, explain why they shouldn't be fired.
- By the time they come to me the situation is really bad.
The second point here is interesting, because of a correlation I've observed:
The worse a data problem seems, the simpler and more fundamental the likely problem.
This means that I field quite a few requests, where the solution is quite simple, and generally, quite upsetting to the business. Here are a few examples of data science requests, matched with their solutions:
Q: Can you put a model together to determine why our paid search campaign isn't working?
A: The "apply now" button has been broke for six months.
Q: Why is our web conversion rate low?
A: Because your website looks most like a spam site.
Q: Why are final revenue numbers 20% lower than initial, is accounting wrong?
A: Because you have a 20% cancellation rate.
Q: Why aren't sales people doing what we want?
A: Because your incentive plan creates a perverse incentive not to work hard.
I realize that these type of problems will likely continue to come my way, but they form the basis for an important lesson learned:
When analyzing seemingly horrible and counter-intuitive business results, start from the beginning, and look for the simplest solutions.