(using throwaway account to post this)

Very true.

I was recently involved in a reasonably huge data mining & business intelligence task (that I probably should not disclose). I could say this was an eye-opener, but I am old enough to be cynical and disillusioned so that it was not a surprise.

First, we had some smart people in the team (shamelessly including myself :-), "smart" almost by definition means "experts in programming, sw development and enough mathematics and statistics) doing the sw implementation, data extraction and statistics. Then there were slightly less smart people, but experts in the domain being studied, that were supposed to make the sense of the results and write the report. These people were offloaded from the team, because they were very urgently needed for other projects.

Second, the company bought very expensive tool for data mining and statistical analysis, and subcontracted other company to extend it with necessary functionality. The tool did not work as expected, the subcontracted extension was late by 2 months (they finished it at the time the final report should have been made!) and it was buggy and did not work with the new version of the tool.

Third, it was quite clear that the report should be bent towards what the customer wants to hear (that is not to say it would contain fabricated data - just the interpretations should be more favourable).

So, those smart people spent their time in 1) implementing around bugs in the sw we were supposed to use, 2) writing ad-hoc statistical analysis sw to be able to do at least something, 3) analysing data in the domain they were not experts in, 4) writing the report.

After all this, the report was stellar, the customer extremely satisfied, the results solid, the reasoning compelling.

Had I not been involved and had I not known how much of the potential had been wasted and on how small fraction of the data the analysis had been performed, I would consider the final report to be a nice example of a clever, honest, top level business intelligence job.

I'm not buying your elevator pitch.

To be frank, I didn't expect you to based on our previous conversations on forecasting. You are too skeptical of it, and haven't read some of the recent research on how effective it can be in a variety of situations.

is not true because they lack people smart enough to correctly process the data, interpret it, and arrive at the correct conclusions.

Exactly, this is the problem I'm solving.

So, what's wrong with the stock price as the metric?

As I said, the signaling problem. Using previous performance as a metric means that there are lots of good forecasters out there who simply can't get discovered - right now, it's signaling all the way down (Top companies hire from top colleges, take from top highschools). Basically, I'm betting that there are lots of organizations and people out there who are good forecasters, but don't have the right signals to prove it.

Besides, evaluating forecasting capability is... difficult. Both theoretically (out of many possible futures only one gets realized) and practically (there is no incentive for people to give you hard predictions they make).

You should read the linked article on prediction polls - they weren't even paying people in Tetlock's study (only giving giftcard gifts not at all comensurate to the work people are putting in) and they solved the problem to the point where they could beat prediction markets.