I'm not buying your elevator pitch. Primarily because lots of data is not nearly enough. You need smart people and, occasionally, very smart people. This means that

companies had access to tons of data that they could use to ACTUALLY make better decisions

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

the management consulting companies would come in as outsiders, charge a bunch of money, and use their clout to use the data to make big decisions

is also not quite true because companies like McKinsey and Bain actually look for and hire very smart people -- again, it's not just data. Besides, in a lot of cases external consultants are used as hatchet men to do things that are politically impossible for the insiders to do, that is, what matters is not their access to data but their status as outsiders.

there's no objective way to tell which companies are actually good at making decisions

Sure there is -- money. It's not "pure" capitalism around here, but it is capitalism.

An objective metric(bayesian scoring rule) that shows how good an organization or individual is at predicting the future.

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

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).

I don't think that McKinsey's and Bain's business is crunching data. I think it is renting out smart people.


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 ... (read more)

7lusername4y(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 e

Open Thread, Dec. 28 - Jan. 3, 2016

by [anonymous] 1 min read27th Dec 2015145 comments


If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.