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Comment by peatmoss | Hacker News Reader

> Data Scientist's job is to launder management's intuition using quantitative methods

Ouch. This is savage, but sadly correct in many cases.

HOWEVER, to play devil's advocate here, I've also seen corporate data scientists overstate the conclusions / generalizability of their analysis. I've also seen data scientists fall prey to believing that their analysis proves would should be done, rather than what is likely to happen.

The role of an executive or decision maker is to apply a normative lens to problems. The role of the data scientist / economist / whatever is to reduce the uncertainty that an action will have the desired effect.

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Yep. At this point, I essentially don't trust any ML result that shows > 95% accuracy.

So often, those models proved to be over-fitted and not generalizable.

But too many decision makers simply can't properly judge such results.

Good point. Data is one aspect of making a decision. The other aspect is understanding the industry and environment. Often data scientists give just one variable needed to make a decision. In health care for example you need to factor in a whole host of legislation. You also need to factor aspects of the industry not reflected in the data. As an example doctors not wanting to use iPads is something you can't measure and can't force as company. Even though data analysis might suggest this is the way to go.

> The role of an executive or decision maker is to apply a normative lens to problems. The role of the data scientist / economist / whatever is to reduce the uncertainty that an action will have the desired effect.

Where do business analysts fit into this dichotomy? Their whole job is to poke around in Tableau in order to surface high-ROI strategies for the business to pursue. (Where, in choosing which proposals to surface to management, they're effectively making 90% of the strategic decisions.)

Or how about corporate buyers in trading and retail companies?

Or quantitative investment managers?

People who poke around in Tableau might not get a lot of respect in the hierarchy of DataFolk, but descriptive statistics and thoughtfully chosen visualizations can be immensely useful. Exploratory data analysis sometimes reveals patterns that are so obvious that to apply statistical inference is just vanity.

If understanding the data generating processes is the goal, I'd rather see some useful plots than wade through a technical description of some model whose assumptions were flagrantly violated.

What does a “normative” lens mean?

As opposed to "postive". It's the old is-ought dichotomy https://en.m.wikipedia.org/wiki/Is%E2%80%93ought_problem.

Positive claims are about what is true. Normative claims are about what should be true, or rather what decisions we should make. Put another way, positive claims deal only with facts while normative claims deal also with values.

GP is saying that it's the data-scientist's job to give the executive the facts and it's the executive's job to decide what to do about the facts.

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