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slt2021

I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs.

I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick some flashy metric like a percentage and run around with it.

Whenever my analysis contradicted - there was so much scrutiny in numbers, data quality, etc, and even after answering all questions and concerns - analysis would be tossed away as non-actionable/useless/etc.

if you want to succeed as a Data Scientist and be praised by management - you got to provide data analysis that supports managements ideas (however wrong or ineffective they might be).

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

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> if you want to succeed as a Data Scientist and be praised by management - you got to provide data analysis that supports managements ideas (however wrong or ineffective they might be).

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

It’s no different than the days when grey bearded wisemen would read the stars and weave a tale about the great glory that awaits the king if he proceeds with whatever he already wants to do.

The beards might be a bit shorter or nonexistent, but the story hasn’t changed.

And the alternative is to use the data as bones, throw it up in the air and let it tell you what to do?

Absolutely. If you don't like what K-Means is telling you, change a variable and re-run. (that's one great thing about business data: there's no shortage of variables! True, there's usually a shortage of independent variables, but fixing that is difficult and underfunded).

And you'd better hope the bones actually say something useful.

I was the infra lead on a data lake project and got take part all the way to breaking down the data and turning into PowerBI reports. The result was "sell more" and to clients who marketing already identified, years ago, as whales.

There were some interesting other insights, esp. w/r/t to niche products that sold around weird dates (Easter, Memorial Day, 4th July -- but not obvious gift days like Valentines or X-Mas), but it led to a lot of "you're doing it wrong!" recriminations and follow up projects.

If you [Croesus] go to war, a great empire will fall.

> 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.

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.

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.

> 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.

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.

> I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs.

So, a synonym for 'consultant?' :)

My experience with consultants normally ended up with them asking why they are there and what report should they present to upper management.

I've always used them as "independent 3rd parties" who were listened to.

This sounds a lot like how my kids will listen to a teacher/coach, but not their parents...

Which is similar to how a lot of parents won't listen to their kids but will listen to the coach, teacher, or priest.

Parents can be listening to their kids 99% of the time it will be transparent and uneventful. When there’s confrontation/divergence in opinion, by definition it didn’t work out through the usual channel, and of course a third party weighting in the balance will have visible effects.

My Mother-in-Law was called by a tech support scammer. Her bank was unwilling to accept their charges, and the scammer wanted her to call the bank to tell them to accept them anyway. My Brother-in-Law was telling her "no, this is a scam, do not do this", but she was unwilling to listen. Eventually, he told her to call me, thinking if she wouldn't listen to her son, maybe she'd listen to her son-in-law. Which she did.

"A prophet is not welcome in his hometown"

This has been going on for mellennia, unfortunately.

As a consultant with roots in backend dev, I fully understand the scrutiny that we receive because unfortunately, it is often very warranted... It feels a bit refreshing to read your comment and see someone articulate what I am trying to convey to my clients. I am a tool, and yes, this pun is intended.

Hello

I want to make the move from development into consultancy and would appreciate hearing how you did it.

I cannot DM you, but if you have the time to type a small paragraph, I'm all ears.

If you don't want to make that public, you can directly email me (<HN-username>@gmail.com)

Sure, it is actually not very complicated in my case. I did backend development for a short while during and after university and then moved into IT consulting fairly quickly.

It was a LinkedIn recruiter message which I usually ignore. However, my SO did not (she is in IT as well) and convinced me to join a hiring event. I ended up liking it a lot and went through the hiring process. Soon, I started out on the most junior level and joined my first project with 3 very senior colleagues after a few weeks.

The learning curve was very steep both on the technical level and also regarding the consulting aspect - at first there was nothing I could 'consult' on due to lack of experience. This changed with growing experience, with the guidance of senior colleagues and my private efforts to gain skills and expertise.

Let me know if this was helpful!

This almost reads like my trajectory so far, but I'm at the point where I can't really consult due to the lack of experience, but I did make a good impression so far. Can I ask you, into what efforts should I put my private time? More technical knowledge? Into very fine details, or brief insights into different areas? Any good resources?

Thanks a lot!

Here's my take on this not listening to the "expert":

A few years ago there was a problem with storm-water infiltration into my (elderly at the time) mother's property from her neighbor. I, being a dutiful son and a civil engineer, investigated it and came up with the probable cause, the likely effects of non-action and the most cost-effective solution. I presented it to my mother in the most layman-like terms that I could. She said she'd think about it – meaning she'd refer i.e. defer – to her daughters. In the meantime I had a very layman-like chat with my mother's carer and told her the situation in layman's terms. The carer listened and said that what I said I made total sense to her. Later on, one of my sisters accosted me and stated that it was completely obvious what the problem and the solution was – "even the carer could see it". Human foreheads don't have the real estate for where my eyebrows wanted to ascend.

My advice is to consider whether the message should be separated from the messenger somehow.

If Data Scientists are essentially in-house management consultants, I wonder which is cheaper?

This could be a reason why Data Scientist as a job title exploded in last years, every middle manager could afford one/two/few headcounts of data scientists to produce analysis that advances that middle manager's corporate agenda (more growth, empire building, expansion to certain de-novo areas, etc).

Recent tech layoffs is the other side of that growth, when cheap money is gone and company is forced to stick to core competencies and shutdown growth plans

All symptoms of the same problem..... you can hire McKinsey to confirm your priors, massage the data to confirm your priors, or anything in between.

A consultant with the data to back up their claims!

In the news business, if your story or opinion backs up the preconceived notions of the investigative reporter then you are a 'source' otherwise you are a 'conspiracy theorist'.

This would match what psychologists say about humans in general: we feel first, then we use our brain to justify that feeling. We’re not rational beings.

We totally are, it's just that rationality is a tool, not a guide. If you want to work out the truth, rationality will help you do that, but if instead you want to justify a decision you already made, well, it'll help you do that too.

Hypothesis don't come from rationality either, they result from well informed intuition. All of the formality of science is about tricking ourselves into discovering our intuition is wrong using a rational series of steps even when everything in our nature is to use that ability to reason to do the opposite.

A whole industry of emotional branding is thriving, systematically overloading our brains so it hurt to even think differently in the moment.

We are accepting a whole lot of assumptions every day.

Thats because psychologists dont understand or choose to ignore how chemistry influences our personalities and emotions. An extremely simple example from the same medical/health profession is the use of SSRI's to make people feel happy. The legal system recognises how chemicals influence our feelings because of the laws that exists on illegal drugs or drink driving.

The definition of rational is being informed enough to know what said chemicals will do in the short term and long term in order to make an informed decision, but then I'm reminded we dont get taught any of the above unless we specialise at a Uni, so most people cant make any sort of informed decision.

I'd like to know more about "[Your] life under [your] state employed 'parents' and at the hands of other employees of the state"

I think the answer is simpler: people care about their careers and their family first. Think, "If the data says something that gets in the way of my career well I don't care about the data."

Had the same problem when I was an economics researcher -- publication bias for what stakeholders want to hear (often the government) is rampant because that's where funding for the economics department mostly comes from.

It's only rational. The company certainly doesn't care about that individual first, as evidenced by e.g. its decision to lay them off when it doesn't think the individual is serving them, so why should the individual put the company first?

This is also known as The Iron Law of Institutions.

or is it that psychologists feel that we aren't rational and use reason to justify this?

it isn't clear to me how the grounds for realizaing the theory are reconciliable with its conclusion

"Man is not a rational animal; he is a rationalizing animal."

-- Robert A. Heinlein, Tunnel in the Sky, 1955

Yes. I worked in the data org of a moderately sized financial firms tech org. The tech org claimed to be hugely data driven. Was in the org mottos and all of that.

Nonetheless, the CTO went on a multi-year, 10s of millions of dollars, huge data tech stack & staffing reorg shake up... with really zero data points explaining the driver, or what we would measure to determine it was successful.

So it became a self referential decision that we are successful by doing what he decided, and we are doing it because he decided it.

Huh. I've not thought of it as laundering, but I think you've basically summarised consulting in healthcare. Pay to legitimize and push through a pre-existing idea (eg let's close down a few ERs) or a delusion (e.g. lean, we don't need a waiting room) and say it was recommended by consultants to stakeholders and the public.

Right, the more appropriate analogy is "parallel construction"...

all consulting is like that, Partners/MDs at consulting companies meet with Board/CEOs to get rough idea of what they want/need, and quickly negotiates a consulting engagement contract to create PowerPoint with all the evidence and analysis gathered that supports CEO's initial idea.

This is the only reason why a 60+ PowerPoint slide deck can cost several millions dollars

Also a big reason McKinsey and BCG exists - provide cover for business plans intended by management to protect them from shareholder lawsuits. My friend did a sojourn at McKinsey and 6 months of his life was producing PowerPoints and memos backing up an expansion to AIPAC region. Was already happening but he was providing all manner of business justification for board meetings and whatnot.

This phenomenon is true to varying degrees in academic medicine (maybe all of academia) as well - personally have seen excellent data and methods disregarded when they don't confirm existing agendas. The choice for the researcher can become one of burning out trying to do good work and getting nowhere, or acquiesce and only present data that is uncontroversial. Huge existential threat to knowledge advancement.

"Data launderer" would be a good job title...

That would imply the data is clean when they are finished. GIGO

The data is not laundered. Preconceived ideas and biases are laundered and given scientific sounding justifications.

Concept Confirmer, Bias Booster?

Context Provider.

Affirmation Artificier?

Assessment Assurance, for double the bang.

Cherry Picker

Good role for all Perky Cheekers, surely!

Most data is very dirty.

Decision laundering. Take in dirty decisions and produce clean ones.

Data Torturer? <https://en.wiktionary.org/wiki/if_you_torture_the_data_long_...>

This isn't just "Data Scientist" but scientist as well. The more a finding is in contradiction, either with existing scientific consensus or even with just popular culture, the more the science is criticized. I've seen unequal criticism based on how much people wanted the results to be true/false and even after responding to the criticism I've seen people just ignore science they don't like.

The skepticism isn't a problem, the unequal application of it, the potential to harm careers, and the chilling effect as people wisen to how best meet their own personal goals is.

> I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs.

The SNAFU principle: communication is only possible between equals. When an hierarchical divide exists the subordinate will tell the superior what he wants to hear.

>The SNAFU principle: communication is only possible between equals. When an hierarchical divide exists the subordinate will tell the superior what he wants to hear.

Sadly true. As humorously depicted here[0]:

   In the beginning was the DEMO Project. And the Project was
   without form. And darkness was upon the staff members 
   thereof. So they spake unto their Division Head, saying, "It 
   is a crock of shit, and it stinks."

   And the Division Head spake unto his Department Head, 
   saying, "It is a crock of excrement and none may abide the 
   odor thereof." Now, the Department Head spake unto his 
   Directorate Head, saying, "It is a container of excrement, 
   and is very strong, such that none may abide before it." And
   it came to pass that the Directorate Head spake unto
   the Assistant Technical Director, saying, "It is a vessel of
   fertilizer and none may abide by its strength."

   And the assistant Technical Director spake thus unto the
   Technical Director, saying, "It containeth that which aids 
   growth and it is very strong." And, Lo, the Technical 
   Director spake then unto the Captain, saying, "The powerful 
   new Project will help promote the growth of the 
   laboratories."

   And the Captain looked down upon the Project, and He saw 
   that it was Good!

[0] https://www.anvari.org/fortune/Best_Fortunes/820_in-the-begi...

Same goes for economists and the politicians who sponsor them, just as it did for the astrologers and their patron kings.

Economics is the go-to for conservative, status-quo maintaining arguments because there's a wealth of statistics and information available for "how things have always been", and precious little-to-none for "how things could be if..."

It's easier to poke holes in predictions of the future than in interpretations of the past, especially when the people making those decisions have likely reached their decision-making status through "how things have always been".

I think this depends a lot on the org. In a place I used to work we collected and analysed a lot of data which convinced management to significantly change the spec of the product and spend a lot more time and effort on testing, because the product was being used in unexpected ways.

I would say it was a very engineering driven org however, so if you could present compelling data it could go a long way.

Authoritarian types consider any information derived by science which is contrary to their position as invalid or irrelevant because facts challenge their authority and ability to exercise control.

yes. I used to think the Church had a honest disagreement with Galileo about heliocentricity. When I grew up I realized the Church never cared about orbits at all, what they care about is maintenance of status quo.

And then when I got old, I realized, there is even a reason that some people want status quo... because they have usually been around long enough to see society fall apart into anarchy and mass murder, so in their mind, they are doing the right thing.

The church was not upset about heliocentrism. They were upset that Galileo was attempting to reinterpret the words of Bible in order to bolster his astronomical authority.

"[Therefore,] when God willed that at Joshua’s command the whole system of the world should rest and should remain for many hours in the same state, it sufficed to make the sun stand still. In this manner, by the stopping of the sun, the day could be lengthened on earth—which agrees exquisitely with the literal sense of the sacred text.”

- Galileo Galilei

"The Church" wasn't then and isn't now a monolith of opinion.

A modern characterisation of "The Galileo Affair" would be that he was SWAT'ed by someone he was really really mean online to.

    Thus the whole "Galileo affair" starts as a conflict initiated by a secular Aristotelian philosopher, who, unable to silence Galileo by philosophical arguments, uses religion to achieve his aim.  [1]

and

    While delle Colombe was almost alone in arguing publicly against Galileo, there was a group of scholars and churchmen who supported his Aristotelian views. After Galileo referred disparagingly to delle Colombe as 'pippione' ('pigeon'), his close friend the painter Lodovico Cigoli coined the nickname 'Lega del Pippione' ('The Pigeon League') for delle Colombe's group.[2]

Galileo literally refered to delle Colombe (and friends) as Simplicio (simple minded) and worse in his highly popular Dialogue Concerning the Two Chief World Systems [3] and within a year or so the Pigeon League got their revenge, using their influence to have religuous charges bought against Galileo.

The affair was complex since very early on Pope Urban VIII had been a patron to Galileo and had given him permission to publish on the Copernican theory .. this was very much a case of personal vendettas and internal politics rather than a straight up case of "The Church Versus Galileo".

[1] https://en.wikipedia.org/wiki/Galileo_affair#cite_note-Spell...

[2] https://en.wikipedia.org/wiki/Lodovico_delle_Colombe

[3] https://en.wikipedia.org/wiki/Dialogue_Concerning_the_Two_Ch...

>>I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick some flashy metric like a percentage and run around with it.

>> Whenever my analysis contradicted - there was so much scrutiny in numbers, data quality, etc, and even after answering all questions and concerns - analysis would be tossed away as non-actionable/useless/etc.

It's a good sign at the company that I run, anytime our analysts/data scientists come up with metrics that say we're killing it, or that our ideas should bear a ton of fruit, the kneejerk reaction is to be extremely skeptical of the results. Usually they're still right.

When the data scientists say we're fucking something up, we tend to pay a lot more attention.

Only the paranoid survive, after all.

An interesting essay that echoes these same sentiments:

https://ryxcommar.com/2022/11/27/goodbye-data-science/

Oh the experiment didn't go as expected? Rerun 5 more times with minor tweaks. It definitely not p-hacking ;).

I’ve been there, we wanted to release a feature, it kept coming back with issues that made it perform much worse than control, after 5 or so iterations with bug fixes it came back positive.

It took a lot of analysis and time to clarify to higher ups that we weren’t just P-hacking , but at least they were concerned about that.

sadly same with academia and funding sources

I agree with you. I call Data Scientists "soothsayers for the Pharoah".

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

https://www.youtube.com/watch?v=kAichhoZrKs

I wonder what a data scientist could really find out about executive (over?) compensation. employee compensation. working from home. office cubicle size and layout. tool expenditure for employees vs productivity.

How would you measure productivity at scale?

Well, obviously you ask the CEO.

> I used to joke that Data Scientists exist not to uncover insights or provide analysis, but merely to provide factoids that confirm senior management's prior beliefs.

Also someone to blame if it doesn't work out

As a data scientist at a large corporate I find this is often the push… but I resist every time and tell people what they don’t want to hear. Maybe I’m playing this whole corporate ladder thing wrong :/

"Data Scientists exist not to uncover insights ..."

That was a plot point in Dirk Gentley's Holistic Detective Agency (1989), though the observation much pre-dates this.

how is this really different from any other aspect of life? Very few people really like to be told counter information, and it is always easier when providing data that aligns with the current group think. Doesn't matter if it is business, politics, or really anything. Being the outlier trying to change the direction of things is a struggle.

110% my experience working in data science.

I found it incredibly stressful to discover and provide analyses (even experimental results) that wasn't expected, or contradicted prior beliefs. The findings were always very harshly scrutinized, and typically lead to tons of pointless extra work to 'understand what is going on'.

That's like a portrait artist that finds success by painting people more beautiful than they really are vs a starving one that paints them true to life due to sense of artistic integrity. Reminds me how Garth Brooks started doing metal after becoming a country music star.

Not just data scientists.

My friend runs a successful market research agency and she says she gets called in when management have decided they need to make a change but need evidence to sell it to the shareholders and staff.

So Big Data isn’t dead. It’s just found it’s place in society….

Sounds like a good way to weed out middle management ;)

Indeed, confirmation bias happens to almost everyone.

Bingo. Welcome to the world of office politics!

Reminds me of the book "Bullshit Jobs"

True - aka “are right a lot”

Government should do it, that way we can be sure it is honest and correct.

I mean, that makes sense does it not? If you're confirming something people already had a hunch about, why would they challenge it? And if it does go against their belief, they are going to want to make sure the data is correct before they change the course of the ship.

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