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Potemkin Data Science (2020)

• melling 5y ago
  “Data science, often only a step or two removed from dashboard generation” I’ve been trying to learn data science and machine learning. However, data science as a field seems to have already fallen out of favor.
  • jamil7 5y ago
    As far as I can tell from the outside it looks like it's not necessarily falling out of favor but fragmenting into different or more specialized roles. Maybe someone who knows more can correct me.
  • deleted 5y ago
    [deleted]
  • otabdeveloper4 5y ago
    Except that dashboard generation is exactly what "data scientists" do. (In the same way that "DevOps" is just a fancy name for a "sysadmin with some light scripting skills".)
    • kqr 5y ago
      DevOps is a set of practises that practically involve frequent deployments, fast cycle times, low release overhead, short feedback paths, quick time-to-restore, and so on. It's an extension of lean manufacturing to software development, where the focus is on continuously pushing tiny packets of functionality to customers (instead of occasionally pushing huge bundles of features), to react quicker to changing demand and mistaken assumptions. It is in no way "a sysadmin with scripting skills" -- the name for which, is, by the way, just sysadmin. Sysadmins know how to write Perl and shell and Python and have done so since forever.
      • otabdeveloper4 5y ago
        I know the sales pitch, spare me. > ...the name for which, is, by the way, just sysadmin No, because some time in the past "IT support" (you know, the guys setting up Windows and Wi-Fi routers in your office) got upgraded in name to "sysadmins", and so the old "sysadmins" who knew Unix and how to code some Perl needed a new, untarnished name. Hence, "DevOps".
    • beforeolives 5y ago
      The job title is too broad to generalise in this way. Many data scientists never touch a dashboard at all, for many others it's a very small part of their job.
      • otabdeveloper4 5y ago
        Regardless of how significant the data science insights are, in the end the way you communicate them is through some sort of dashboard.
        • beforeolives 5y ago
          No, not always. And "insights" might not even be a part of your job as a data scientist. (I wrote a longer comment about this in the thread, I don't want to repeat it here)
        • bart_spoon 5y ago
          What if you are building a data product? I'm not communicating anything on a lot of the things I'm building, because they are simply components of a larger software program. I haven't touched a dashboard in years.
    • auxym 5y ago
      I once was in a "DevOps" role with the responsibility of getting devs' "works on my machine" code to build in CI. Fun times trying to get code you didn't write to compile without knowing any context. Anyways, that sort of turned me off trying to move into programming and so I went back to mechanical engineering after less than a year. Maybe I should have stuck it out longer.
      • beckingz 5y ago
        You were on the DevOps team! They did what ops did!
    • FlyingSaucer 5y ago
      I'm a DS by title. I don't do any dashboards. Some people on the team collaborate on them occasionally but its generally deciding which metrics are tracked along with creating/maintaining them while the actual dashboard design and creation is done by the frontend team. The thing is that Data department have essentially swallowed the former analytics departments, and many people who have done business intelligence/business analytics now seem to fall into the data science umbrella. This is part of the reason the term now refers to different things depending on who you talk to or which team they are a part of. Look at job postings, its extremely difficult to understand what the actual job entails these days.
  • beforeolives 5y ago
    Data science been criticised for over 10 years now, sometimes rightfully and sometimes not. As far as the article - imagine that someone pointed out a bunch crappy software practices and poor outcomes, they put the term "software development" in the title and never told you that the criticisms only apply to a subset of all software jobs and teams. You would then think that software development has fallen out of favour too.
  • sgt101 5y ago
    Data science is the transformation of a set of data resources into a representation of a domain of activity that has integrity and utility. The curation, cleaning, integration & analysis of data to create pictures of what is going on won't go out of favour until there are no more pictures of this sort to be made!
• wonnage 5y ago
  Tangentially related to the article, but it mentions the Rebekah Jones story, which has been thoroughly dissected here: https://www.nationalreview.com/2021/05/rebekah-jones-the-covid-whistleblower-who-wasnt/ https://www.nationalreview.com/2021/05/rebekah-jones-the-cov...
  • ramraj07 5y ago
    Jesus I feel violated for even remotely believing in that person's story before.
  • isoprophlex 5y ago
    Any story that drenches it's premise in hyperbole like this can be immediately discounted in my view, as there's an almost zero chance that I'm reading a dispassionate objective overview of established facts. The article reads like random gutter journalism. > But it’s not true. Indeed, it’s nonsense from start to finish. Jones isn’t a martyr; she’s a myth-peddler. She isn’t a scientist; she’s a fabulist. She’s not a whistleblower; she’s a good old-fashioned confidence trickster.
    • pinguin7 5y ago
      While I agree on your basic premise, this might be one of those times where a horrible article makes valid points and is correct in its assessment of the situation. I think this is one of the greatest flaws of current journalism: By increasing the "feel good value" for its intended audience and confirming their respective biases, an author can completely disenfranchise readers coming from a different point of view. You and me were both repulsed by the sweeping statements and lack of nuance and, since time is precious and the article looked like yet another propaganda tool, you rejected its value. Rightly so. Yet in it where description that were correct and pointed to a deeper issue of whom we believe and why. So by writing for just a section of a possible audience any discussion is impossible. I won't read the National Review (just this article) and thus their content will be irrelevant to any good discussion I'll have in the future.
    • bart_spoon 5y ago
      You are not wrong that its over the top to use that kind of language, but if you don't actually read the article to see if the author indeed making an argument without any established facts, you aren't making any more of an attempt at objectivity as a reader than the author is.
• beyondcompute 5y ago
  morally ambiguous act of consuming What? I mean, you can argue that it’s “morally ambiguous” in the same sense that almost everything is ambiguous, for example being happy and enjoying an evening with your friends while some people in the world are suffering. But otherwise I would argue that buying a smartphone in a capitalist society is a much less “ambiguous” act than eating meat in a communist society, for example. But in general if one is familiar with the basics of economics, one should understand that for the growth of standard of living (that includes health of your parents, for instance, not just “mindless pleasures”) there needs to be the growth of economy. And for that, consumption is essential. So yeah, consumption of in-good-faith sustainable and environmentally-friendly goods, consumption of local stuff and especially consumption of services is not that ambiguous at all. You contribute to economies going and to communities’ well-being.
  • ramraj07 5y ago
    "if one is familiar with the basics of economics" seems like a fairly large assumption to me here, what economics is a science now? To me it just looks like a working theory to explain the world and some of its fundamental assumptions (that the world is not zero sum) are dubious at best. Focusing on this problem we are discussing, how much can you control the effects of whar you consume other than by controlling how much? You might buy vegan patties in whole foods, but other than the fraction of your money that went to the direct purchase of energy or land for agriculture, the money just gets split between roughly the remainder of the economically contributing population. Thus as long as you consume, you only have partial power over it's good-faith nature, the remainder is controlled by how much the entire population (and businesses) act in good faith in general. If you really want to keep your hands clean then I see no way other than not to spend a single dime more than you absolutely need to for your survival. Everything else is morally ambiguous at best. And this statement that we absolutely need the economy to grow for continued prosperity is preposterous. It's clearly A method of continuing prosperity, and clearly a method that at least works. But it's not proven to be the only method and we better start inventing new ones that don't put all their money on what's basically a glorified Ponzi scheme that everyone's forced to play.
    • 5y ago
• natureboy21 5y ago
  Overall pretty great points. I have been inside a Fortune 100 company that had a totally backward Data Science team that was running to crazy levels of complexity with no known goal in sight. One thing that is worth pointing out is that the deficiency in identifying goals before running after impressive technological complexity as a status symbol is not restricted to just Data Science. I see it all the time with technologies like "Bluetooth". Let's put Bluetooth in everything. We don't know why but it seems impressive so let's just do it.
  • deleted 5y ago
    [deleted]
• beforeolives 5y ago
  My perspective on this as a data scientist: Data science as a field (and as a job title) is broad and generally splits into - research - software engineering and data work - product/business decision making With many data science jobs being some blend of the three instead of staying purely in one category. And you can split each category even further. The point is that there is so much breadth that people can have the same title and completely different job descriptions. You need a disclaimer about this pretty much every time you discuss anything data-science related. The article focuses exclusively on the third category of data science (as does most of the criticism of data science). So to understand the article as criticism towards the field, you need to take two things into account - what proportion of all data science jobs are predominantly in the third category or involve any influence on business/product decisions (anectodally, not that many) - how many of those jobs and teams actually exhibit the dysfunctions listed in the article I don't have any numbers to answer these questions. Personally, I'm interested in the engineering type of data science jobs (ML engineering, scientific computing and other adjacent roles). I wouldn't want to be in a position where I have to advise on decisions, or come up with justifications for someone's business/product choices, or continually have to come up with excuses for my existence in the company. In my current job search I have a preference for positions titled "software engineer" just to avoid all of this ambiguity. It's also very tempting to switch to being a backend developer or something similar and drop the whole data science thing altogether.
  • blr246 5y ago
    Hi beforeolives— I like your breakdown, and I've observed similar things in my experience as an engineering focused data person! I've had many discussions with my colleagues about how to manage effectively these different blends of roles and skills. I'm looking for someone for an engineering type of data role right now. Is there a way to get in touch with you about it? Our product helps companies listen to their customers by unifying natural language feedback across various channels, applying signals using various natural language modeling techniques, then aggregating them to help teams deliver better outcomes using more relevant information. Hope to hear from you (brandon at frame.ai) :) edit: forgot to share agreement for your breakdown
• nojito 5y ago
  I read this piece and kept reading hoping that they would mention the real reason for this and it didn’t happen. The vast majority of data scientists are terrible at their jobs. They lack fundamental skills on how to extract business value from data and share that value with the organization. The fact of the matter is the majority of organizations don’t need FANG style data science with esoteric models and pipelines. It’s very similar to the current software developer trend of over/pre optimizing or resume driven development that is ravaging the industry today.
  • padthai 5y ago
    Tech is fashion driven. Ten years ago it was Hadoop/SVM/MongoDB, five years ago Spark/CNN/Neo4j. If today you appear at the door with Nomad/MCMC/Riak, you will be looked down. We are expecting something like K8s/Prophet/Snowflake. Somewhere in the same company might be someone making the same as you with a couple of SQL/Qlik courses in their well groomed head and a handful of ironed shirts in their closet. But we will look down at them because dressing well is for sheep and SQL is like, 100 years old.
• sublimefire 5y ago
  > I have tried to make my red flags and categories vague enough that they can apply to almost any organization <...> Trying to shoehorn something complicated into a set of broad statements does not help to address the underlying issues as they are not specific enough hence not actionable. What is more "Data science" seems to be an ambiguous definition. Besides it is intrinsically an afterthought as data does not exist until the company collects it. I would also argue it is "a thing" only in large orgs or globally successful software; small companies do not posses datasets worthy of exploration. Specifics would help as otherwise it feels like we're talking about technical debt.
  • d3nj4l 5y ago
    Agreed - this article felt more like it was trying to appear clever and insightful without actually saying anything of insight. Okay, so data science in many organisations is dysfunctional: many people have already made that claim without resorting to waxing lyrical with historical analogies. What next?
  • padthai 5y ago
    > small companies do not posses datasets worthy of exploration Or with a cash flow for a permanent position. Tech workers are used to be in companies with healthy gross margins and revenue where they make sense (theoretically at least). But even it tech I would not be surprised if there are teams of DS/SWE people in Uber or Doordash helping their companies to lose money faster.
• jeegsy 5y ago
  > Former Florida Department of Health data scientist Rebekah Jones is one such example: she was fired for apparently refusing to doctor the COVID data being presented to the public by the state government An unfortunate choice of example in hindsight
  • stdbrouw 5y ago
    I guess you're referring to recent reporting that she might have made up some of her earlier claims, e.g. that she was being asked to "delete" deaths from the system: https://www.nationalreview.com/2021/05/rebekah-jones-the-covid-whistleblower-who-wasnt/ https://www.nationalreview.com/2021/05/rebekah-jones-the-cov.... Does look like she might not be the hero some people think she is, but maybe let's wait a bit on other reporting before vilifying her.
    • bart_spoon 5y ago
      What other reporting should we wait for? Looks pretty damning to me.
      • stdbrouw 5y ago
        Any other news source than the one that rejects climate change and originated the Obama birther movement? :-)
        • jeegsy 5y ago
          Fortunately for me, I dont practice the "falsus in uno, falsus in omnibus" method of news analysis and so therfore, the previous stories from this outlet are irrelevant. What makes the follow-up story important is that it suddenly occurred to me that the original reports about Jones depended primarily on her word as I recall. That recollection is enough for me to dismiss the story as uncorroborated. MY standard, not the source link posted here.
          • stdbrouw 5y ago
• tharne 5y ago
  Meh, in the real world, the boundaries aren't so clear. In most organizations, data science or analytics will likely play all these Potemkin-ish roles to varying degrees, while also adding real value to the business. Sure, you may do some "decision laundering" for an executive, but that executive may then go on to help champion data science and develop a data-driven culture because by doing him or her a solid every now and again, you build a relationship. Favor trading may seem dirty or impure to a lot people, but it's also how a lot of great work gets done. And sure, your org will likely use data science as marketing with a healthy does of McKinsey-inspired corporate BS thrown in. This does not and generally will not preclude the organization from also using data science productively and appropriately. Anyone who thinks you can rise above petty politics to do some sort of "pure" data science is going to find themselves, frustrated, ineffective and burnt out.
  • konschubert 5y ago
    > but that executive may then go on to help champion data science and develop a data-driven culture That's not an end in itself, though, is it?
    • SQueeeeeL 5y ago
      Yeah, financial consulting firms basically played this game for a long time; but they know the game, always give a report that satisfies the person who brought you in. Luckily most "data scientists" also learn pretty quick who butters their bread and 'correct' their results accordingly. Those who aren't 'team players' tend to not get brought into meetings with the higher ups, and eventually move onto real jobs.
      • slt2021 5y ago
        this doesn't sound like science, or data science. sounds more like consluting services
        • SQueeeeeL 5y ago
          Did you read the article? This was essentially it's point about most corporate DS operations. Science is boring and takes a long time, but building a regression model on an excel sheet is easy and doesn't burn too many brain cells
• zwaps 5y ago
  One of the points Andrew Ng repeatedly makes, especially recently, is that we do not focus a lot on the data. ML is a field of models, not data. That's fine, it's an academic discipline. However, a data scientist should probably focus on something else. Nevertheless, we currently want to be ML people, more or less. It's sexy to download __some__ dataset and develop a new model architecture. It's not really sexy to collect good data - not just any data, but good data. Now, coming from academia, I think this is just a normal thing. It happens in every discipline in some way. In academic fields that are driven by observational data, data collection is usually a difficult and tedious task. Developing theories, methods and, of course, new results is much more interesting. And rewarding! Look at economics and Piketty's work: His main contribution was to collect data on wealth. Nobody really cares about his models much. However, for some reason, that data simply did not exist before. And it's now valuable for hundreds of different projects. As a data scientist, one needs to think about data, first and foremost. One needs to spend most time on collection, on measurement errors, on selection effects, on identification, censoring and all that stuff that nobody likes to think about. Fitting the next best function approximator to the data is the most fun, but probably not the most valuable thing a data scientist can do. From that standpoint, I think data scientist is a proper job that is probably needed in every company in the future. However, no coursera course on the latest ML package will make you a valuable data scientists. I think data scientists who are expert statisticians, survey and experiment designers, causal inference gurus and combine that with proper CS techniques will be valuable.
  • mateo411 5y ago
    I agree with this. I think you really need to be a good data engineer to be a good data scientist. Now, these terms are different roles at different organizations. I think you need to have a well developed software engineering skillset. You need to have some experience with database systems and query optimization. Of course you need a strong background in statistics and mathematics. Often these skillsets are split across teams and departments, which tends to slow the process down. It takes a long time to develop these skills, but if you have them, you are very valuable. Many DS and sometimes companies make a mistake of undervaluing the software engineering skillset. However, if your data comes from the real world, it will be messy, it will need to be organized. If you don't know how to do that, then you aren't going to build your models. If you are bad at querying your data, then you are going to build your models much more slowly.
• tfehring 5y ago
  My impression is that the article mostly describes data analysts, not data scientists, though of course job titles are fuzzy and variable. IME data scientists pretty much universally at least attempt to work on things that directly and demonstrably increase revenue or reduce expenses. That experience is colored by the fact that I work in a field (insurance) where statistical analysis is core to the product, but my impression is that it's true more generally - if you're a data scientist in e.g. conversion optimization at a big company you probably have no trouble justifying your salary, and even the relatively unsuccessful data science teams I'm familiar with tend to work on things like staffing level optimization that are at least plausibly a value-add for the business. The more interesting line of argument to support data science being a bullshit job is that all of these lucrative use cases are just a tragedy of the commons. Insurers in aggregate did just fine when pricing was based on actuaries using simple approaches to "square the triangle" and then adjust rates based on rules of thumb, the only reason they can't always do that today is that everyone else has data scientists. And if you don't have a good recommender system, your customer will just buy somewhere else (or "worse", avoid an unneeded impulse buy). Of course, it's not strictly zero-sum. Insurance pricing is "fairer" now, which is probably a good thing, and data science is enabling all sorts of obviously positive-sum products like wearable health monitoring and car safety features (to say nothing of self-driving eventually). Other use cases like "engagement" optimization are probably negative-sum. But in general my point is that creating millions of dollars' worth of business value per year, as many (most?) data scientists do, doesn't preclude you (or me) from having a bullshit job.
• version_five 5y ago
  I've worked in what you could call "data science" consulting for about 6 years. I also know data scientists that work in-house in data centric businesses, and have some acquaintance with in-house data science groups in bigger traditional businesses. In all honesty, almost all firms that are hiring consultants to do data science projects (based on the sample I've been exposed to) fall firmly into the situations the article describes. It's all about appearance and justifying existing decisions or plans, and even the suggestion of changing something swamped out of existence by the bureaucratic immune system of these orgs. On the other hand, I see (especially in advertising but also e.g. fintech and others) cases where an internal data science team really does drive the agenda, and thing like AB testing are used for real in order to make changes that result in more money. Lastly, the in-house team at a legacy company. Although I have less experience here, the ones I've seen closely mirror "innovation" or transformation groups that are supposed to come up with new initiatives or changes but inevitably are ignored because they have no links to operations. So over all, the article resonated. As a data scientist , there is meaningful work at companies where data is closely linked to operations, mostly in "new" businesses. As a consultant, I find it interesting to understand legacy businesses and their data, and learn about what potential upside there is, even if I know they won't listen to me.
• supernova87a 5y ago
  One of the problems with data science is how black box it is compared to other corporate/software development functions: For many roles and positions in a company, high and low, you can tell whether the person is doing a good (or at least competent) job. A sales rep can show leads he/she has followed up or converted, and $ made. A coder can point to functionality created and lines written. A CFO can point to deals done, documents prepared, etc.etc. With data science, sometimes you just have absolutely no idea whether the person is good or not. "This problem can't be solved". You just don't know whether it's true or not. "These are amazing insights". Do they have any practical value that can turn into $? Data science is from some perspectives the most black box part of a modern organization. And so much hype is put into it, so many people paid sky-high salaries in the belief that it is producing something. Is it usually? Not sure.
• SilurianWenlock 5y ago
  Why cant software engineers just do the data science role?