The joyless world of data-driven startups

&gt; Sometimes it works. Sometimes it’s critical. But sometimes it fails, or results in unintended consequences that we may not notice for years.<p>&gt; Data-driven journalism gave us Buzzfeed<p>&gt; Data-driven music gave us X-Factor and Pop Idol<p>&gt; Data-driven movies gave us 25 Hollywood sequels planned for this year<p>&gt; Data-driven education gave us Key Performance Indicators and Teaching to the test<p>These first three examples are awful examples. A ton of people love all of those things. The only failure of data-drivenness here is the failure to generate content that <i>the author</i> wants. Now, the author can attempt to make some argument about how websites, TV shows, and movies have a moral obligation to strive for whatever objectives the author prefers, but that&#x27;s a separate issue to settle.<p>The fourth example is a little different, because we&#x27;re talking about mandatory education programs for children, rather than products that people choose to pay for or consume. Also, I don&#x27;t think that data-drivenness itself is a significant contributor to those problems in education.

I think the article goes a bit too far against data. Hits like Bohemian Rhapsody are by their nature freak events and not easily reproducible. Nobody is suggesting (I hope) that you can achieve that kind of success without a healthy dose of luck.<p>However I agree that for early stage startups data driven decision making can be difficult. My experience is its expensive and you often have very little data.<p>The other issue is this kinda uncanny valley of false rigor. On the one extreme you have very informal analysis. For example, we tweaked our blog post template and increased newsletter signup rates but I can&#x27;t tell you exact %s because at this stage we don&#x27;t track it. We seem to be getting a lot more signups, but perhaps its illusory. That&#x27;s ok. At our level of traffic it really isn&#x27;t important. At the other extreme you try to model non-stationary processes and all that and have rigorous control over sources of error. In the middle is where I see many companies with, say, A&#x2F;B testing, believing they have a high level of statistical rigor but not actually achieving that rigor in practice due to many uncontrolled sources of error. This middle spot, where you have too much faith in faulty reasoning, is where I <i>believe</i> bad data driven decision making resides.<p>Oh, and on turd polishing: <a href="http://www.dorodango.com/create.html" rel="nofollow">http:&#x2F;&#x2F;www.dorodango.com&#x2F;create.html</a>

The so-called data-driven science have not understand the notion of science. In a minimal sense, science is to produce knowledge. There are two things to it: hypothesis generation; testing the hypothesis. As the history and philosophy of sciences have shown, there is no algorithmic way of generating hypotheses. Or if you generate hypotheses algorithmically, you are still left to figure out whether these hypotheses are ad hoc or not. After all, the history of sciences have given powerful heuristics to reduce the solution space to generate hypotheses to solve or explain problems or facts. Here, whether one picks &#x27;solve&#x27; or &#x27;explain&#x27; depends on which philosophy of science one picks up.<p>Whenever I see statistics and data-sciences, I see tons of adhoc bullshit masquerading as sciences&#x2F;knowledge. It is always easy to come up with a hypothesis to explain a set of chosen facts; in order for that hypothesis to be non ad hoc, it has to predict surprising facts.<p>As the fad continues, we may hear like robots replacing scientists to produce knowledge about various phenomena. For a best critique of AI, check the book by UCBerkeley philosopher Hubert Dreyfus: what computers can&#x27;t do, a critique of artificial reason.

“Epistemology”, he whispered.<p>Using data is good, but “based on” offers a lot of wiggle room. A 10% increase in CTR is nothing to sneeze at, but it does not answer the question of the best use of your engineers’ (or designers’, or marketers’) time. Should they have instead been working on the thing that has a 50% chance of a 20% improvement? How do we account for all the data we didn’t bring to bear?<p>The data is small, the interpretation is big.<p>There is also the problem that philosophers call “regress”, which is that every rational decision has to trace back to a premise that one assumes in. Should we be in the business we are in, compared to all the other potential uses of talent? We can’t know that empirically, at root.

Premature optimization: still the root of all evil.<p>One of the better takeaways from the article was the notion that being data-driven means you&#x27;re aiming for average, and you might not even hit it. Aim for the moon, you might only achieve orbit instead.<p>I watched a CEO make arbitrary layoff decisions based on what the numbers said should be the size of a development organization and the the ratio of developers to QA. The actual software being built was irrelevant to his figures. He used numbers to justify grinding the dev organization into the ground.

I&#x27;m not afraid of computers acting like people (AI). I&#x27;m very worried about people acting like computers.<p>Every circuit, every program is based on a principle: comparators (analogue) = NAND (digital) = if statements (software). Machines choose their answer by taking a huge amount of information, and sorting it. By design, this leads to some monstrous conclusions. For example, eugenics might be logically efficient, but it is morally abhorrent.<p>Taking risks, making mistakes: these are not flaws, they are the very essence of being human.<p>Test yourself! I guess that everyone on here is very rational (as I am). I only discovered this problem in my character after a conversation with an artist, a good friend from high school. She makes all her decisions based on the heart, rather than the mind. Try to do something totally random! When things make no logical sense, the emotions wake up again. You&#x27;ll &quot;feel&quot; again. It doesn&#x27;t matter if that&#x27;s a good or bad feeling - acting like a machine makes you feel nothing at all. A machine can defend every action it takes, because it&#x27;s never wrong. But machines can&#x27;t apologise.<p>There will be data-driven businesses. They&#x27;re not actually run by humans (whatever the management says), they&#x27;re run by machines. Those companies could ultimately be fully automated away. It&#x27;s far better, as a human, to be creative (even if the most creative thing you can do, like me, is teaching machines how to talk to other machines).

The most important skill, in order to be data-driven, is to ask the right questions. If you&#x27;re looking to get to product market fit, the questions you should be asking are very different from the ones you should be asking if you&#x27;re looking to grow a &quot;good&quot; product. In both cases, data can help you reach your goal, but only if you ask the right questions.<p>If an early-stage startup tries growth hacking before it reaches product market fit, it will likely end in disaster.

Lots of ignorance in the article and some of the comments regarding data-driven decision making. Does the author realize how much of the civilization around us is built and guided by data-driven decisions? Also, people&#x2F;companies not being able to effectively use their measurements to their advantage is not really evidence against the idea itself. When your model doesn&#x27;t work, it&#x27;s not modeling that&#x27;s broken, it&#x27;s just your model.<p>Gut reactions can take us only so far: they break down as we move away from single human-scale familiar problems (ones that the brain has some built-in, evolved capacity of handling, such as reading other people&#x27;s facial expressions).

&gt;&gt; Everyone tells early stage startups to use data for big strategic decisions.<p>This is not even remotely true in my experience working at and advising startups. Sure, data is important, but more so for tactical matters like ad performance and A&#x2F;B testing. Big strategic decisions typically employ far less data relatively, pretty much definitionally since no data exists for &quot;big strategic decisions&quot;.

There is a looming disaster of a similar nature coming in healthcare too.<p>Data shouldn&#x27;t be used to set goals it should be used to achieve them. It may also tell you when it is not currently possible to achieve your goal. That doesn&#x27;t mean you should throw up your hands and set goals that the data seems to indicate are achievable because (among other things) that is tantamount to believing we can actually predict the future.

There is one, and only one reason to be &#x27;data driven&#x27;. Or to test one&#x27;s hypotheses, for that matter. And that is to make sure you aren&#x27;t fooling yourself; that you haven&#x27;t fallen prey to the myriad cognitive biases; to prevent your preconceived notions from clouding your judgement of reality, aka what is actually going on.<p>While data always provides more information, the less strong your prior beliefs, the less informative your experiment will be -- If you believe something and it turns out to be majorly false, you get a nice shift in expectations. If you believe in something and it turns out to be very true, you gain lots of information in terms of quantifying the effect you are looking into.<p>If you are Google, looking to eck out every last 1&#x2F;1000th of a penny on ads, yeah, maybe a&#x2F;b testing the shade of blue of a button can be justified.<p>The more other companies are &quot;Data Driven&quot; [like the somewhat unfortunate examples the author chose], as opposed to &quot;Hypothesis Driven&quot;, the more there is room for somebody else to fry bigger fish.<p>In other words, it&#x27;s not the &quot;data&#x27;s&quot; fault, it is ours.

Oh thank goodness it isn&#x27;t just me.

This article omits one important aspect: the data isn&#x27;t used only to create. It&#x27;s also a guide what to delete&#x2F;abandon. Sure, it might be influenced by chaotic fluctuations but anyway can help make a decision. Sometimes making any decision is better than wandering around with ambivalent gut feelings.

Personally I enjoyed this article.<p>I&#x27;m working within several different businesses right now, and the consistent theme I&#x27;m trying to relate to the folks I work with is that data-driven techniques can take you right up to the edge of what is known to be possible. It&#x27;s the people who work with the ambiguity there and take leaps into the unknown that ultimately change things. It&#x27;s fine to want to be part of the pack, but for the really ambitious folks being at the front-edge of the pack is still being part of the pack. Learning to make the move out in front is the hard part.

&gt; Sometimes it works. Sometimes it’s critical. But sometimes it fails, or results in unintended consequences that we may not notice for years.<p>A bit off-topic, but it explains why we got Windows 8x and the upcoming Windows 10 - data driven metrics.<p>When will Microsoft learn that developers and advanced users turned off the &quot;phone-home&quot; metrics gathering functions in Windows XP, Vista, 7 and Office?<p>People want Windows 10 to be Windows 7.5. It would be nice to get some lost Windows XP functionality back and shell bugs fixed that are in since Vista.

Being data-driven is detrimental when it replaces common-sense (talking to users, collecting feedback, improving based on feedback).<p>Before the internet (and being able to track every single action), successful companies were built. It can be done. Using data to drive decisions has some value, but it&#x27;s not the end-all solution, it&#x27;s merely a piece of the puzzle.

Anyone read the Crunchbase profile of the company the author is the CTO for?<p>&quot;Bipsync provides a research automation platform to maximize the productivity of professional investors. Founded in Silicon Valley in 2012 by experienced investors and software developers at Stanford University, the company uses modern technologies and user-centered design to speed up data capture, automate research maintenance and identify insights that drive better decisions for investors and funds.&quot; [1]<p>I mean, maybe I shouldn&#x27;t be looking for patterns, because, y&#x27;know, data. But it seems oddly conflicting to be pitching a product that encourages the use of data to drive decisions and then publicly condemning... the use of data to drive decisions.<p>Aside from that contradiction, the company just got seed funding four months ago. It&#x27;s probably far too early to make decisions about the efficacy of being &quot;data-driven.&quot; From personal experience, trying to manage people by telling them, &quot;I&#x27;m right, let&#x27;s do it my way,&quot; is terribly demotivating (and very prone to error). Conversely, trying to weigh everyone&#x27;s input equally and sift out good ideas is an organizational nightmare that creates a ton of complexity. Complexity slows down execution. And who decides on the best ideas?<p>Creating a mental framework for hypothesis testing and building a product based on optimizing for specific metrics is, in my mind, what being data-driven actually means. There are no inconsistencies or personal biases. It&#x27;s scalable. You can teach the entire team how to approach the design of a feature as a problem with a testable hypothesis. Politics go out the window as execution strategy is determined by return on investment of engineering resources. Being data-driven doesn&#x27;t discourage creativity, it just allows you to reframe problems.<p>Buzzfeed clickbait titles are but a small (and, well, effective) subset of a vast array of largely positive things that come from being &quot;data-driven.&quot; Attempting to demonize patterns of logical, rational decision-making because you (personally) don&#x27;t like one outcome is... well, an anti-pattern. (It happens all of the time. See: The history of the scientific method. ;))<p>Sure, it&#x27;s not sexy. But it doesn&#x27;t need to be. It just needs to work.<p>1. <a href="https://www.crunchbase.com/organization/bipsync" rel="nofollow">https:&#x2F;&#x2F;www.crunchbase.com&#x2F;organization&#x2F;bipsync</a>

In God we trust; all others must bring data.

The problem with the data-driven approach is you might be optimising towards a local maximum.

I struggle to reconcile the data-driven approach to running products and companies with innovator&#x27;s dilemma.<p>It seems like micro-decisions are best made after looking at the data, but macro decisions are not.

Data-driven decisions will get you into local maxima, but you&#x27;ll get stuck there when there&#x27;s a good chance that a more radical change would help much, much more.

Vision is a fancier way of saying &quot;dumb luck&quot;.

This reminds me of Warren Buffet&#x27;s approach to investing. It&#x27;s better to be approximately right than precisely wrong. I fear that all we are doing with data is figuring out a precise way to fail instead of focusing on being right.<p>&quot;But fail often and fast&quot; the cliche goes, but what about the opposite, it should be true by simple negation of logic right?<p>&quot;Win seldomly and slow&quot;, then suddenly collecting data on every useless piece of data becomes futile. You are not focused on winning and without the burden of speed and pressure to screw things up. You are absolutely calm and able to think things through.