31–40 of 168 posts
This makes for a good litmus test of whether people are lying to you about software or, more likely, have absolutely no idea what they are doing.
Earlier quoted context omitted.
I find it strange that these are presented in tension, when they’re complementary. You can create situations where you have a lot of data but can’t reach conclusions, because you lack a narrative and explanatory model which “makes sense” of that data; inversely, you can convincingly argue complete nonsense that’s obviously contrary to facts. Deep understanding requires a model/narrative which fits the collection of d…
I was about to write that in case of Bezos with Amazon, the customer was simpler and the answer was to just pour money into it until you substituted the market, but I realise now that that is not that simple. It seems simple because we have hindsight. My main idea though is that it is very hard to foresee what the customer will want after you deliver the product. Not what the customers want now, because sometimes the…
(Disclaimer: I used to work on a customer sentiment analysis team at Amazon, doing a lot of surveys.)
Amusingly, the two paragraphs after what I cited agree on that danger:
> The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.
> These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.
I don’t think the digital revolution was lost on Kodak — I think that for organizational reasons they couldn’t pivot.
> The first actual digital still camera was developed by Eastman Kodak engineer Steven Sasson in 1975. He built a prototype (US patent 4,131,919) from a movie camera lens, a handful of Motorola parts, 16 batteries and some newly invented Fairchild CCD electronic sensors.
https://www.cnet.com/google-amp/news/history-of-digital-came...
I know that's not what the article says per se, but it's only one slightly abstracted reinterpretation removed, as OP's title demonstrates.
Data doesn't lie; it could be nuanced, yes, but if its truthful then you cannot really argue against that.
You know, in data science, you see people spending hours writing pandas scripts that replicate a few clicks in excel for a one of analysis. You see datasets of a few gigabytes being processed with spark when SQL would be fine. You see ML techniques being thrown at questions that could be answered simply and reliably with basic statistical tests.
Especially in the B2C space a lot of companies, departments, products don't actually have a lot of customers and certainly not many decision makers. The N number is always going to be low. You can just talk to people. Let's say you are doing pretty well and running a SaS with 1000 corporate customers paying a million each - that's a billion dollar revenue - you can just talk to them. Certainly you can just talk to every single person who signs the cheque and those are the only people that matter.
And which is easier - putting together a thorough suite of A/B tests or getting some real customers to use your app on video and talking to them about what they are finding annoying, useful, missing? I see less people do that than you'd think.
This reminds me of the Principal Chalmers meme. In this case, first pondering whether he is wrong, only to conclude that it's the data that's wrong. I know that's not what the article says per se, but it's only one slightly abstracted reinterpretation removed, as OP's title demonstrates.
Principal Skinner; Chalmers was the superintendent.
Obligatory: https://en.m.wikipedia.org/wiki/All_models_are_wrong
https://www.google.com/search?q=data+driven+companies+more+p...
Any good-argument-driven based argument you attempt to make is almost always based on political motivating factors, rather on what is good for the business.
Intuition driven decisions work when the market is behaving normally, however, are generally too slow in a fast changing market like we have been since the start of COVID.
While I agree completely with the premise of this article, on the other hand I'm weighing the relatively robust findings by Meehl et al. They find, time and time again, in all sorts of fields, that extremely parsimonious models like equal-weighted linear regression of one or two predictors outperform expert judgment[1]. One would think this is cognitively dissonant enough, but it gets worse: This article, with the th…
When the psychiatric profession or Google or whoever else use experimentation to decide on what criteria they should follow, with sound controls, valid statistical analysis and loads of replication, they either arrive at evaluation procedures without much bias or, more likely, they realize the phenomenon they're trying to measure is almost all noise with no or excessively weak signals.
A better approach would be to acknowledge as much normative bias as possible up front, then conduct tests using sound experimental design. But the problem with this approach is that the data shows performing a bunch of well-crafted experiments is expensive, and management doesn't buy in if the vast majority are unlikely to reject the null. That leaves us which a class of "data driven" managers who are in fact indulging their biases to a sometimes extreme degree, using "the data" as a shield.
I won’t belabor the point because others have already made it: this article assumes there is some way to sort through good and bad arguments in the absence of data - a pretty big leap. The reality is all of our arguments are appealing to some sort of data (eg previous experience), it’s just that it doesn’t always fit in a neat definition of data. Obligatory: https://en.m.wikipedia.org/wiki/All_models_are_wrong