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>"the pandemic pushed all these tech companies up artificially. they hired in record numbers to meet demand and thought it would be permanent/the new floor." T

>"the pandemic pushed all these tech companies up artificially. they hired in record numbers to meet demand and thought it would be permanent/the new floor." This is the really surprising thing to me - many(most?) of these companies are data driven companies. Companies with teams of analytics folks, data scientists, modeling-experts and teams of engineers working on tools to do ML and large scale data analytics. Do none of them actually use their tools and internal expertise to model their own hiring practices and historical market trends? Or maybe they did and always knew they would need to do mass layoffs when inevitably the cheap money pump got shut off?

anikom15 4y ago
No amount of machine learning, analytics, statistics, models, and experts can predict the future.
- bogomipz 4y ago
  No of course not but I would think it could certainly help to temper exuberance. Especially if you were considering a spectrum of uncertainties.
mjr00 4y ago
I hope that if we've learned anything from the past few years, it's that you can interpret and manipulate data to make it say whatever you want to say. That's true even when analyzing data about the present, doubly so trying to predict the future. Being data-driven is a method for justifying decisions. But it's still a human making a decision in the end.
- apohn 4y ago
  >But it's still a human making a decision in the end I've got a story here from a previous job that illustrates this. I used to be part of an Analytics and Forecasting team at a well known non-tech company. It's a global company that sells a lot of things to consumers. This means they strive to have a very good understanding of sales and manufacturing volume because that drives everything from raw material orders and supplier contracts to how much of product to put where so people can go and buy it. We had short term forecasting teams generating forecasts (at a brand level) and analysts (mostly aggregations, not really statistical inference) that rolled into a medium term analytics/forecasting team(s), that rolled into a long term analytics/forecasting team(s). Not to mention Research teams that did pure statistical and economic modeling to try and understand market factors in a more comprehensive way so they could better inform all these forecasts. All of this went right to the top of the food chain. The glue between all these teams were managers. And one of the fundamental problem for managers with all of this comes from this simple question - if one of these teams provides a forecast that is very different from the rest, what does that mean? Is that team right or wrong? And how does that information now flow into the other teams so they can incorporate it into the info that goes upstairs. From this question emerges an astonishing amount of group think. Not just from the company I worked at, but at all competitors as well. When companies ask "Do we forecast sales to go up" they are really asking "Do we think this market segment is going to go up in the future and we have the right strategy to move up with it?" Cars, Snacks, phones, whatever the market segment, you're really asking how you are doing compared to your competitors in the space. To understand where you are going, you need to understand how your competitors are doing. This data comes from agencies and consulting houses. But since everybody uses the same data from the same agencies, everybody is feeding each other. What can emerge from this is a massive amount of group think. I'm greatly simplifying everything of course. When these teams get it right they do amazing stuff like making sure the thing you want to buy is available when you want to buy it. But when they get it wrong, it seems like most everybody else gets it wrong as well. This can slowdown an entire industry as everybody realizes they got it wrong.
noobermin 4y ago
What do you do when the model says your favorite project (say the one that got you promoted or is getting you promoted) is a bad idea? Heck, forget fancy modeling, what does an excel sheet say? As old as the bible, the saying, "Physician, heal thyself!"
minwcnt5 4y ago
The risks of under-hiring if things had continued and not being able to keep up with competitors was probably just too great. It's much easier to simply lay people off if the bubble pops than to play catch up if it doesn't.
SpicyLemonZest 4y ago
They thought there was a real and persistent break from historical market trends, similar to the break from historical remote work trends.
mandeepj 4y ago
> Do none of them actually use their tools and internal expertise to model their own hiring practices and historical market trends? This! It’s exactly what have occupied my mind since layoffs started. May they did use tools. But, remember the output is a forecast, not a shining light out of a crystal ball. Estimates/Forecasts can go wrong and clearly turned out that way.
mrtranscendence 4y ago
I work as a data scientist in the grocery industry, specifically (at least for the past six months) on sales forecasts. Even for relatively short-term forecasts accuracy is surprisingly low, even with lots of data and the most up-to-date forecasting methods, applied by smart people (those who aren't me). I'm not at all surprised that companies can't see precisely when downturns will happen.

anikom15 4y ago

No amount of machine learning, analytics, statistics, models, and experts can predict the future.

bogomipz 4y ago
No of course not but I would think it could certainly help to temper exuberance. Especially if you were considering a spectrum of uncertainties.

mjr00 4y ago

I hope that if we've learned anything from the past few years, it's that you can interpret and manipulate data to make it say whatever you want to say. That's true even when analyzing data about the present, doubly so trying to predict the future. Being data-driven is a method for justifying decisions. But it's still a human making a decision in the end.

apohn 4y ago
>But it's still a human making a decision in the end I've got a story here from a previous job that illustrates this. I used to be part of an Analytics and Forecasting team at a well known non-tech company. It's a global company that sells a lot of things to consumers. This means they strive to have a very good understanding of sales and manufacturing volume because that drives everything from raw material orders and supplier contracts to how much of product to put where so people can go and buy it. We had short term forecasting teams generating forecasts (at a brand level) and analysts (mostly aggregations, not really statistical inference) that rolled into a medium term analytics/forecasting team(s), that rolled into a long term analytics/forecasting team(s). Not to mention Research teams that did pure statistical and economic modeling to try and understand market factors in a more comprehensive way so they could better inform all these forecasts. All of this went right to the top of the food chain. The glue between all these teams were managers. And one of the fundamental problem for managers with all of this comes from this simple question - if one of these teams provides a forecast that is very different from the rest, what does that mean? Is that team right or wrong? And how does that information now flow into the other teams so they can incorporate it into the info that goes upstairs. From this question emerges an astonishing amount of group think. Not just from the company I worked at, but at all competitors as well. When companies ask "Do we forecast sales to go up" they are really asking "Do we think this market segment is going to go up in the future and we have the right strategy to move up with it?" Cars, Snacks, phones, whatever the market segment, you're really asking how you are doing compared to your competitors in the space. To understand where you are going, you need to understand how your competitors are doing. This data comes from agencies and consulting houses. But since everybody uses the same data from the same agencies, everybody is feeding each other. What can emerge from this is a massive amount of group think. I'm greatly simplifying everything of course. When these teams get it right they do amazing stuff like making sure the thing you want to buy is available when you want to buy it. But when they get it wrong, it seems like most everybody else gets it wrong as well. This can slowdown an entire industry as everybody realizes they got it wrong.

noobermin 4y ago

What do you do when the model says your favorite project (say the one that got you promoted or is getting you promoted) is a bad idea? Heck, forget fancy modeling, what does an excel sheet say? As old as the bible, the saying, "Physician, heal thyself!"

minwcnt5 4y ago

The risks of under-hiring if things had continued and not being able to keep up with competitors was probably just too great. It's much easier to simply lay people off if the bubble pops than to play catch up if it doesn't.

SpicyLemonZest 4y ago

They thought there was a real and persistent break from historical market trends, similar to the break from historical remote work trends.

mandeepj 4y ago

> Do none of them actually use their tools and internal expertise to model their own hiring practices and historical market trends? This! It’s exactly what have occupied my mind since layoffs started. May they did use tools. But, remember the output is a forecast, not a shining light out of a crystal ball. Estimates/Forecasts can go wrong and clearly turned out that way.

mrtranscendence 4y ago

I work as a data scientist in the grocery industry, specifically (at least for the past six months) on sales forecasts. Even for relatively short-term forecasts accuracy is surprisingly low, even with lots of data and the most up-to-date forecasting methods, applied by smart people (those who aren't me). I'm not at all surprised that companies can't see precisely when downturns will happen.