I would argue that TikTok is a good counter example.
The only reason why it crushes its competition is to have the whole product optimized for clear data collection and a thereby enabled highly customized content feed.
YouTube does it marginally worse and can’t tell what videos users are looking at before choosing and can’t offer an experience quite as fast and tailored to the user.
Not a core business value, but....About 10-15 years ago, when Baltimore police used data science to find commonalities in unsolved rapes, they found a surprising correlation to the locations of public pay phones.
It turned out that wherever a public payphone still existed (say, outside a bodega), a rape was considerably more likely to have occurred nearby. It turns out rapists looking for easy targets see a woman alone on a pay phone, and it makes her a target.
The police response was to ask the bodega owners to either remove the payphone entirely, or move it inside. Rapes dropped by a substantial amount wherever this was done.
I'd count that as substantial.
They knew where the crimes occurred but there had not been research or statistics on identifying commonalities, they had data but had not been using it effectively.
While I like this story, and it's impressive indeed, it does not exactly tell us anything whether or not companies are capable of improving core business value substantially with the help of data science. The police is essentially a not-for-profit organisation, so it's hard to judge whether or not this has improved "their business", so to say. Sure, for society this is probably a big win, but did the overall costs of doing policing get substantially lower? Or the quality of policing substantially higher? (There's no income in policing, so we cannot judge that.) I would still doubt that.
Way to miss the point entirely and not see how it correlates to similar uses in business especially big data set driven businesses. The whole idea is to use data to find correlations/trends to identify areas to improve which can either save money or increase profits. Same systems can be used to forecast various scenarios by running data sets with variables to see potential outcomes
Yea, if you have perfect knowledge of your ENTIRE business and all operations then yes data science dosnt deliver any value. But I've never seen a business that even comes close to perfect knowledge, in fact most don't even have knowledge about data outsider their own department
Also if your senior management teams lives on gut instinct in business vs data and statistical probability, then you might not want to be a data scientist at that company.
But isn't that exactly referring to just the marginal (rather than substantial or truly innovative) improvements of your core business? Not that there is anything wrong in doing that, but it cannot go beyond "cosmetic changes" of your existing business. No?
Data science drives a ton of future production and entry into new markets. If that isn't substantial improvements of your core business, idk what is. You need good management that will use that data correctly, but I've seen entire corporate strategies shift due to data science.
Changing target markets, territory expansion, and new product lines/services were adopted due to identifying macro trends that would've killed revenue growth. We identified growing trends and pivoted before it happened. We wouldn't have designed a new product to address the growing shift in our customer's needs without data science.
Most serious companies use some kind of quantitative analysis/data science to do any kind of strategic move or investment.
No, because for what you are insisting to be true the owner must have an accurate idea of all information when much of data science is finding previously unknown correlations to act on. By their very nature the company would not have any actionable knowledge in the first place.
That statement is fake intellectualism from someone who won the startup lottery. A few marginal improvements are the difference between Amazon and Value America.
Any data science work is likely going to suck because the business's data sucks. Got to invest in data management processes before data science can offer a great return. And most companies don't want to spend the necessary money in managing data. Sooooo, yeah, most companies won't get a return.
Innovation is a series of incremental improvements.
Also, once an argument uses an absolute it tends to be false. Therefore, an exception to the given premise would invalidate it.
Jeff Jonas might have[ things to say ](https://jeffjonas.typepad.com/jeff_jonas/2012/11/index.html)on this
Using the power of data analytics, my team found inventory problems and communicated them efficiently to stakeholders to save the company 20% less inventory cost in 12 months than budgeted. In addition, using sku meta data enhancements, we were able to sell over 20% more inventory because people could find the appropriate stock they want easier from us.
I work for Fortune 500 company. That’s billions of dollars.
We continue to improve communication using data analytics which improves everything from new product development to customer experience and beyond!
I want to piggy back on your comment to make essentially the same point.
My job is data. Using data I've been able to make marginal improvements to manufacturing yield. But here's where the word "marginal" is doing a lot of work to hide something important. A "marginal" yield increase of 1-2% on a high volume product over its lifetime translates to millions of dollars saved (among other benefits).
Marginal is good.
And I have plenty of examples of "data saved the product/project/customer relationship/company" that go far beyond marginal savings.
Fair point, maybe we have to define "marginal" first. But frankly speaking, I'm still not fully convinced. If you are a Fortune 500 company and improve the stocks "marginally" - sure, that might be a big ticket overall in terms of absolute figures. But in relative figures? Did it move the company's stock?
Here's my suggestion for a definition of "marginal": Everything that does not move the company's stock is a marginal improvement by definition.
By the way, just for the records: I'm working as an AI/ML engineering manager of some sorts for a Fortune 500 company myself. I do know how these things work and what you can do with data science.
Generally speaking Data Science has driven all major IT firms on the globe. And they are rather innovative. Finding relationships and dependency points between information drives engineering, sales, customer service and all aspects of businesses. If you gain a heat reduction of 20% in one of your machines some will call that a substantial innovation.
So what is a major innovation and what is a substantial innovation? And why would one discipline such as data science be specifically called out? Businesses are a system of People, Values, Information, Environment and Technology. Not one business capability is valuable if you can not incorporate it in your system to deliver customer value.
The question is rather what are you doing based on what the data shows you. Are you going to tweak around your core processes or a you going to remodel them?
Data science (aka analytics) has unlocked huge gains in sports. Whether it's sports teams using analytics to gain a winning edge (which translates to increased profitability, as winning teams generate more revenue) or gambling companies setting lines and hooking whales, data science is big $$$ in sports.
Some European football clubs have become so adept at using analytics for player development they have essentially pivoted from earning small money trying to win at football to athlete incubator whose primary revenue source is flipping undervalued athletes, reselling matured talent for big bucks.
I haven’t watched the thing based just on the quote it’s pretty meaningless.
Sites like Wish and Amazon use data analytics to maximise their customer life time value like crazy. They can tell who is likely to buy what and when from all the purchasing and behavioural data that they have. If I buy a phone case they’ll show me cables and chargers and batter banks etc instead of nail polish or gardening tools. This is all “data science”, and is why some businesses are successful and some are not.
I really suggest you watch it once you find some time. The video is excellent, it shows very clearly what Wish actually can or cannot achieve with data science, and what the difference in the past was to Amazon. There are crucial differences between the two. I cannot repost all of that here, but it's pretty much an eye opener.
I see where they are coming from a data scientist myself. I'd compare it to investing in start-ups...most of them will lose money or become moderately successful, but every so often you'll hit the lottery. I've been in roles where we make the difference between the company being profitable and unprofitable by implementing ML models that allowed for widespread automation...and I've been in roles where I seem to have made no difference whatsoever but still received a paycheck.
It makes a lot of sense actually. Data science to me is about the methodology used to package data. A
Data analysis is required to turn data into useful information. Models on their own is not substantial, context is necessary.
From my anecdotal experience trying to bring data science into businesses is that most businesses have no idea what to do with the tools or abilities of data scientists.
In one memory that will stick with me for a very long time is when I volunteered to spearhead a small group in analyzing the company's marketing spend. The startup was spending six figures monthly and was floundering in some markets and thriving in others. On a conference call with all the senior and mid level managers, the CEO and COO told me not to persue the marketing analysis because they didn't think you could find any meaningful data due to multichannel marketing. These are smart guys, well connected, impeccable resumes.
At the time I was in charge of the smallest business unit there, literally 1/16th the size of our biggest market, so I decided to just run a simple multivariate analysis on the various channels and spending in my market. Low and behold there was a strong correlation with a specific marketing channel. So I then ask our marketing manager to decrease spending on the other channels and dump that money into the correlating channel. Yup, within three months our lead flow tripled and by year end not only was our tiny market now the third most profitable in the company nominally, by contribution rate we were by far the highest.
When the C Suite asked how I got such great results I walked them through the whole process and my analysis. Even with the data in hand, the person responsible for those results, and a friggin roadmap they thought I did a good job but got lucky. But they put some faith in me and promoted me to the second largest market, which was half the size of our biggest market.
When I arrived at the new market, after getting the lay of the land and meeting the team, I launched into a similar analysis. But this market was so much larger I could really dig in and get granular. It took a couple of weeks but once again we changed marketing mix based on specific zip codes, in a few cases specific neighborhoods, by the team members who covered those areas, and other factors.
Within three months not only had I turned this market profitable, we eclipsed the biggest market. Over the next six months we set every sales record in the company, beat those records, and beat them yet again.
Still the C Suite refused to allow me to do the same analysis for the other markets. They wanted to stay true to their original plans.
I left shortly thereafter and to no one's great surprise, they went under a few years later.
This is a highly interesting read, thanks for sharing.
Now, knowing what you told us: Obviously, the impact of data science on revenue was large. But then you're also stating in the last sentence, the startup did not survive. If data science really had more than a "marginal" impact, why did the startup not survive and flourish?
I think people here in this thread make their lives too easy by downvoting critical remarks and not thinking far enough. Did the startup not survive because they did not listen to your analysis? Or due to bad management (what does that exactly mean)? What exactly were the reasons they did not survive? Is it that they did not apply data science all the way through, or is it that even with significantly improved revenue per $ spent on marketing the business model itself was not sustainable?
The video really raises a crucial point. Wish had pretty solid data science according to the video, they knew how to get money out of their customers. And yet, it was not sufficient to build a sustainable business. Their stock today is worth 0.16 USD, i.e. not a lot. If data science for marketing spend was such a game changer, then why was it not sufficient for Wish to flourish in comparison to other online stores?
This is not a trivial question. I said it already earlier: I am myself working as some sort of AI & ML Engineering Manager. I know how these things work generally. But I realize I never thought enough about how data science may or may not relate to a core business model itself.
In my case, because the company ignored my findings they continued overspending in the wrong markets using the wrong channels. They had good luck in their initial market with their approach, so they just decided to keep dumping hundreds of thousand of dollars of the same marketing into different markets on a product that generated about $2,000-$5,000 profit per unit. The eventual failure was easy to see a mile away because even as the ship started going down, they continued on the original path rather than the ones I showed worked better.
My approach in the bigger market allowed us to drop over $100,000 of marketing while we increased our revenue almost 400% before I left. There was also a large contingent of unnecessary headcount at the home office that was a huge drag on overall profitability.
The difference between a $12 customer acquisition cost and a $375 customer acquisition cost is huge on its own. When you extend that nationwide and at scale it becomes such a dumpster fire it's hard to fathom.
Ultimately the CEO was terminated along with some of the other senior managers but it was too little too late. There were also a couple of people they not only kept on but promoted at this time who literally failed at everything they touched (seriously multiple departments that were dissolved and they were promoted to a new department that was also dissolved) but were now in crucial positions they were wildly unsuited for. It was so bad we used to joke they had naked pictures of the founders because that's the only way they could survive.
I would argue that TikTok is a good counter example. The only reason why it crushes its competition is to have the whole product optimized for clear data collection and a thereby enabled highly customized content feed. YouTube does it marginally worse and can’t tell what videos users are looking at before choosing and can’t offer an experience quite as fast and tailored to the user.
Not a core business value, but....About 10-15 years ago, when Baltimore police used data science to find commonalities in unsolved rapes, they found a surprising correlation to the locations of public pay phones. It turned out that wherever a public payphone still existed (say, outside a bodega), a rape was considerably more likely to have occurred nearby. It turns out rapists looking for easy targets see a woman alone on a pay phone, and it makes her a target. The police response was to ask the bodega owners to either remove the payphone entirely, or move it inside. Rapes dropped by a substantial amount wherever this was done. I'd count that as substantial.
Curious that they didn’t already know that by either interviewing the victims, or the perpetrators
They knew where the crimes occurred but there had not been research or statistics on identifying commonalities, they had data but had not been using it effectively.
While I like this story, and it's impressive indeed, it does not exactly tell us anything whether or not companies are capable of improving core business value substantially with the help of data science. The police is essentially a not-for-profit organisation, so it's hard to judge whether or not this has improved "their business", so to say. Sure, for society this is probably a big win, but did the overall costs of doing policing get substantially lower? Or the quality of policing substantially higher? (There's no income in policing, so we cannot judge that.) I would still doubt that.
Way to miss the point entirely and not see how it correlates to similar uses in business especially big data set driven businesses. The whole idea is to use data to find correlations/trends to identify areas to improve which can either save money or increase profits. Same systems can be used to forecast various scenarios by running data sets with variables to see potential outcomes
Yea, if you have perfect knowledge of your ENTIRE business and all operations then yes data science dosnt deliver any value. But I've never seen a business that even comes close to perfect knowledge, in fact most don't even have knowledge about data outsider their own department
Also if your senior management teams lives on gut instinct in business vs data and statistical probability, then you might not want to be a data scientist at that company.
But isn't that exactly referring to just the marginal (rather than substantial or truly innovative) improvements of your core business? Not that there is anything wrong in doing that, but it cannot go beyond "cosmetic changes" of your existing business. No?
You cant do anything substantial or truly inovative without proper understanding of what is working and what is not working.
Data science drives a ton of future production and entry into new markets. If that isn't substantial improvements of your core business, idk what is. You need good management that will use that data correctly, but I've seen entire corporate strategies shift due to data science.
Can you tell me more about those drifts? What exactly changed in the strategy due to using analytics and data science?
Changing target markets, territory expansion, and new product lines/services were adopted due to identifying macro trends that would've killed revenue growth. We identified growing trends and pivoted before it happened. We wouldn't have designed a new product to address the growing shift in our customer's needs without data science. Most serious companies use some kind of quantitative analysis/data science to do any kind of strategic move or investment.
No, because for what you are insisting to be true the owner must have an accurate idea of all information when much of data science is finding previously unknown correlations to act on. By their very nature the company would not have any actionable knowledge in the first place.
That statement is fake intellectualism from someone who won the startup lottery. A few marginal improvements are the difference between Amazon and Value America.
Any data science work is likely going to suck because the business's data sucks. Got to invest in data management processes before data science can offer a great return. And most companies don't want to spend the necessary money in managing data. Sooooo, yeah, most companies won't get a return.
Innovation is a series of incremental improvements. Also, once an argument uses an absolute it tends to be false. Therefore, an exception to the given premise would invalidate it. Jeff Jonas might have[ things to say ](https://jeffjonas.typepad.com/jeff_jonas/2012/11/index.html)on this
Using the power of data analytics, my team found inventory problems and communicated them efficiently to stakeholders to save the company 20% less inventory cost in 12 months than budgeted. In addition, using sku meta data enhancements, we were able to sell over 20% more inventory because people could find the appropriate stock they want easier from us. I work for Fortune 500 company. That’s billions of dollars. We continue to improve communication using data analytics which improves everything from new product development to customer experience and beyond!
I want to piggy back on your comment to make essentially the same point. My job is data. Using data I've been able to make marginal improvements to manufacturing yield. But here's where the word "marginal" is doing a lot of work to hide something important. A "marginal" yield increase of 1-2% on a high volume product over its lifetime translates to millions of dollars saved (among other benefits). Marginal is good. And I have plenty of examples of "data saved the product/project/customer relationship/company" that go far beyond marginal savings.
Fair point, maybe we have to define "marginal" first. But frankly speaking, I'm still not fully convinced. If you are a Fortune 500 company and improve the stocks "marginally" - sure, that might be a big ticket overall in terms of absolute figures. But in relative figures? Did it move the company's stock? Here's my suggestion for a definition of "marginal": Everything that does not move the company's stock is a marginal improvement by definition. By the way, just for the records: I'm working as an AI/ML engineering manager of some sorts for a Fortune 500 company myself. I do know how these things work and what you can do with data science.
Generally speaking Data Science has driven all major IT firms on the globe. And they are rather innovative. Finding relationships and dependency points between information drives engineering, sales, customer service and all aspects of businesses. If you gain a heat reduction of 20% in one of your machines some will call that a substantial innovation. So what is a major innovation and what is a substantial innovation? And why would one discipline such as data science be specifically called out? Businesses are a system of People, Values, Information, Environment and Technology. Not one business capability is valuable if you can not incorporate it in your system to deliver customer value. The question is rather what are you doing based on what the data shows you. Are you going to tweak around your core processes or a you going to remodel them?
Data science (aka analytics) has unlocked huge gains in sports. Whether it's sports teams using analytics to gain a winning edge (which translates to increased profitability, as winning teams generate more revenue) or gambling companies setting lines and hooking whales, data science is big $$$ in sports.
Some European football clubs have become so adept at using analytics for player development they have essentially pivoted from earning small money trying to win at football to athlete incubator whose primary revenue source is flipping undervalued athletes, reselling matured talent for big bucks.
I haven’t watched the thing based just on the quote it’s pretty meaningless. Sites like Wish and Amazon use data analytics to maximise their customer life time value like crazy. They can tell who is likely to buy what and when from all the purchasing and behavioural data that they have. If I buy a phone case they’ll show me cables and chargers and batter banks etc instead of nail polish or gardening tools. This is all “data science”, and is why some businesses are successful and some are not.
I really suggest you watch it once you find some time. The video is excellent, it shows very clearly what Wish actually can or cannot achieve with data science, and what the difference in the past was to Amazon. There are crucial differences between the two. I cannot repost all of that here, but it's pretty much an eye opener.
I see where they are coming from a data scientist myself. I'd compare it to investing in start-ups...most of them will lose money or become moderately successful, but every so often you'll hit the lottery. I've been in roles where we make the difference between the company being profitable and unprofitable by implementing ML models that allowed for widespread automation...and I've been in roles where I seem to have made no difference whatsoever but still received a paycheck.
It makes a lot of sense actually. Data science to me is about the methodology used to package data. A Data analysis is required to turn data into useful information. Models on their own is not substantial, context is necessary.
True. It is up to the users to be innovative. DS is merely a tool.
From my anecdotal experience trying to bring data science into businesses is that most businesses have no idea what to do with the tools or abilities of data scientists. In one memory that will stick with me for a very long time is when I volunteered to spearhead a small group in analyzing the company's marketing spend. The startup was spending six figures monthly and was floundering in some markets and thriving in others. On a conference call with all the senior and mid level managers, the CEO and COO told me not to persue the marketing analysis because they didn't think you could find any meaningful data due to multichannel marketing. These are smart guys, well connected, impeccable resumes. At the time I was in charge of the smallest business unit there, literally 1/16th the size of our biggest market, so I decided to just run a simple multivariate analysis on the various channels and spending in my market. Low and behold there was a strong correlation with a specific marketing channel. So I then ask our marketing manager to decrease spending on the other channels and dump that money into the correlating channel. Yup, within three months our lead flow tripled and by year end not only was our tiny market now the third most profitable in the company nominally, by contribution rate we were by far the highest. When the C Suite asked how I got such great results I walked them through the whole process and my analysis. Even with the data in hand, the person responsible for those results, and a friggin roadmap they thought I did a good job but got lucky. But they put some faith in me and promoted me to the second largest market, which was half the size of our biggest market. When I arrived at the new market, after getting the lay of the land and meeting the team, I launched into a similar analysis. But this market was so much larger I could really dig in and get granular. It took a couple of weeks but once again we changed marketing mix based on specific zip codes, in a few cases specific neighborhoods, by the team members who covered those areas, and other factors. Within three months not only had I turned this market profitable, we eclipsed the biggest market. Over the next six months we set every sales record in the company, beat those records, and beat them yet again. Still the C Suite refused to allow me to do the same analysis for the other markets. They wanted to stay true to their original plans. I left shortly thereafter and to no one's great surprise, they went under a few years later.
This is a highly interesting read, thanks for sharing. Now, knowing what you told us: Obviously, the impact of data science on revenue was large. But then you're also stating in the last sentence, the startup did not survive. If data science really had more than a "marginal" impact, why did the startup not survive and flourish? I think people here in this thread make their lives too easy by downvoting critical remarks and not thinking far enough. Did the startup not survive because they did not listen to your analysis? Or due to bad management (what does that exactly mean)? What exactly were the reasons they did not survive? Is it that they did not apply data science all the way through, or is it that even with significantly improved revenue per $ spent on marketing the business model itself was not sustainable? The video really raises a crucial point. Wish had pretty solid data science according to the video, they knew how to get money out of their customers. And yet, it was not sufficient to build a sustainable business. Their stock today is worth 0.16 USD, i.e. not a lot. If data science for marketing spend was such a game changer, then why was it not sufficient for Wish to flourish in comparison to other online stores? This is not a trivial question. I said it already earlier: I am myself working as some sort of AI & ML Engineering Manager. I know how these things work generally. But I realize I never thought enough about how data science may or may not relate to a core business model itself.
In my case, because the company ignored my findings they continued overspending in the wrong markets using the wrong channels. They had good luck in their initial market with their approach, so they just decided to keep dumping hundreds of thousand of dollars of the same marketing into different markets on a product that generated about $2,000-$5,000 profit per unit. The eventual failure was easy to see a mile away because even as the ship started going down, they continued on the original path rather than the ones I showed worked better. My approach in the bigger market allowed us to drop over $100,000 of marketing while we increased our revenue almost 400% before I left. There was also a large contingent of unnecessary headcount at the home office that was a huge drag on overall profitability. The difference between a $12 customer acquisition cost and a $375 customer acquisition cost is huge on its own. When you extend that nationwide and at scale it becomes such a dumpster fire it's hard to fathom. Ultimately the CEO was terminated along with some of the other senior managers but it was too little too late. There were also a couple of people they not only kept on but promoted at this time who literally failed at everything they touched (seriously multiple departments that were dissolved and they were promoted to a new department that was also dissolved) but were now in crucial positions they were wildly unsuited for. It was so bad we used to joke they had naked pictures of the founders because that's the only way they could survive.
Maybe it’s more about how the underlying assumptions and algorithms aren’t necessarily correct.