What Everyone is Getting Wrong About Snowflake (Tech Strategy – Podcast 105)

This week’s podcast is about Snowflake, the “data ecosystem” company. It is one of the most compelling digital business models out there. It is one of the few companies that could become the next Google or Microsoft.

You can listen to this podcast here or at iTunes and Google Podcasts.

Here is my new book (released December 1):

A summary of the Digital Operating Basics:

  1. Scale and growth and small incremental cost.
  2. Personalization. A personalized consumer experience is key to continued growth. Continually innovating on the consumer experience enables cross-selling more and more products and services to a broad audience“market of one” is the ultimate personalization.
  3. A digital core for operations. Algorithms and data are essential weapons.
  4. Ecosystem and connectedness. Every major player needs at least 10 partners for sharing data, meeting a range of consumer preferences, growing faster than it otherwise could and continually refreshing with technology and innovation.
  5. People, culture and work design form a social engine that enables innovation and execution personalized for each customer. Decision-making is designed for innovation and speed.
  6. Operational cash flow. Companies need powerful moneymaking models. Target a big opportunity and increase cash gross margin by innovation and cost reductions over time. Fund multiple experiments against consumer experience. Add revenue streams on the same digital core.

Here is a previous podcast on that.


Related articles:

From the Concept Library, concepts for this article are:

  • Complementary Platforms
  • Digital Operating Basics
  • Cloud Services

From the Company Library, companies for this article are:

  • Snowflake

Photo by Zdeněk Macháček on Unsplash


I write, speak and consult about how to win (and not lose) in digital strategy and transformation.

I am the founder of TechMoat Consulting, a boutique consulting firm that helps retailers, brands, and technology companies exploit digital change to grow faster, innovate better and build digital moats. Get in touch here.

My book series Moats and Marathons is one-of-a-kind framework for building and measuring competitive advantages in digital businesses.

This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.

——Transcription below

Welcome, welcome everybody. My name is Jeff Tauzin and this is Asia Tech Strategy. And the topic for today, what everyone is getting wrong about Snowflake, the quote unquote data ecosystem company. Now for those of you who are subscribers, I’ve been sending you quite a lot about this company in the last couple weeks. And I’ve actually mentioned it on the podcast that there’s a company floating out there that is really worth taking a look at. And that was basically Snowflake. So I’m gonna go through some of the main points about what I think people are getting wrong about this company. It’s really interesting in the sense that it’s a very compelling company, although the price is quite high if you’re looking at the stock right now. But it’s also got enough complexity in terms of its business model and its data strategy that I think people aren’t getting it right. I’ve been reading everything about this company that I can find. I don’t see it. Now, maybe it’s because most people don’t spend their days building out business models of digital companies like I do. Or maybe it’s just in my head, so I’ll let you decide. Anyways, that’ll be kind of the point today is what are the two things, two to three things I think people are really getting wrong about this company. And then to go through some of it. Now, that said, the vast majority of what I’ve been putting out on this company is really going to stay just for subscribers because I think there’s a lot of value here in my opinion. So this will be a bit higher level and definitely there’s a lot more out on the emails. And if you want to subscribe you can go over to jefftausen.com sign up there. There’s a free 30 day trial. See what you think. One other announcement I guess. The first part of my book series is now up on Amazon. It’s going to be released on December 1st. But it’s actually posted there now if you want to put in an order. I guess two. clarifications on that. Number one, this is gonna kinda be a long thing. It’s gonna come out in about six parts, five to six parts. So this is part number one that’s coming out on December 1st. The other thing is it’s a basic link at this point, so if you go up there, there’s not even a photo or a book cover. You’ll just see my name and the title and that’s all, because we’re still getting the book cover put together and stuff like that. So it’s pretty primitive in terms of being up on Amazon. But the title’s called the Motes and Marathons Part One. You can find it there. It’s how to build and measure competitive advantage in digital businesses. So anyways, that’s there. I’ll put the link in the show notes. And last thing, my standard disclaimer here, nothing in this podcast or in my writing or on the website is investment advice. The numbers and information for me and any guests may be incorrect. The views and opinions expressed may long be relevant or accurate. Overall, investing is risky. This is not investment advice. Do your own research. And with that, let’s get into the topic. Now as always, there’s a couple key concepts for today. The number one for today is really digital operating basics. Well actually there’s two main ones. Digital operating basics, which I will go through. I’ve talked about that a couple times, quite a bit in the past. It’s one of the six levels of my sort of framework. The other one is complementary platforms. So those are kind of the two big ones. I’ll go into those. You could also put FirstMover in there as well. I guess I’ll list that, but it’s really digital operating basics and complimentary platforms. Both are in the concept library. I’ll detail them as I go along. Now, why is Snowflake so interesting? It got my attention because Berkshire Hathaway had invested in it, and so had Geico, which is basically a Warren Buffett company. I mean, he’s been buying Berkshire. I mean, he has literally been involved in Geico. since he was like 23 years old, when he was at law school, I’m sorry, business school. And his teacher, Ben Graham, had invested in Geico years before that. He heard his guru, his mentor, was an investor and chairman. So he famously went down and knocked on the door of Geico on like a Saturday afternoon and ended up talking to the people there. And then ended up buying the whole company decades later. It was Geico as an investor. in Snowflake because they’re a big customer. Geico is a customer of Snowflake and Berkshire is. Anyways, anytime Buffett or a handful of other investors buy a company, I know they’re looking for competitive advantage. So once they do that, I start to take it apart and say, what is it they’re seeing here? So that’s how it got my attention. Very interesting company. Basically it’s described in a couple of ways. I’ll detail the backstory. But the terms you’re going to hear are… data ecosystem, data warehouse as a service, data storage as a service. The first thing that jumps out at me is, okay, it looks like the thinking in terms of the business model and strategy is fuzzy at best. And that’s awesome, because that’s right in my strike zone. I look for good companies that have some level of complexity in the business model digital-wise. that I think I can figure out a little better than other people. That’s kind of what I target. As soon as I hear the word ecosystem, okay, that’s a fuzzy word. Most people use the word ecosystem when they don’t know what else to call it. It’s sort of general, fuzzy, so that got my attention. And then they say, oh, it’s a data ecosystem. Well, that’s, I think the same thing when I hear the word data. I don’t even know what data is to tell you the truth. Like it’s a weird idea, it’s a weird concept. People talk about data, data ecosystems, data advantages, data network effects, and then you ask them to define data and nobody can do it. It’s kind of a BS concept. I think, I mean, what isn’t data? Isn’t everything data? Is a photo data? Is a blog data? Is one piece of information in a spreadsheet data? Is a video camera on the street gathering data? Isn’t everything data? So the fact that they say, oh, it’s a data ecosystem, I think, okay, this is where the thinking is probably not where it should be, that got my attention and jumped in. And I think this is, okay, this is a little bit, I guess, arrogant. I think we’re ahead of the curve on this one because I’ve read a lot about this company and I don’t think people are really getting it right. And I think maybe I am, but I’ll let you decide. I think I’m at least further down the path than most people looking at this company, in my opinion, but I could be wrong. Okay, so anyways, it’s the data ecosystem company. You ask people to define that, nobody can do it because it’s a really kind of a fuzzy thinking at best. I’ll give you some of the backstory of the company. Now, as mentioned, the company refers to itself as a data warehouse, as a service company, sometimes as a cloud computing company, a data warehouse. The language is fuzzy. They talk a lot about data cloud as their primary product offering. Now, when people use the word ecosystem, oftentimes it’s just fuzzy thinking, but usually what they’re meaning, they’re usually talking about platforms. People tend to use those words a little bit interchangeably. I try not to do that. Data ecosystem. Okay, so what’s it doing right now? It basically goes to companies, mostly large companies. They’re… ClientList is a who’s who of Fortune 500 companies. So they’re in the B2B sales business, mostly direct sales. This is not a self-service platform for the most part. And they offer companies the ability to consolidate, store, and analyze their data. And those words are important, consolidate, I’m sorry, consolidate, store, and analyze. These are kind of the key initial activities for a corporate client. So they say, look, it’s a service company, so you just log in, you know, SaaS company. You start to pull the data from various aspects of your business. Maybe it’s your warehouses, maybe it’s your sales force, maybe it’s your management team, maybe it’s your operations, but you consolidate all the quote unquote structured and semi-structured data unquote into one place. And those terms are important. Structured data and semi-structured, I’ll talk about that. And you create sort of a consolidated source of information, a warehouse for information, for data, which they say creates a quote unquote, single version of the truth for the company. Gets everyone on the same page. It creates transparency. It lets everyone start seeing the same numbers for what’s going on in the company. That’s very, very important. So consolidation and storage is sort of the beginning. then you start to analyze the data. You’ve got it all in one place. You start to build out various data analytics on top of this data science. If the first bit of consolidation is really about sort of data science getting it all crunched together, data engineering, I’m sorry, this is more about data analytics. You start to be able to do decisions much faster. You know, it’s not reports coming up every month, every week, real time, current decisions as you run the operation. And also it gives you ability to sort of time travel back and look at historical data. And then you can kind of see where that’s going because data analytics quickly turns into a situation of artificial intelligence, which is really prediction as opposed to analysis. And AI requires big, huge amounts of data to work. So everyone sort of starts looking like a big data company now because you got all your data in one place. And I mean, don’t underestimate, that is not how companies work. There is very little consolidated, transparent data in most companies. It’s usually one, there’s not a lot of data. It’s all scattered all over the place. It’s in spreadsheets and reports and purchase orders and emails and logs in the warehouse. Pulling that all together into one place is a big, big move. And it also lays the foundation for analytics, AI, and lots of other things. But step number one, data consolidation, and that’s their sort of key first offering. Okay, what happens next? Then you start to hear the word sharing. Well, if we’ve got all the data in one place, we can start to share the data internally. You know, this is, hey, I’m on, let’s say Dropbox, I wanna show you a file, you log into Dropbox, and you can see the file. Hey, let’s work on the file together. We both love being to Dropbox and start editing. You know, you can start to do analysis and other things based on data and you can share things internally. You can start to do team collaborations. You can start to do team projects that all draw on the data cloud, the data lake. Okay, that’s interesting. You can also start to share data outside of your company. So we can call it external sharing. Your partners. your supply chain, your customers, well, suddenly data starts going back and forth between you and your main suppliers, which has a lot of benefits. Now there’s obviously rules and governance that have to be put in place for that, but once you start to consolidate data, you can start to share it. Then you can pull data from suppliers and put it into your data lake and vice versa. So consolidation. leads to analysis. Consolidation also leads to sharing internally and externally. Next wave, you start to see people build apps on top of this data, very data intensive apps. So the other IT systems in your enterprise, application developers can start to write apps that build off these data lakes that all these companies have been. putting inside their companies basically. So that starts to look more like an operating system. That starts to look more like Android, but it’s like a data intensive Android. And suddenly there’s an app store that has hundreds of thousands, well, probably tens of thousands of apps all running off this. So you can sort of see that coming. And then there’s this big idea that cuts across all of this. Well, actually there’s one more idea. Let’s say the next thing that comes after that is this idea of a data marketplace. Okay, you’ve got a lot of data in your company. You’re all sharing it internally, externally. It’s giving a lot of transparency. It’s the basis for a lot of internal and external collaboration. Good, good, good. Application developers are starting to build apps that run on all of this data. And then there’s this idea, well, I could sort of sell my data if I wanted. Now most companies probably don’t want to do that, but other companies could do that. They could sell data to you. They could say, look, you have a lot of internal data. Would you like to buy some of our data? And you can basically build a marketplace between companies and other providers of data. So it’s like storage, analysis, sharing, collaboration, application development, and now marketplace transactions around data. So you can sort of see one leads to the next, to the next, to the next. And cutting across all of this is this idea of network effects. Doesn’t all of this get better the bigger you are? You know, network effects. The more someone uses a product, the more people that use a product, the better the product gets. More people on Zoom, Zoom becomes better for everyone. Doesn’t this get better? the more people that use it. The more numbers of users on the Snowflake system, they’re all putting in their data into their various company data lakes, but you can share a lot of that and application developers can start to build off a much larger corpus of data. So the number of users should increase the value of the service. Most analysis tends to get better with more and more data. So the more data within companies, the more data across all the companies, the analytics should get better. The more types of data you start to bring in, maybe we just start with documents and then we start going to photos and then we start going to video. The more types of data, the whole system should get better and smarter. The more granularity of the data, I mean, you can kind of break this down on lots of levels, but generally speaking, the more people using this sort of quote unquote, data ecosystem. You can see a lot of the core functions, sharing, analysis, AI, a data marketplace, application development, all of those things should get better and we sort of see what we’d call an increasing returns to scale. So that basic story I just told you is pretty standard and it’s what people are saying and you can see why people are getting excited about it. there looks like there’s some very powerful aspects to this business model. And if you look at the current market cap, it’s about $100 billion, which is about 100 times their sales in the last year, which is, you could view that as very, very high, or depending how you take it apart. Okay, so that’s kind of the basic story. And who are the customers for this company? I mean, it’s… Geico, Pepsi, Office Depot, McKesson, which is a big pharmaceutical distribution company. Pretty powerful competitive advantage actually in McKesson. Instacart, DoorDash, Pizza Hut, Kraft Heinz, Allianz, Western Union, Square, Hulu, Petco, Capital One. I mean, it is a who’s who of very impressive customers. So that gets people’s attention as well. And then you kind of look at the management team, and that’s pretty awesome too. Background of the company founded 2012, San Mateo, California, basically founded by three, arguably data warehousing experts. One of them, well, two of them worked at basically Oracle, which is a database company as data architects. And they founded the company, so they obviously know this stuff, Sequoia Capital and other investors get involved. Pretty impressive list of companies. And then in 2019, a gentleman named Frank Slutman takes over as CEO. And if you’re looking for sort of a rock star in enterprise software, CEO-wise, that’s Frank. He’s, his track record is pretty awesome. He’s taken at least two companies to really spectacular success, both in the data enterprise side of life. He led a company called Data Domain. When it started, it had about 20 people when he joined. He took it to IPO. It was sold for $2.4 billion to EMC. He then took over a company called ServiceNow, which is a company you should be familiar with. I don’t think I’ve covered that in the podcast. I’ve written a bit about them. He basically took them from $75 million in revenue in 2011 to $1.5 market cap of that company went up to about 50 billion. So that’s, you know, he’s hit two of them over the fence and then he’s taking over this company as of about two years ago. So everyone’s really excited about the company. And the financials look pretty good. It’s not making money, I won’t go through it, but it’s actually got pretty healthy gross margins, fantastically negative working capital, which I always like. Well. I mean, what do I think people are getting wrong about this company? Okay, that’s the basic story. Now, when I look at companies like this that clearly have a lot of moving parts, this is not like selling Pepsi. There is multiple things happening here. I sort of go through in detail as much as I can, and then when I feel like I’ve got all the details, I sort of pull myself up to a higher level, and I… I basically put on two sets of goggles. Goggles number one, what’s the view of the customer? Because that’s really where the rubber hits the road. At the end of the day, the customer walks in and do they like it? What do they care about? What is their process for buying and using this? Why would they choose this over something else? I walk myself through the customer experience. And then I do the same thing from the competitor view. If I was a competitor, could I take 20% of this business from them? And I try and just sort of get to a solid answer on those questions. So let’s say we look at the custom review. Now, this is where I start to differ. The CEO, Frank, he has a bunch of interviews. He’s actually doing some podcasts and stuff, which is they’ve got some marketing stuff going on. He talks about their core use case. And that’s really why someone would sign up for this as a customer. What is the core use case? And he cites a couple things. He says, look, number one, it’s the ease of use for digital transformation of a company. Virtually every company has to become digital. Now or in the future, it’s just the way the world will be. It’s actually doing digital transformation is actually quite hard. You hire consultants, you hire staff, it takes a long time. He’s making that very, very easy to begin the process. So it’s really easy to sign up. You can go and start warehousing and doing analysis of your data really within one day. And that’s shocking for people because, you know, most people think, okay, we’re gonna start to become more data-driven. We’re gonna consolidate our data into a data lake. then we’re going to start to do analysis. That’s a six week to three month process. No, no, you can do it today and you can get going. So this idea of making it super easy for companies to become data driven for the first time. And the first step is warehouse the data, start to do the analysis, that’s it. And when you do that, that is not a small thing. That is, you know, most companies have always functioned by sort of slow decision making based on occasional reports and lots of sort of anecdotal analysis and then management just making the best call. There was a lot of judgment and guessing at the management level. That’s the way business has always been. And you have some reports and you do some tracking, but that is a world away from real time data gathering and analysis. that’s coming up on the screens minute by minute where you can make decisions as a CEO management team sales agent and see the results in real time and you have advanced analysis in real time and you have real transparency. And then the AI starts to come in and they’re pretty great at this. So it’s a real shift away from this idea of let’s have a lot of people gathering reports, reading, doing anecdotal occasional analysis and then making their best call. Now let’s move to completely data-driven decision-making and operations across the organization. That’s a big change. And that’s kind of what the first use case is. How do you become data-driven? And then your operations basically become to a large degree digital. And you can start with just sort of descriptive models. You gather the data, you start to run models. The first models tend to be descriptive. Here’s a picture of what’s been going on. From there, you start to go prediction, which is AI. AI is cheap and fast prediction. Then you start to say, look, if the AI starts to recommend, if we move all our boxes from that warehouse to this warehouse, we think our costs will decrease by 1%, that’s a prediction. That’s AI. You start to move to predictive models, and then you start to go to what he would call prescriptive models. where the data systems analyze, predict, and decide themselves what to do without the humans involved. Suddenly, the inventory is moving around without people. The machines are doing it. And he calls this, you know, when you start to operate this way as a business, he calls it, you know, data and digital technologies, quote unquote, are the beating heart of business, unquote. That’s a huge deal. And that’s really what that main pitch to a customer is. Now a lot of companies are already data driven, but some are not. Okay, the second thing they offer is the consolidation of data. Most companies actually have very little data. It’s fragmented, it’s scattered. You got databases and lots of departments. Okay, the consolidation, that’s also a certain degree of value. And then the other one he cited was low latency. Suddenly, there’s no time between when the data comes in and when you can do analysis and when you can make decisions. The data can be coming in minute by minute. It’s raining in Bangkok. How do we want to change the inventory of all of our 7-elevens in this two blocks? The AI can do that immediately. The data is coming in. The analysis is happening. The predictions are being made. your latency, your speed of decision making, your speed of analysis drops dramatically. And those are kind of the three big benefits, he said, to users at this stage of the business. Okay, so if that’s the customer view today, how powerful is that? I mean, this is a judgment call. Is that awesome? Is that pretty good? Is that a moderate upgrade? Is a dramatic upgrade? You know, one of the things I’ve pointed to a lot is what I call the six digital superpowers. I mean, it’s a simplistic cheat sheet for when something really matters and when it doesn’t in the digital world. And number one on my list was does this digital tool technology or business model dramatically improve the user experience? You know, not just like, hey, I like, you know. I can now order my pizza on my phone as opposed to going downtown. That’s nice, but it’s not a dramatic improvement. It’s pretty good. It’s just a normal technological upgrade. That stuff happens all the time. Or does this make the competitors, does it put them out of business? Does it make them obsolete? WhatsApp was a dramatic improvement in the user experience for texting versus buying text messages from your carrier. What Alibaba is doing with supermarkets in China, I think, is a dramatic improvement. I would argue that this is a dramatic improvement in the user experience. One, that basic bit I just told you, but then all the things that come next, the sharing, the application mark, you know, the data marketplaces, the application developers building this thing, all of those things sit on top of that core use case I just told you. So you can kind of see that this is going to be a dramatic improvement in what companies can do based on their offering. And I think that’s true. So to me, yeah, it checks that box. Now the first concept for today was digital operating basics. If you go back, I’ll put a link in the notes to podcast 98, which was called Lessons in Digital Operating Basics from Ramchurran. And I detailed this out as really six things, which I consider just like, look, every business in the future is gonna be a digital business. And to be a digital business, you have to have your core operations operate that way, which in practice means six things. And I know some of this is from Dr. Ramchurian, who’s a big consultant, some of it’s from other people, but it’s pretty standard. You know, you have to go for scale and growth. based on data technology, digital, you have to go for personalization, you have to build a digital core for your operations. That your core business has to be based on data and algorithms, those are essential weapons for any business. Number four, you have to have some degree of connectedness as a business, you can’t be an isolated island anymore, you have to be connected with other parties, suppliers, customers, your greater ecosystem. And then you have some culture and some operational cashflow issues. But it’s really number three and number four on the digital operating basics. That I think that is basically the core use case of Snowflake. As the world goes digital and as all businesses become digital businesses, their core use case is effectively selling companies digital operating basics. I mean, look, I’ll read these again. Number three on digital operating basics. Businesses need a digital core for their operations. Algorithm and data are essential weapons. Number four, businesses need an ecosystem and a degree of connectedness. Every major player needs at least 10 plus partners that they share data with, that they work with to meet a range of consumer preferences that enables them to grow faster than normal. and that allows them to continually refresh their technology and innovation. Those two, number three and four on the digital operating basics, that’s the core use case of Snowflake. That’s why you start to think about how big could this business be? It’s almost like they’re offering core infrastructure for a digital world. I mean, literally, as far as I can tell, every business will need those three things, I’m sorry, those two things in the future. and that’s their core use case, and they seem to be really good at it. So the growth projection for this company is actually pretty challenging because it looks really, really big, but it’s kind of hard to put a number on. Anyway, so that’s kind of the first thing I think people are getting wrong about Snowflake is, one, how transformative their core offering to consumers is, customers is, and two, that really what they’re offering is the digital operating basics to everybody, every company. That’s kind of a big idea. Now, turning to the other viewpoint, we look at the competitor view. If I was a well-run, well-funded competitor, could I take 20% of this business’s market, customer base? And that’s really how people think about this stuff. And between those two factors, like how much do consumers like you and… How much are you vulnerable to your competitors? That pretty much tells you the trajectory of a company. Those are my sort of go-to, where the rubber meets the road questions at the end of all the analysis. Okay, and this is where I think people are getting, this is usually about the business model. And this is where I think the picture for Snowflake is just not right. For those of you who are subscribers, I mean, I basically titled the last couple articles. Snowflake is three plus complementary platforms with four plus network effects. That’s my summary of the business model. I can see at least three complementary platforms and at least four significant network effects. And that’s the other sort of big concept for today. Concept number one for today, digital operating basics. Concept number two, complementary platforms. If you look at my sort of pyramid, the top of the pyramid, the competitive fortress, the most competitively powerful companies, business model-wise, one of those is complementary platforms. And I think you can map out Snowflake not as this fuzzy ecosystem idea, I think you can map it out as three clear platform business models that are all complementary. They support each other, they help each other. The platforms I’ve cited for this are the core business is a data standardization and coordination platform, which is one of my five platform types. You can find that in the concept library. Their other one is an innovation platform, which is more like an operating system, like an Android type model. And then the third one would be a data marketplace. So there’s at least three platform business models that are sitting on top of each other, all strengthening each other. You know, that’s just a really powerful business model. It’s, you know, it’s one thing to compete with a company that has one business, and then you’ve got to build that business to compete with them. But when they have two or three that all link and support each other, the only way you can really compete is you have to replicate all three. That’s just much, much harder, and most companies can’t do that. Linked, complementary platforms is a digital version of linked business models. If you go in the concept library, you’ll see me point to lots of linked businesses like NASCAR. NASCAR is a linked business. I mean, I haven’t looked at this in like 10 years, but last time I looked at it, I mean, basically the company owns the racetracks and it owns the teams, the racing teams. And you need both because if you want to compete with them, well, you have to start a racing team, but where are you going to run your cars? You need the stadiums. You need the tracks. Well, the company, your competitor owns those and they won’t let you on. So yeah, you’ve got the racing car teams, but to compete you also need the stadiums. Or you could do it the other way. We’re gonna build the, not stadiums, but racing tracks, fine, that will be our business. But our main competitor has the state, you know, the racing tracks and the teams. So we can’t get the teams to run on our tracks. So you kind of have to do both. There’s a lot of linked businesses like this. It’s a pretty great barrier to entry for a business. So anyways, those would be sort of the three platforms for Snowflake. I detailed those out in pretty ridiculous specificity for the subscribers, but I’m gonna keep that bit private and just for them. Okay, so that’s the first thing. What is the competitor view? This is about complimentary platforms. Number two, it’s about network effects. And I think there’s at least four. I suspect there’s one or two more that I’m sort of. trying to get my brain around, but I think it’s at least four. That’s pretty powerful. And then the third factor I put within this competitor view is okay, that’s all impressive, but who are their competitors? And here’s the problem. Their competitors are Amazon Web Services, Google Cloud, and Azure, Microsoft Azure. They have three of the most formidable, scary competitors you could think of. So yes, great customer view. Yes, very strong, impressive business model. However, scary competitors. And that’s kind of where I think this company sits right now. How do you beat them? And my working conclusion on all of this right now is this is sort of a balance between first mover advantage and increasing returns to scale. By which I mean Snowflake got there first. They got to the market early. They understood the right question to target and they went after it fast. That’s what they’re doing and they’ve got a great CEO and they’re moving quick. So they’ve got an advantage by the fact that they were first or early mover versus these big giants. However, the big cloud giants are coming and they will come after their business. So there’s a window of time in which this company has to get to sufficient scale that they’re locked in and protected. That’s kind of the race. First mover versus increasing returns to scale. If this company gets to be 20% of the market, and this whole market is very fragmented right now, the whole data warehousing market, it’s all fragmented. If they can get 20%, does that protect them such that when these other companies come in and get five to 10%, there is a competitive advantage based on superior scale that protects them? And that’s when you start talking about network effects. If they get bigger than Azure, Google Play, and AWS, if they stay consistently big enough, bigger, does it make their products superior such that they’re protected? Or are these three companies gonna catch them in the next two years and they’re gonna lose that? So that’s kind of the two factors against each other in my mind. How fast are they moving? How fast are they growing? And before their window of time closes, are they gonna get to sufficient scale that they’re protected against these very scary competitors that are running after them right now? So it’s a bit of a race. And that’s pretty much where I am in my thinking. So the question kinda comes down to growth, which was the last email I sent out about this. And you can tell, I mean, those of you who read it, that I’m struggling with the growth question. It’s so complicated and they have so many use cases they can build out trying to project the growth forward is quite difficult. And ultimately this business is not you put up a website and people sign up, it’s direct sales with teams working with companies. So that usually tends to be slower. So it all comes down to this growth. The growth is gonna determine the evaluation because you know, the price you could argue is very high right now, or it could be a good price depending on what the growth is. So one, it impacts valuation, but it also impacts this sort of key question of how fast are they growing and how big are they gonna get relative to their scary competitors before the window of opportunity closes, sort of window of time closes. So it’s also that kind of aspect. And the framework I put forward was the one I did in a podcast a week ago about core versus adjacency growth, which is a model by Bain and Company, Chris Zuck. If you look at last week’s podcast, that’s kind of why I was struggling with that question. So the growth is kind of the big question. Two other ideas I’m sort of struggling with on this company is the idea of a learning platform. which is my fifth type of platform. I’ve talked about that for Google and Baidu and Jihoo, but I’m still sort of struggling to understand that. Waze is an example of a learning platform. And this idea of a data network effect, which people talk about, but I’m not really convinced exists. So that’s kind of where I am in my thinking right now. I think I’m about 75% of the way there. I feel pretty good about the 75%. But there’s that 25% that I’m beating my head against right now. Ultimately, I consider this just like a new animal. Like we kind of know the savanna, we know the jungle, we know what a lion is, we know what a tiger is, we know what a monkey is, we kind of all know what they are and their strengths and their weaknesses and where they win and where they’re prey and where they’re predators and all of that. This is like just this new animal showed up on the savanna one day and we have no idea, it’s like a whole new thing. You know, it’s like crocodiles we had never seen before and suddenly there’s crocodiles and it’s like, what is that thing? I don’t know. That’s kind of snowflake. It’s a new animal. And then you ask your question, OK, well, how is the crocodile going to do against the lion? We know AWS is a lion. If a lion and a crocodile fight, who wins? I’m not totally sure who wins. I don’t really know who does win that fight. In reality, I think the crocodile does win that fight, but I’m not totally sure. Anyways. And we’ve seen a couple of these. I made the same analogy a month or two ago about WeChat mini programs, that this was a new animal. I think I called it a tiger. Alibaba was a lion. WeChat mini programs was a tiger. What happened, they’re both impressive animals, but what happens when they fight? I don’t know. Turns out the tiger usually wins. This is the same thing. This is a new animal. I think we can understand the moving parts. I think we can understand its strengths. I’m not sure we know what happens when it goes head to head with another big animal, but we’re going to find out. So that’s kind of where I am on this one. If you have any thoughts on sort of how to take apart the growth, I think probably the best approach to growth was the email I sent out, which is like you break the growth into core versus adjacencies, and then you take it apart by use case. I think that’s probably, I don’t like this approach people do where they estimate the entire enterprise B2B spending as the total addressable universe and then they say, well, this company could be 20%. So let’s just do a macro picture and say it could be, I think that’s just made up. I think you have to sort of go bottom up on this one, which means use cases. We know the core use cases. We can look at those and then we can look at relatively close adjacency. and we can sort of project them. I think that’s the way to do this one, but it’s pretty tricky. And I think that’s really what I wanted to cover today. Fascinating company. Absolutely take a look at this thing. I’ve been looking at cloud companies a lot in the last six months, just because I think it’s such a big deal. But, you know, most of them are very big and complicated and you can’t really get good numbers. I like this one, it was a sort of focused approach that was a standalone company. So I think you could get a, you can really get your brain around this one. In a way I don’t, I can’t get my brain around AWS and Google Cloud nearly as, or Alibaba Cloud nearly as easily. So I kind of like this. I mean, it was definitely take a look at it. If you’re a, you know, if you’re an investor, tell your, if you’re doing it yourself or if you have a team that does it, tell them to take a look at this company. If there is a company out there that’s going to become the next Google or the next Facebook in 10 to 15 to 20 years, this could be it. It could be that big. It may not be, but it’s a potential, hey, this is the next Google. That’s a solid maybe on this one. Okay, and the two concepts for today, digital operating basics, complementary platforms, super important concepts. You can find them in the concept library. And that is it for the content for today. Let’s see, as for me, it’s been a great week. Just a nice week buried in thinking and reading, which is kinda when I’m happiest in life. Pleasant weekend, I went out, for those of you who know Bangkok, I went out to Bangkrachow, the sort of green space in the center of Bangkok, which is fantastic. I rode my scooter around, sat there. Such a nice, interesting thing that exists in the middle of the city. It’s basically, for those of you who aren’t familiar, there’s kind of a peninsula or one part of the river that’s totally basically not developed, and it’s just green, and it’s just little paths, and you can buzz around on your scooter, and there’s cafes, but there’s no real development. And you have to take a boat to get there for the most part, but the boat takes off near my place, so I can just sort of zoom down to the pier, hop on the boat with the scooter, which is really funny. you ride your scooter onto the little boat and then they take you across the river and you buzz around. Really pretty fantastic. So that’s kinda my thing to do. But yeah, but apart from that, it’s been a pretty great week. And the book is all getting finished. Oh, one other thing I’ve been doing, which may be of interest, I started a LinkedIn group, which is just gonna be chatting about competitive advantages in digital businesses, which is really what the book is about too. I just put it up the other day, so there’s literally just me. if you’re curious. So it’s not really a group at this point. It’s just me, I suppose. Anyways, we’ll start to maybe get some discussion going there, which I think will be fun. Yep, but that’s basically it. Anyways, I hope everyone is doing well. I hope everyone is maybe wherever you are in the world coming out of the, you know, the lockdown period. We’re definitely coming out of it here, which is fantastic. Oh, recommendation for you. That movie Dune, I saw Dune the other day. Holy cow was that a spectacular visual movie. I mean, I’m an old Dune fanatic. Like I’ve read that book since I was a kid. I’ve read all of the books multiple times. I’m one of the like die hard, I love Dune people. Children of Dune, Dune God Emperor, all of that stuff. But the movies they’ve made about it have always kind of sucked. This one was visually stunning. I had heard this and I went and saw it and I saw it in the biggest screen I could find and it was amazing. I was kind of stunned by it and then I looked at who directed it and it’s the same guy who did Blade Runner 2049. For those of you who have seen that movie, that was the same thing. I remember when I watched that movie and I was like, holy cow, this is visually amazing. Same person. So. If you like the sort of doing stuff and dude go see it but if you’re gonna see it see it in the biggest screen you can find in town Because it was really pretty amazing Uh, anyways, there’s a recommendation. Okay, that’s it for me. Uh, I hope everyone’s doing well and I will talk to you next week Bye

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