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Thoughtful Friday #2: Where is the First True Data Platform?

Three Data Point Thursday
Thoughtful Friday #2: Where is the First True Data Platform?
By Sven Balnojan  • Issue #46 • View online
Thoughtful Fridays are intended to get you to think about something. They are usually short, and possibly incomplete, and appear infrequently. 

That's a platform...
That's a platform...
Tobi Lütke built a hugely successful platform with a market cap of 160 billion dollars. Reid Hoffman, argues in a “Masters of Scale” Episode that most companies should look into building platforms. 
The reasoning behind this? Let’s explore them. Oh wait, and let’s also see whether the data space is actually building a platform or not!
Svens Actionable Thoughts
If you only have 30 seconds to spare, here is what I would consider actionable insights for investors, data leaders, and data company founders. 
- The data space is still lacking a big platform. At least in the external sense, Reid means, the one that harnesses network effects for the positive.
- Tabular & GoodData are possible candidates. Both companies seem to focus on building a platform in the future.
- External platforms are a long shot. Shopify took roughly 10 years to reach a point where they felt like a platform.
- Build a platform by ignoring most short-term revenues. Shopify built its platform by ignoring short-term profits for almost 10 years. You’ll have to leave all of the money on the table for the participants of the platform to actually reach the point of a true platform.
True Data Platform?
 I think Reid has two things in mind that make platforms such great (and terrible!) things:
1. A platform runs on network effects. The more users, the even more value for each added user. Once you go past a certain point, the platform will become the only thing out there.
2. Our Platforms run on already existing “networks”. They run on the internet, helping already existing relationships speed up. The key point is, you do not have to put down any wires to be able to launch a digital platform, you can launch platforms pretty easily.  
So where are the platforms in the data space? Lots of companies associated themselves with “platforms”…
But Wait!
If you noticed the two “benefits” above, Reid actually only makes the argument that certain kinds of platforms will be really successful.
The company databricks for instance brands itself as “The Modern Data Cloud Platform”. And it has a platform. One that is targeted at the insides of one company. Turns out, that platform does not benefit from 1, there might be network effects inside the company, but that doesn’t help us grow databricks. It does benefit from 2, databricks has an easy time connecting data from source to end because the internet is already there though.
This since the platform is only benefitting from 2, it’s probably not the one we are looking for. So is there a data platform that also benefits from 1? A completely externally facing one?
FWIW: Of course databricks is moving in the direction of enabling externally facing connections through databricks with their integration of both sources & endpoints, but this is not the main purpose of the platform and as such, they are simply not able to benefit from the network effects (or able to build a proper platform, Shopify spent 10 years forfeiting profits to build the platform).
So where is it? Hello Tabular & GoodData
Turns out, at least what Martin Casado from a16z describes for the company Tabular is in my eyes exactly what a true external platform looks like: 
“We think of Tabular, informally, as a “headless” data warehouse. Where companies like Databricks and Snowflake pioneered the technical separation of storage and compute, we believe Tabular will complete the process by also separating these resources at the vendor level. This means customers will be able to mix-and-match the best data management and data processing systems to best meet their needs.”
Mix-and-match or in other words, combine different parts of the data world together, and focus only on that. Most interestingly this will be a sidestep for the company that currently focuses on Apache Iceberg, but it is what I believe to be a huge value driver once they tap into the network effects mentioned above. 
I also feel like a16z is excited about the external platform vision, tapping the huge network effects market, whereas the company Tabular still is speaking in terms of the internal platform idea. So, how do we go from a service/product business model to a platform business model?
Another company that’s going in this direction, albeit more slowly is the company GoodData. At least their concept of a “headless BI” is a move into modularization which again would allow them to build a platform using it. 
The Hard Part
Bill Gates has a famous quote about external platforms:
“When that 15B happened a few months after Facebook Platform and Gates said something along the lines of, “That’s a crock of shit. This isn’t a platform. A platform is when the economic value of everybody that uses it, exceeds the value of the company that creates it. Then it’s a platform.” (Strachery, The Bill Gates Line)
Do you want to know how long it took Shopify to hit that spot? 10 years according to Tobi. I like the lessons Tobi draws from his success. Platforms are hard to build. You’ll have to leave all the money on the table in order to grow the platform further and further and further. Leave the money for the users of the platform. 
You’ll also have to start with not building a platform, with building the part that is easy to build, the 80%, or in the data world more like the 50%, that everyone needs and that is easy to build. The mix-and-match of the simplest choices like the most popular transformation tool, the most popular 2 databases, the most popular reporting tool, the most popular 2-3 ingestion tools, etc.
But that will not make a platform, only after building out this part will you be able to get to work on building a platform to satisfy the other 50%.
Maybe this explains why there are no external data platforms out there. Or maybe I haven’t looked close enough. If you have more insights, please share them with me! I’d love to discuss this more!
And of course, leave feedback if you have a strong opinion about the newsletter! So? 
Did you enjoy this issue?
Sven Balnojan

Data; Business Intelligence; Machine Learning, Artificial Intelligence; Everything about what powers our future.

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