View profile

Web3 Leaders vs Data Leaders

Three Data Point Thursday
Web3 Leaders vs Data Leaders
By Sven Balnojan  • Issue #74 • View online
I’m Sven, and this is Thoughtful Friday. The email that helps you understand and shape the one thing that will power the future: data. I’m also writing a book about the data mesh part of that.
But unlike the Thursday mails, this is a deep dive into one of my thoughts. It’s rough and my perspective on this will likely change. So for you, I think you should deeply think about this topic this week:
  • Why the data space has more parallels to the web3 space, than the web2 space.
  • Why data leaders need to think in systems, not just products
  • Why data leaders need to grow the pie
  • Why data leaders need to lead communities
  • Why data leaders need to unlearn more
This should be a must-watch for data company founders, data leaders, and VCs in the data space. When I watched this, a bunch of lightbulbs went off. It displays very well how I think the data space is becoming different than other software spaces right now. The data space is becoming increasingly complex and adopts more and more openness (usually in the form of open-source). In some parts, we’re seeing 50-80% of new start-ups betting on open-source. On top of that, the data space is in desperate need of protocol-level solutions to solve the “snowflake problem” that things don’t integrate well together. See the parallels now?
Let us look into the aspects of each individually.
Systems thinking, is one of my favorite skills. I have already written about a few specific systems thinking skills I find very important in the data space, first and foremost mechanism design
For web3 founders, this skill is essential because things may be harder to change, which is in the nature of the underlying technology: blockchain. However, several other influences make systems thinking equally important in the data space. These are:  operating in a vastly complex environment with thousands of data sources, thousands of use cases and data consuming technologies, and thousands of number crunching technologies in-between. Systems thinking simply means to take this seriously, taking the high-level approach and seeing the whole thing instead of just one small moving part which won’t be there anyways in 6 months.
Business example - Notebook Start-Up Space: I like the fact that new start-ups are launching their new notebook thingy after notebook thingy. There’s hyperquery, (which autocorrected on me again!) and, and many more I don’t know. Databricks runs millions of notebooks each day, and every cloud provider is enlarging his notebook offering. 
IMHO, the systems thinking approach would start in this space with the question of value; How do these solutions help to deliver more value out of data for the company? How will they do so in 5-10 years when the data space is totally transformed? 
Right now, it seems to me like hyperquery and are focused on providing a better tool, to solve small problems, while is actually trying to tackle a bigger issue. That’s systems thinking vs. problem thinking. 
I know the default mode is to just use problem thinking, use a problem you’re seeing, solve it for yourself, find a product /market fit, scale it up, and enjoy. Systems thinking however is the part where we are not able to do that, where we need to have a broader vision.
I think the data space is at that place right now where systems thinking is more important.
Next up, Community Leadership. I feel the importance of this skill is very easy to explain with an example. 
Business Example: Airbyte went from zero to a billion-dollar evaluation, thousands of GitHub stars, and explosive growth of their community within under 2 years by being a community leader. They were community leaders from day one. That’s how you go for explosive growth in such a complex space, without a large sales or marketing team, even if they turn your back on open-source per se.
Even though I talk about open source a lot, I like to highlight here, that the key attribute of companies like Airbyte or Meltano is not their open-source usage, but their openness per se. Open source is just one tool in the open tool kit. 
Being open means having people who co-develop your product in a sense. It produces very direct feedback which Airbyte utilized from day one with doing a soft launch after 2 months of existence. In that environment, you need to have skills far beyond the usual product skills which are much more on the side of community leadership.
Next, Pie Maker: The data amount is growing exponentially, the goal of the data company should not be to take a shrinking part from another company but to focus primarily on growing the whole pie with this growing data amount. Open-source plays a crucial role here. 
Business example - Tracking space: The company Segment open-sourced their tracking library analytics.js but basically abandoned it later on for the sake of going closed (not just in the open-source sense). 
That’s a great example of doing the opposite of growing the pie. The library is still at the heart of a few other companies, but it could’ve sparked a complete revamp of the tracking industry, growing the pie for everyone. Segment chose to not grow the pie though. Now they have to code every single integration into these 1000x1000x1000 systems themselves which simply is not gonna work in the long run.
Many companies in the data space are more on the side of Pie Makers. Companies like Meltano, Datakitchen, or Dbtlabs are big on promoting data best practices in general and thus grow the pie for everyone, not just themselves. 
The Unlearning skill. Web3 founders are great at looking at “common knowledge” and quickly dissecting what is not relevant anymore, what is built on Web 2 foundations that aren’t true anymore.
Example: I for instance believe it is essential for data companies to have a “data vision”. It’s been the success recipe for dbtlabs, datakitchen, will be one for Monte Carlo, and possibly hex, if they can get it going or Meltano if they nail this DataOps OS thingy. 
Yet, most companies in that space are missing this. Network effects and openness are also key concepts applying to the data space. But a lot of the other parts of business seem to be in serious turmoil when it comes to the data space, customer interaction, defining value, architectures valid in the past, best practices of working in general, organizational structures, and the like. 
If you want to watch the original video that this rant is based on taking a look here:
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.

In order to unsubscribe, click here.
If you were forwarded this newsletter and you like it, you can subscribe here.
Powered by Revue