5x a data team at Postman, 13 kinds of network effects, Meltano extends beyond ETL; ThDPTh #44
I’ve been on a network effect “binge” lately, working on some company evaluation models that include them as a large part.
One thing I stumbled over was the venture fund NfX, which publishes some great content in that direction.
Read about it below…
I’m Sven, I collect “Data Points” to help understand & shape the future, one powered by data.
If you only have 30 seconds to spare, here is what I would consider actionable insights for investors, data leaders, and data company founders.
Going against the trend and moving towards a more central data team structure can make a huge difference. At Postman, the move towards a more centralized structure made sure to align the data work towards a common goal.
Adapting the data team collaboration mode to different organizational units’ needs is important. Postman works differently with different units. Sometimes, they have distributed data analysts, sometimes a unit needs more help.
In any (data) product, extensibility is the key to creating a business ecosystem around it. But there is not just the obvious way of extending via “plugins”, there is a whole set of things you can extend.
You can even turn your product just into one specific version of a much larger framework. Go look for that, and then see what kinds of extensibilities you really need.
Network effects are everywhere. According to NfX they are responsible for 70% of the value creation in technology since 1994.
If a data company is not leveraging network effects at the core of the business model, it is unclear whether it can survive in the markets. NfX provides a great list a data company can go through and check whether they can utilize some of them.
☀️ What: Prukalpa from Atlan describes how the team at Postman manages to both increase their value derived from data as well as keep on growing 4–5x in one year. Postman is a 5,6 billion USD startup based in India providing an API building & using the platform. From 2020–2021 they grew from a data team of 5 to 25 people. The 25 people are divided into:
- 8 people in the data engineering team
- 13 central data analysts
- temporal embedded data analysts that sit with a team for a period of time
- permanently embedded data analysts.
🐰 My perspective: I really like how they quickly grew a mixture of centralized & decentralized structures without sticking too much to either model. They adapted the approach to fit different organizational units, depending on their needs, not on some higher idea.
I find it very telling that the team even moved from a decentralized to a centralized one, a move that is against current trends, but it seems to be just the right decision for them.
entrepreneurshandbook.co • Share
🔮 What: NFX claims, network effects have been responsible for 70% of the value creation in technology since 1994. They culminate lots of the knowledge on network effects in this “bible”. Sounds like you should seriously think through your business if you’re in technology but do not involve network effects into it.
Go through the list, think about it, Question is not so much whether you can tap it, but whether you can move it into your core!
🐰 My perspective: There are a lot of different types of network effects. If you can put one of them at the core of your business, you’re in for a fun ride! If not, I am not sure you can compete in the data industry in the long run.
There are just too many network effects already in place, of various different types. Companies like DarkTrace get better with each customer they get. Every single open-source project is basically a network effect marketing, sales & brand machine for the company behind them. The data snowflake problem still dominates the industry and leads to the simple fact that the data company with the most connectors also gets the most customers which in turn brings more connectors. So I feel, network effects are all over the place.
What about you, do the tools you use to have these dynamics? Or are there other tools on the rise which you should watch more closely because they do and your tool doesn’t? How about your data company?
🔥 What: Meltano has a few interesting directions in the form of issues to implement on their roadmap. One of them is the idea of “deployment plugins”, e.g. for Kubernetes/helm, etc.
I’m much more interested in the point that they are expanding beyond the sources & targets as a form of extension!
🐰 My perspective: After closely observing the company Automattic, I realized that a winning open-source project will need to expand into three dimensions, not just the one the project itself resides in. WordPress and a lot of other systems did that.
For a data integration tool, having connectors is one thing, but in my framework, that’s just the very first level of an open-source project. The one which will not make it successful. The much more interesting question is, how does one extend other parts of the data integration tool. This is exactly what Meltano is now considering. I hope they soon will realize that there is so much more to customize, the GUI, the data orchestration engine they use, etc. Once they get to that point, they will be able to build a large ecosystem of companies around the project itself.
If you think about WordPress, at the beginning it was just about blogging. But soon they realized that actually writing is the core, so really it can turn into a CMS.
A data integration tool is really just an orchestration/job launch engine together with sources & targets. But an orchestration/job launch engine can be used for so much more than just launching sources & targets! Once we realize that, the data integration tool becomes a whole new thing (I hope).
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