When the company Airbyte soft-launched its software for moving data, it had two connectors. The two connectors (one of them was S3) connect to a data source and pull the data into some other place. A year later, they had over 200 connectors, which is more than most of the decade-old competitors in the space.
Clearly, they focused on growing the number of connectors, which seems like growing technology adoptions. But if you take a look at the datacisions cycle, they were applying all of their energy on just one step, going from data creation => to data storage.
They realized that there is a huge market segment where this is the bottleneck in the datacisions cycle. These are companies that have working mechanisms on the rest of the datacisions cycles, companies that are already to a certain degree data-driven. Their sole bottleneck is to move stuff around quickly, then combining things together & making data-based decisions isn’t a problem for them.
On the other hand, if you look at the company Meltano, the picture looks different. The company started out focusing on connectors and moving data, but today the roadmap is full of integrations to “great expectations” and in general “plugins”. Meltano chose to focus on a slightly different market segment, one where the problem is not connecting to the source but also in extracting information & insights from the data. One where it is necessary to easily control the quality of the data.
I think it’s easy to conflate the two approaches. But in truth, they focus on very different market segments in terms of the company profile & their state of the datacisions cycle.
The problem? Looking through the technology lens always puts value aside. Even if you know your current customer base really wants the integration to Snowflake, the question remains, does it help their companies make better decisions? (It probably does, but sometimes it doesn’t!)
The solution? Start with the datacisions cycle first, think about every technology choice you have what does it do to decisions? For whom does it enable quicker decisions?
Another Example: The company Atlan made a very conscious decision in this direction to focus on companies using Snowflake in the beginning. That cuts off a huge part of the market segment, but it also accesses one, which has certain characteristics alongside the datacisions cycle. These are companies willing to pay for speeding up analysis which means they are very likely already deriving lots of analyses from their data. But that means, they are also a good pick for a data collaboration space, which thrives on loads of analyses, not just data.