Prior to founders Gabi Steele and Leah Weiss were data engineers in the early days of WeWork. They later opened their own consultancy to help clients build data stacks, and saw a persistent consistency in the kind of information their clients needed.
They knew from experience that there were several key issues in building a data stack, starting with data ingestion and storage. But the piece of the puzzle they wanted to attack was about data transformation. That is, taking the data from various business sources and making it useful to industries that could really put that data to work if they figured out how to do it.
“The data transformation part is so crucial. It’s the foundation of everything you want to do with data, and it’s very hard to get it right. But what we’ve noticed is that this translation between business requirements, owned by experts in the business, and data teams who then have to code business logic into SQL can be painful and cause a lot of friction. And that’s exactly the sore point we’re solving,” Weiss told me.
Steele and Weiss made quite a bit of money helping clients solve their data problems, but they realized they could use software to solve the problem and automate much of what they were doing as consultants.
“What Prequl does is we build an interface for business users to specify what they want to measure and how they want to find their metrics. And then we abstract the complex work of transforming that data into our user interface, which is pretty cool,” Steele explained.
However, they don’t stop there. Because of their extensive experience working with companies, they have seen the same questions asked over and over again, which has helped them guide the businessman on the right path to get the answers they need with a set of core metrics for a given business. company problem.
“We believe there is a set of core metrics that we think really need to be standardized, where there are a few permutations of those metrics depending on how exactly your business operates. So that’s the kind of nuance we want to get to. We want to create standardization with enough room for flexibility, so we really speak the language of your company,” said Weiss.
The company launched last year and the two women are working with a small team to build the product, but they are out to solve a fundamental problem they have encountered and they want to solve it with software.
“We didn’t study computer science or data science, and we found our way through learning these tools on the job and had people who gave us opportunities. That’s really what motivates us. So whenever we can, we’ll try to create opportunities for ourselves and for others to make the data space more accessible and inclusive, to feel less like a community run by gatekeepers — and that motivates us,” Weiss said.
The company has raised a $7 million seed round to help realize their vision. The investment was led by Bessemer Venture Partners with the participation of Felicis and a number of industry angels.
