Modern Treasury was founded in 2018 with an ambitious vision – to bring the payments, reconciliation, and financial data that only large banks enjoy to the masses via a suite of powerful APIs. As a product-led company, using data to continuously improve was a priority from day 1; but their tooling was making it difficult for that to happen in practice.
Modern Treasury’s product and engineering teams had constant questions they needed to ask of their data (which is great), but none of the right tooling or infrastructure to answer those questions efficiently (which is not as great). A small, lean data team was stuck focusing on day-to-day tickets, helping engineers answer short term questions instead of focusing on strategic, longer term questions with big picture implications. Think “did we bill this customer last month” and not “which product lines are really moving the needle for us?”
A big part of the problem was Modern Treasury’s less-than-efficient analysis workflow. There were a few scattered reporting dashboards, but most data was pulled ad-hoc, as needed, with SQL queries in a simple IDE. Product and engineering teams would have double-digit tabs of these queries open in their browser just to answer run-of-the-mill daily business questions, with no standard definitions or simple “check the dashboard” option. Explorations started from scratch, which in practice meant they didn’t happen very often.
And when it came to sharing work and results, state of the art was screenshotting table or chart results from the SQL IDE and pasting them into Slack. Collaboration was an afterthought. All in all, far too much time was being spent on pulling data and not enough time on analyzing it. Product and engineering teams simply weren’t generating the insights they needed to improve the product and move the business forward.
Chang Sun runs analytics engineering for Modern Treasury, and was focusing on empowering more data-driven thinking among the EPD teams. His team had experience with notebook-like tools in the past and were looking for something where they could both iteratively explore and empower teams to answer their own questions. They got started with Hex to better understand feature usage across the user base:
“We started with Hex on an exploration into how our customers were using our different products together and how feature usage was trending over time. It started as an exploration and ended up turning into valuable, regular reporting – we wouldn’t have been able to do this, let alone share it, in any other tool.”
With Hex, Chang’s team was able to reorient Modern Treasury around long term thinking and fundamental data exploration. They set up:
Standard definitions for things like customers and billed events
Starting points for exploration, so product and engineering teams could answer their own questions
Basic dashboards for regular reporting instead of recurring SQL queries
Hex’s linear, notebook-oriented UI allowed Chang’s team to investigate and analyze their data without needing to get out of the flow. You can run a query, pipe the output into a dataframe, and run another query or aggregation easily instead of opening a new tab and copying results.
The ability to use both SQL and Python, plus natively visualize data inline, gives Chang’s team a complete workbench for exploring and analyzing their data. Sharing up to date results was as easy as a link, even directly to a cell – no screenshots and stale data here. Over time, Hex shifted from a tool for Modern Treasury’s data team to something that the whole product and engineering team wanted to use. Today, engineers run their own basic explorations – from understanding critical product health to investigations stemming from support tickets – in Hex. They build on top of the standard definitions and starting points that Chang’s team created. Hex has been instrumental in enabling analytical thinking for the whole company, which freed up the data team to focus on fundamental, longer term questions and explorations.
“With our engineers being able to self-serve in Hex, the data team is able to focus on long term, strategic projects. It minimizes the amount of time spent on things that don’t fundamentally move the needle and lets us focus on what matters.”
With product and engineering teams empowered to investigate and understand their own data – plus a data team with the time to focus on strategic investigations – Modern Treasury is able to make more informed, faster decisions across the business.
Watch Modern Treasury walk through this story in our webinar.
Chang Sun, Analytics Engineer
Modern Treasury