Don’t be afraid of evolving all or part of your finance tech stack over time with something that scales more effectively as your company grows, Teddy Collins, senior director of corporate finance at SeatGeek, said in an Airbase webcast.
“Every two or so years, our entire financial model infrastructure was totally different, totally unrecognizable from a couple of years prior,” said Collins, who joined SeatGeek in 2015 as a financial operations manager and worked his way to head of financial planning and analysis, investor relations, and other functions. “It’s about not being afraid to throw things away and start from scratch or be beholden to the technical debt of your infrastructure.”
The finance function at the company, which provides live-event ticket pricing and trading, was built around a single, 20-megabyte master model Excel file that became obsolete as the company grew.
“It did all these fancy things, and then we had to start sharing, get other people involved, so that turned into multiple files,” said Collins, who started his career in investment banking. “Then we transitioned into using dozens and dozens of Google sheets. That got people involved but also respected confidential information — who should have access to what. That was a great stepping stone to really know what we wanted and design a more advanced system.”
To add the Google sheets to the mix, he and his team created advanced workflow diagrams of every file, every piece of data that moves between spreadsheets, to know if they needed to retain that data and build it into the next system or get rid of it.
“That helped us to really understand what’s necessary or whether there are certain things we can cut out of this process,” he said.
He then transitioned the system to third-party, cloud-based applications that incorporated automation, machine learning and other advanced functionalities.
“When we started, we had it all mapped out and we understood how we wanted to move the data,” he said.
In the first phase of the move, his team replaced all but about 10 of the team’s 60 spreadsheets. “Then we kept chipping away at it,” he said. “We started with the highest ROI things and then went to the lower ROI things. We were at the point where, a couple of months ago, we eliminated the last spreadsheet out there, so now we’re able to go from beginning to end, all within our planning tool and other groups can do their own site analyses, other bottom-up things.”
Collins said they designed the planning tool with a customer-focused mindset to feed information to different operational functions based on what they need out of the data.
“Not everyone wants to learn all these tools that might not be here in a few years,” he said. “We’ve worked with our business systems partners to create automated connections between some spreadsheets and our connected planning tool as well.”
In the early stage of the process, it was important to get the accounting team to redesign the chart of accounts to make sure it supported executives' questions.
“It’s not just your general ledger accounts, but those other dimensions, whether it’s your department structure or other classes you may need, to understand how the business is operating," he said. "[Those might be] business unit cost centers and so on, and you extract that information in an automated way to produce your reporting.”
One-time expenses is an example. “If we’re running a report that strips out one-time expenses, we know we can filter out certain GL accounts,” he said. “Initially, we’d have a spreadsheet, a transactions report, and look at specific journal entries. We’d put a '1' or a 'true' in some spreadsheet column.”
That all changed when they made the transition to the connected system. “You knew you wanted to filter that out,” he said. “You want to write that formula once in a system and have that permeate through your model infrastructure.”
If you’re preparing to make a similar transition like this, Collins said, your team members' skills don’t necessarily have to change that much. From investment banking, he brought modeling skills, and then later developed Structured Query Language (SQL) skills for manipulating data, but beyond that, it’s more about bringing an analytical approach to the task and knowing what to ask.
“It’s just about being curious and just trying to solve problems with that data,” he said. “Over the years at SeatGeek, once I learned the typical data sets that I work with, it was less about manipulating data and more about sketching a report or setting data from one system to the other. The most important thing is learning enough about a topic to be able to talk with someone who really knows a lot about that topic.”