DataSnipper builds agentic automation software used by over 600,000 audit and finance professionals in over 175 countries, serving the likes of the Big Four accounting firms, Fortune 500 companies, government agencies, and global enterprises. The company is growing fast, and last year, its leadership decided the data infrastructure needed to keep pace.
Zack Porach, Senior Product Manager of the Data Platform team, joined DataSnipper last year with a specific mission: build a centralized data platform from scratch. He relocated from Ohio to Amsterdam for the role.
“When I was brought over, the team didn’t really exist yet,” Zack explains. “I was brought over to start a new team — still growing.” Working alongside him is Nicole Liao, Product Analyst, who has spent two years embedded in DataSnipper’s data team. Together, they sit within the product engineering org, reporting to the VP of Product Engineering.
Before they could build anything new, Zack and Nicole had to understand the full scope of what they were inheriting. Teams across DataSnipper were making decisions using Power BI, Amplitude, Mixpanel, a customer health tool called Vitally, and HubSpot reporting. Depending on which part of the organization you sat in, you used a different tool with a different calculation for the same metric.
Zack saw the pattern immediately. “Siloed data, inconsistent data, manual ad hoc reporting — all of these things were used to make decisions,” he says. There was no centralized definition for something as foundational as “active users.” Different teams had set up their own formulas in their own tools, and the numbers didn’t match.
Zack Porach
Senior Product Manager, DataSnipper
For Nicole, the daily reality was a constant loop of cross-checking. “I used to just check multiple sources to ensure it’s correct,” she says. “That’s it — just me checking it.” Hours that should have been spent on strategic analysis were going toward validating whether a number was even right.
The fragmentation didn’t just slow the team down. It eroded trust. When different tools produced different numbers, stakeholders started questioning the data itself, and every conversation became about the data rather than what the data was telling you.
DataSnipper’s leadership recognized the problem and started investing. The first major decision was to move to Snowflake as the company’s centralized data warehouse. With that foundation in place, the team began building proper data models using dbt, bringing structure and governance to what had previously been a free-for-all.
That investment in Snowflake turned out to unlock something bigger. Optimizely Analytics (formerly NetSpring) connects directly to Snowflake — reading from the warehouse without duplicating or moving data. For a team that had spent years managing a patchwork of opinionated tools, each storing and processing data in its own way, this was a turning point.
“Mixpanel does its own thing, same with Amplitude, same with HubSpot — they’re all really opinionated about how things should be,” Zack explains. The appeal of Optimizely Analytics was the opposite: it lets the data team own the modeling while the platform handles visualization and exploration.
Zack Porach
Senior Product Manager, DataSnipper
Nicole felt the difference immediately on the technical side. “There is no connection issue between Snowflake and Optimizely Analytics,” she says. “This saved quite some time.” No more wrestling with data source refreshes. No more broken connections.
The ultimate goal was easier self-service. Power BI required too much specialized knowledge for the people who actually needed the data. Zack puts it simply: “The ultimate goal was easier self-service, and Optimizely Analytics was picked because it was easier than Power BI.”
With the platform in place, Zack and Nicole’s team shifted from fixing infrastructure to building things that drive the business forward. The first major project was one that connected product analytics directly to revenue.
Nicole developed a Customer Success dashboard designed to do something the team had never been able to do before: show, in concrete terms, how much value DataSnipper delivers to each individual customer.
The concept is elegant. For every key product event within DataSnipper’s platform, Nicole and the team defined a standardized “time saved” metric, measured in seconds. Those time savings are then aggregated at the customer level, giving a clear picture of total efficiency gained. The dashboard also calculates ROI percentage — a single number that tells a Customer Success Manager exactly what to say in a quarterly business review.
Nicole Liao
Product Analyst, DataSnipper
Before Optimizely Analytics, this kind of dashboard wasn’t feasible. The data existed in fragments — usage events in one system, customer data in another, sales information somewhere else. Connecting product usage to business outcomes required stitching sources together manually, with no governance and no guarantee the numbers were right. Now, because everything flows through the centralized warehouse and into Optimizely Analytics, Nicole can blend usage data with business metrics in a single view.
Zack has watched the impact firsthand. “Customer Success — the usage we’ve seen has been growing,” he says. “They’re using it every month. This is the most successful dashboard we built for another team.” The conversations have shifted from “are our customers getting value?” to “how much more value could they unlock?”
Before Optimizely Analytics, every data question landed on Nicole’s desk. She was the bottleneck — not because she wanted to be, but because the tools demanded it. That dynamic has changed.
“It’s really cool if you can set up a platform and let people just do things with it. Optimizely Analytics is good for that,” Zack says.
Nicole Liao
Product Analyst, DataSnipper
Three engineers and one product manager have already created their own analyses and dashboards without needing documentation or hand-holding. They duplicate an existing dashboard, adjust it, and run their own deep dives.
The customizability of Optimizely Analytics has been key to making self-service stick. “The way you’re able to add descriptions all along the way and customize and add more information than just throw a chart at someone — that’s been really helpful at building understanding,” Zack says. People aren’t just looking at charts. They’re learning from them.
For Nicole, the transformation goes deeper than time savings. It’s about what she can do now that simply wasn’t feasible before.
Nicole Liao
Product Analyst, DataSnipper
These aren’t marginal improvements. Retention analysis tells DataSnipper which users come back and which drop off. Cohort analysis shows how different groups of users behave over time. Funnel analysis reveals where users get stuck or abandon a workflow. And answering questions like “how many users did this event but didn’t do that event?” — the kind of product insight that shapes roadmap decisions — is now straightforward.
When everyone references the same source of truth, there are no more debates about why one tool shows one number and another shows something different.
“Having it so people can just trust the data, versus debating whether or not why this number looks this way versus this way — that part sucks. I hate that part. But being like, ‘hey, we set this up, we have this data model, you can just use it’ — that’s awesome. I like that," Zack clearly stating his feelings.
With five product teams already running their own dashboards and Customer Success using analytics to drive commercial conversations, the playbook is proven. Now the focus is on extending it further. Finance data has already been integrated. Go-to-market teams are next. Solutions engineering is starting to receive product usage insights earlier in the sales funnel — giving them data to help them do their job better, as Zack puts it, “earlier in the funnel.”
For Nicole, the ambition is about depth. She wants to keep pushing into the advanced product analytics capabilities that Optimizely Analytics makes possible — uncovering patterns in how DataSnipper’s 600,000+ users engage with the platform, and turning those patterns into product decisions.
For Zack, it’s about reach. Every team at DataSnipper should be able to see the data they need, trust it, and act on it — without waiting for a data team to pull it for them.
In an industry built on trust and precision, DataSnipper is applying those same principles to how it understands its own product and serves its customers.
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