About Pawp: Pawp’s mission is to reduce the number of unnecessary vet visits and save pet parents thousands of dollars a year. Pawp is an app-based, subscription service that includes 24/7, unlimited access to real-time support from licensed veterinarian professionals and access to an annual stipend towards emergency costs. By adapting human telehealth to solve pet care-specific problems, Pawp is leading the pack as the first and only comprehensive pet telehealth platform with embedded financial protection.
The Initial Data Challenge:
In 2021 Pawp raised their Series A and hired their first data person, Joe Zein, “JZ,” to build out their data infrastructure from zero to one. JZ’s background was in finance and he joined Pawp to tackle all things data and strategy; his position is two-fold with insight / BI (Strategy) and product / engineering (Data Science). Up until the point JZ joined, the lean, 10 person Pawp team was running a few queries in Mode against the prod database but did not have a full data stack stood up.
JZ immediately jumped in and started the process to build out a ‘Modern Data Stack.’ With their direct to consumer business model and recent Series A raise, a flood of questions were coming in from the team around customer behavior, retention and other key metrics.
After researching the market and getting set up on dbt, JZ started the search for a new business intelligence tool. He was looking for a product that had strong governance capabilities and ease of use around data exploration.
As a dbt user, he evaluated six other business intelligence tools including LightDash and Looker, among other tools. Ultimately he felt that LightDash was too much work on top of the time and effort he had already invested in dbt. Similarly, he ruled out Looker due to the heavyweight implementation process and ongoing time commitment he expected for maintenance.
When JZ first used Hashboard he was impressed out of the gate by the attribute tray, which allowed him to easily profile attributes, overall ‘clean’ UI as well as the snappiness in the data explorer. He was actually convinced that the Hashboard team was bumping up his compute on the backend during his trial 😂 (we weren’t!) After a POC and working closely with the Hashboard team on data modeling and product feedback, the Pawp team was officially on board.
Early Benefits:
Early on JZ felt that Hashboard broke the cycle of writing SQL and building charts for every insight, reinforcing the idea of a metrics store. His workflows dramatically improved because he was able to easily debug, even with a large number of metrics in place. Having the flexibility to manage his project via the UI or through code was a huge win.
“Part of my thinking early on was that I knew I eventually wanted to migrate over into a more governed, code-controlled environment. I thought this was the perfect mix of both worlds, where I could still do things in the UI right now, but when I was ready, set up my resources in code and integrate with the semantic layer.” -JZ, Data Founding Engineer
As the Pawp team grew quickly, the focus on data increased with more teams getting curious and new stakeholders asking questions. Hashboard’s ability to build and manage a metric layer from the UI provided the benefits of a governed model and provided guardrails for the Pawp team to explore data. Key stakeholders who were less technical didn’t get tripped up with having to write SQL or dealing with out of sync spreadsheets; they used the governed models JZ set up as a jumping off point to explore data and discover insights.
Empowering the Pawp team via Hashboard:
JZ leveraged aliases, descriptions and intentionally only included what was necessary for his end users in the governed data models he built. This work provided a solid framework for the team to understand Pawp’s essential business concepts. From there, Pawp team members typically explore data independently in Hashboard and use the ‘Share Exploration’ links to collaborate and confirm with JZ and other team members before finalizing conclusions.
This workflow has fostered an impactful loop of learning at Pawp and has been a great tool for different types of users to grow more comfortable with data analysis and creating data visualizations. Every single employee at Pawp HQ uses Hashboard! This is a huge win for Pawp, as they are truly operating as a data driven company and a testament to the infrastructure that JZ built.
“I’m not dealing with problems anymore with people having multiple different charts, say about ARR, with different numbers causing confusion. This was a big problem I had in Mode because people didn’t realize they were writing the same query in different places. Hashboard just made my life and the data so much easier and centralized… and now it flows easily down to the team.” -JZ, Data Founding Engineer
Earlier this year JZ documented some examples of how the Pawp team is making data-driven decisions on his blog. Highlights include high value reports for growth, ops and the founding team to self-serve and do easy explorations.
“Hashboard enabled our product team to find answers and uncover insights without requiring the intervention of a data analyst in every instance. The ability to refine queries on the fly and adapt them to a given problem or question is extremely helpful. We’re excited to continue executing on our product vision while guiding our instincts with the insights uncovered using Hashboard.” -Roberto Salem, Head of Growth
What’s Next?
Over the past two years, JZ and team have built a robust data infrastructure that supports Pawp’s operations and decision-making processes. Their data infrastructure helps the entire organization gain insights into customer behavior, improve product offerings, and make grounded decisions across the company.
JZ plans to continue to scale data internally as the Pawp team grows, and is especially optimistic about bringing Hashboard on that growth journey for deeper collaboration and more advanced workflows.
“I think Hashboard is becoming more and more of a comprehensive data platform. Which means I will have less work to do in dbt and more to do in Hashboard. Obviously it’s more enjoyable and easier for me to do it in Hashboard because I’m able to join and configure things on the fly without having to do a lot of planning ahead of time.” -JZ, Data Founding Engineer