Title image

Case study

Rippling builds self-serve data culture through access and accuracy

Company logo

Rippling is a workforce platform that allows businesses to manage all of their HR & IT — payroll, benefits, computers, apps, and more — in one place.

Data stack

Snowflake

Snowflake

Fivetran

Fivetran

Mozart Data

Mozart Data

Mode

Mode

Tableau

Tableau

Once you build the infrastructure, you can create a self-serve data culture by educating teams on how to ask a good question and answer it themselves. It’s critical to not just rely on the data team to do the work.

Alex Netsch
10x more
employees using data to make decisions
50% decrease
in time-to-insight
30% cost savings
with Mozart Data vs. building a data stack

Challenge

Lack of data infrastructure slowed decision making

When Alex Netsch, Head of Sales Ops and Strategy, joined Rippling, the company did not yet have their data infrastructure set up. Without this infrastructure, even basic reporting tasks, such as finding metrics for board decks and executive reporting, were time-consuming.

Netsch could not get data without working with an engineering team, which took the engineers away from building products. Once he had the data, Netsch would still need to export cuts of data into Excel, create visualizations there, and then paste them into slides. This workflow was inefficient; it was slow to switch back and forth between these tools and constantly make requests of the engineering team, and if the data changed at any point, he would need to start this entire process over again.

Netsch knew that Rippling needed to mature their data organization and build a data stack, but he did not have the bandwidth nor the technical expertise to do it.

Solution

Rippling scaled data infrastructure without a dedicated data team

Rippling did not yet have a dedicated data team, so Netsch was working on data projects as head of the operations and strategy team.

Netsch started by getting their data into a PostgreSQL warehouse and implementing Mode on top for BI reporting and dashboards. With a baseline understanding of SQL, he was able to build dashboards for every revenue-generating team, enabling employees to answer questions for themselves. “We use Mode as the source of truth for general BI - things like how many customers we have, how much ARR, what we closed this month vs. last month, etc,” says Netsch.

The next step was to scale the data infrastructure. Postgres was an adequate temporary solution, but it wouldn’t scale well and seemed like it would break as Rippling increased its volume of data. Netsch wanted to migrate to Snowflake, which he viewed as the top-tier warehousing solution, but he needed support in implementing it. Mozart Data provided an out-of-the-box modern data stack built on Fivetran, which they were already using, and Snowflake. By working with Mozart, they got the data stack they needed at a fraction of the cost of building it themselves and the technical expertise to set it up.

“We knew we should do the Snowflake thing, but it was scary, and we pushed it off even though we knew it was getting harder every day we didn’t do it. Having Mozart there at the time we needed to do this transformation was life-saving.”

Impact

Accessibility and accuracy drive data democracy across revenue teams

Rippling now has a single, company-wide source of truth powered by Mode and Mozart.

A critical metric for sales and marketing teams at Rippling is demos per month. By using Mode to create canonical definitions and Mozart to transform their data, Netsch created the infrastructure to ensure this number is accurate every time. The sales, marketing, and support team members are typically using their system of record, such as Salesforce or ZenDesk. But at a director level or above, most people are using Mode. Says Netsch, “When sales pulls demos and marketing pulls the demos, it’s the same number every time. That is the work behind what you see. The dashboards are really easy if you have built the right infrastructure underneath it.”

Sales, marketing, and customer success teams can now find metrics like this quickly and easily, without going to an engineering team. Because Rippling has clean data and consistent definitions, they can trust the data that they find and combine that with their own domain expertise to make better, data-driven decisions.

Particle pattern

Data teams move faster in Mode