We spoke with Dharmesh Patel, Vice President of Analytics and Business Operations at Druva, and Tom Coyle, Director of Business Analytics. Druva is a leading cloud data protection and management company, offering innovative solutions for data backup, disaster recovery, and archiving. With a focus on simplicity, scalability, and security, Druva provides organizations with the tools they need to safeguard their critical data assets in an increasingly complex digital landscape.
Dharmesh brings a wealth of experience to Druva, having worked in various roles across late-stage startups such as Uber, Cafe Press, and Presence Learning, as well as large brands like Adobe and Schwab. Tom plays a pivotal role in leveraging data-driven insights to optimize business processes and drive growth.
Tom: Being a company that protects a bunch of data, we generate a lot of data around these customers. We were finding that it was really hard for Looker to process all that data and serve it up in one place. There's nothing worse than trying to get an answer to a question and not having the data to answer that. We did have some challenges with Looker performance.
There’s nothing worse than trying to get an answer to a question and not having the data to answer it.
Dharmesh: Before Sigma, our primary use cases for Looker were, sort of static “what happened” reporting. But what was missing was the downstream — product and customer success, as well as our product usage telemetry. The whole connection, in terms of customer 360, was just missing.
From an end-user perspective, and business stakeholder perspective, Looker is a little bit intimidating. It's not the easiest to pick up in terms of the datasets on some of the filters you want to create for reports. With performance - in terms of query times, dashboards and report upload times, there was slow performance, unplanned outages, and it would crash randomly here and there.
It really turned off a lot of our business users and drove a little bit of lower engagement that we wanted in terms of our daily active users.
Dharmesh: Some of the problems the older BI tool Looker had were definitely on the back end: The data quality, the lack of connection from our product sets to build a holistic customer 360, were all definitely pain points. We didn't have the full view of our business.
Sigma just works. The data is there, and people know where to go to get the information that they need.
Tom: We were able to get off Looker in what I would call record time – like under a quarter – and migrate all of our key reports. It was really important to us that we deliver that same experience that teams had in Looker, but then try to improve that in Sigma. Whether that meant faster query times, more filters to work with, or more insights generated.
We're now able to generate predictions about what customers are going to do, not just what happened.
When we had Looker, it was mainly just go-to-market, sales, and marketing. We then tested out Sigma, rolling out to customer success and product teams. They loved it: both the flexibility, engaging with the deeper product sets and customer 360 views. Then we slowly implemented the rest of the teams: sales, marketing and finance. Now, just about every dataset is in Sigma and Snowflake.
Tom: I was a little bit of a skeptic first, honestly. But after playing around with Sigma and seeing everything it could do and the pace of feature development, such as input tables and live editing, I saw there were a lot of things that we could incorporate that Looker couldn't do. And it integrated really nicely with Snowflake and dbt.
Sigma just works. The data is there, and people know where to go to get the information that they need. By going to Sigma, we were able to get better performance by having that integration with Snowflake, and then access all that data from our product and put it into a customer story about what exactly was happening.
Dharmesh: Input tables is a Sigma feature that allows for scenario modeling, forecasting, and changing data on the fly. So those are also some things that other tools didn't have.
The future of data products really is trying to bring data into workflows.
Tom: Sigma enabled us to build a more cohesive customer story and picture. We're able to generate predictions about what customers are going to do, not just what happened within these accounts.
Sigma also enabled us to move faster because we could decouple the analytics engineering work that we were doing in Snowflake and dbt, with the visualization layer of what we were bringing to the business.
Tom: Before we started deploying Sigma, Looker had about 100-150 weekly active users within Druva. Today we probably have well over 300 or 400 weekly active users interacting with Sigma data products in different systems.
Beforehand, we were really struggling with our financial close. It was taking us up to two weeks to close out the books each month, which was leading the finance team to do less analysis on where we needed resources. With Sigma, we were able to automate a lot of our ARR and NRR reporting like renewals, and cut down that close time to three or four business days.
Dharmesh: The investment we made in Sigma, both in terms of the tool implementation, but more importantly, the data sets modeling, it's been tremendous. We get compliments from our CEO and CFO all the time that they really believe we're a data-driven company.
Tom: Plus, a lot of the things that we suggest or that we ask for, we'll see in the product a month or two later, which has been pretty awesome to see. The future of data products really is trying to bring data into workflows, in my opinion.
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