Despite the data-driven culture at Groww, ensuring the availability and accessibility of data across the product and engineering teams was still a huge challenge. Samarth, Ankit, and 50 other users needed access to centralized data almost every day to analyze the important business KPIs such as user behavior, buyer journey, investment graphs, and more. But in order to combine this data from disparate sources to a centralized data warehouse, Kashyap and the team had to manually write multiple custom scripts and queries on Jupyter Notebook and Python.
We were spending almost 20 hours a week on writing and managing these scripts and still we were facing a lot of issues with missing data, inefficient queries, inability to track schema changes, and leverage this data for real-time insights.
In order to present the insights and build dashboards for various stakeholders, analysts at Groww used to take almost 2 days, and also some business-critical reports that should have ideally been available in real-time were prepared only on a monthly basis.
About seven months into dealing with scattered data, manual processes, and fragile infrastructure, Samarth and Ankit realized the need for automating their entire process.
They started looking for an automated data pipeline tool that requires zero maintenance, no continuous monitoring and ensures high-level security and compliance. After evaluating a few automated data pipeline platforms, Samarth’s friend at Meesho recommended Hevo to him.
After trying Hevo for about 2 weeks, we were highly impressed with some of the features like easy setup, intuitive UI, end-to-end data encryption, multiple pre-built integrations, and advanced transformation logic and we knew we had a winner.
Using Hevo, Groww built a well-streamlined data and analytics stack where data from MySQL, Freshdesk, Google Ads, Facebook ads flow via the Hevo Automated Data Pipeline Platform to BigQuery Data Warehouse. Groww uses Google Data Studio and Metabase to build reports on the data made available by Hevo.
What I love the most about Hevo is how it automatically fixes most of the data errors, and when Hevo can't solve the issues, it sends out a notification. Hevo allows me to run transformations like date format conversion, concatenating the values, dimensionality reduction on the fly.
With Hevo, Samarth and Ankit could ensure that business teams are having access to accurate and most recent data available for analysis. Also, they were able to drastically reduce the time that was required for generating reports from a few days to just under 30 minutes.
Building a modern data stack with Hevo helped me not just save a lot of time but it also helped me perform real-time data ingestion that helps our team do the real-time data analysis.
By building a single source of truth, the Groww team could analyze user behavior patterns to create an in-house customer support product. The product intelligently guides new visitors on the platform and addresses the concerns of users by providing personalized answers.
Not just Samarth, Ankit, and Kashyap but the entire product and engineering team at Groww found Hevo as a true savior. With data available in a unified view, they now have deeper insights to develop new products that would have otherwise been impossible.