“Used by over 300 people”
This is part of a series of interviews on how companies are building data products. In these interviews, we’re sharing how data teams use data, with a deep dive into a data product at the company. We also cover tech stacks, best practices and other lessons learned.
Stephen Bronstein leads the Data Team at Fuze. It’s a “skinny team” of 3 people that supports all of Fuze’s data needs.
Over the course of the past three years, Stephen has led the team through warehouse transitions and performance tuning, adoption of new data sources, regular surges of new data and use cases, and the on-boarding of hundreds of new data-users. “People care a lot about having the right data at the right time. It’s crucial to drive their work forward,” says Bronstein.
Table of Contents
Who is the end-user of this data product?
Fuze is a cloud-based communications and collaboration platform provider. The Fuze platform unifies voice, video, messaging, and conferencing services on a single, award-winning cloud platform,and delivers intelligent, mobile-ready apps to customers.
As Fuze has grown its customer base and employee count, data has become a mission-critical component. More than 300 people query the data warehouse on a constant basis across Fuze’s 19 global office locations.
Departments include Finance, Sales, Product, and Customer Support.
What business problem does this data product solve?
Each day critical business functions query the data warehouse:
- Finance investigates bookings that have not been activated yet.
- Product uses data to understand platform adoption and arm customer success teams with information for client conversations.
- Sales closes its monthly books by distinguishing commissionable deals from self-service sign-ups.
- Customer Support investigates support tickets in near real-time, troubleshooting issues by calling up data on an account and product usage they can’t get anywhere else.
What is the tech-stack used?
A central Amazon Redshift data warehouse combines data from a growing number of sources and events. The toolchain around Amazon Redshift includes
- ETLeap for data integration
- Dell Boomi for data ingestion and ETL pipelines.
- Looker for business intelligence and dashboards
Data modeling is bespoke, and Fuze runs large-scale data transformations within Redshift in SQL.