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The Product Data Insights team (formerly known as "Product Analysis") was created in October 2020 to provide dedicated, specialized analytics resources for the GitLab Product division and to support product data-related analysis. In June 2024, the team expanded its scope to support all of R&D (including Engineering). The team consists of product analysts, and reports to the VP, Product Management. The team's primary customers are Product and Engineering Managers across all sections, stages, and groups.
In addition to working with the Product and Engineering organizations, Product Data Insights works closely with the Customer Product Adoption (CPA) Pod, enabling collaboration between the product analysts and analytics engineers on the Enterprise Data team. Product Data Insights is also a member of the Functional Analytics Center of Excellence (FACE), a group of functional analytics teams across GitLab.
Simply put, the Product Data Insights team is charged with supporting product usage data-related needs across GitLab. While the scope is quite broad, the team is organized to maximize support of the Product and Engineering divisions, while protecting time for cross-functional initiatives. The Product Data Insights team delivers actionable insights that help the Product division make responsible, data-driven decisions in feature development, prioritization, and improving the user experience. In addition, they help the Engineering division understand team productivity. Part of Product Data Insights' role is also to enable greater self-service of product-related data and follow GitLab's handbook-first approach to build a strong remote culture.
You can read about the Product Data Insights team's issue intake, prioritization, and team processes on their handbook page.
To maximize the team's ability to support a large R&D organization, Product Data Insights is structured so that a single analyst supports a section (or sections) and/or the Engineering division. This gives analysts the opportunity to build subject matter expertise in an area of the product and the data associated with it. In addition, it provides both analysts and stakeholders (PMs or EMs) with a consistent counterpart for their data-related needs. The analyst is intended to be an embedded member of the Product team to encourage closer collaboration and increase the impact of analysis on the product.
As a functional analytics team, Product Data Insights is reliant on other teams within the Data Program to provide high-quality, easily-accessible data. As such, the team collaborates closely with the Enterprise Data team and Analytics Instrumentation group. Here is a quick summary of the responsibilities of each of these 3 teams:
| Team Name | DRI | Goal | Example Tasks |
|---|---|---|---|
| Product Data Insights | Carolyn Braza: Senior Manager, Product Data Insights | Perform analysis to unlock product insights and champion data-driven product decisions; Build and maintain meaningful Engineering Productivity metrics | Perform ad hoc product analysis; create and maintain standardized Product or Engineering metric dashboards; consult on data collection, instrumentation, and modeling |
| Analytics Instrumentation | Tanuja Jayarama Raju: Senior Product Manager | Define and improve capabilities of product data set | Build reliable and scalable tooling for product data instrumentation and collection; ensure our product data collection adheres to our commitment to user privacy; empower team members to create new instrumentation, charts, and dashboards in a self-service fashion |
| Data Team | Israel Weeks: Director, Data and Analytics | Build and maintain product data models and infrastructure to support data analysis | Create repeatable and reliable product data sets, models, related data pipelines/processes; adhere to Trusted Data standards |