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Our vision is to make GitLab the definitive platform for understanding software delivery performance and AI-powered development in the modern era. The Analytics Section closes the DevSecOps loop by providing comprehensive insights into how teams build, ship, and improve software—from SDLC metrics and Value Stream Analytics to AI usage intelligence and developer productivity.
We are uniquely positioned at the intersection of three critical industry trends:
This positions us to compete directly with GitHub Copilot's usage analytics, the Atlassian DX platform acquisition, and standalone engineering intelligence platforms. Our competitive advantage lies in native data ownership, real-time processing through the Data Insights Platform, and seamless integration with GitLab's complete SDLC data.
We will deliver value by:
When talking with users, we consistently hear:
The Analytics Section is comprised of a single Stage: the Analytics stage. This stage has three groups:
Platform Insights focuses on enabling users to analyze and visualize data to uncover insights about software delivery and platform usage.
Categories:
Key Initiatives:
The Optimize group owns all DevSecOps reporting and metrics across GitLab, focusing on Value Stream Analytics and helping teams identify and eliminate waste in their software delivery process.
Categories:
Key Reports & Capabilities:
Strategic Direction:
Analytics Instrumentation focuses on data collection, processing, and unification efforts.
Categories:
Key Responsibilities:
Current Focus:
The Analytics section works closely with other product groups to:
Multiple groups and teams within GitLab produce and consume analytics data, currently through various approaches. Our long-term vision is to establish a Single Source of Truth for:
This vision is being realized through:
The primary personas we address, in priority order:
Sasha - Software Developer - We address Sasha in the analytics context, focusing on adding instrumentation, debugging data collection, and supporting analytics efforts for their team.
Parker - Product Manager - Parker needs to understand product usage, make data-driven decisions, and report on outcomes.
Devon - DevOps Engineer - Devon needs visibility into deployment patterns, DORA metrics, and infrastructure performance.
Cameron - Compliance Manager - Cameron needs audit trails, compliance reporting, and governance visibility.
Both internal GitLab team members and external customers share these personas and use cases. We dogfood extensively while remaining mindful that we must validate with external users, adhering to our you're not the customer product principle.
We have the right to win in this Section because:
Native Data Advantage - GitLab uniquely owns the complete SDLC dataset—source code, issues, CI/CD events, deployments, security scans, and AI interactions. No standalone analytics tool has this comprehensive context, enabling correlations and insights impossible elsewhere.
AI-Era Timing - The 2025 DORA Report confirms AI is amplifying both strengths and dysfunctions in development teams. Organizations need new metrics and visibility into AI usage. We're positioned to deliver this at the moment of maximum market need.
Platform Engineering Momentum - Platform engineering is the dominant DevOps trend, and data observability is a core platform capability. We can embed analytics directly into the platform teams are building, not as a separate tool.
Real-Time Processing Capability - Our Data Insights Platform built on ClickHouse delivers sub-second query performance at scale, enabling real-time dashboards and immediate feedback loops that batch-processing competitors cannot match.
Developer Trust and Distribution - Developers trust GitLab with their code. Extending that trust to usage analytics, combined with our bottom-up adoption model, creates a natural expansion path without displacement.
Competitive Gaps - GitHub's Copilot metrics are limited to AI usage. Atlassian's DX acquisition lacks integrated SDLC data. We can deliver both AI intelligence and complete software delivery insights in one platform.
Single DevOps Platform - Our integrated platform enables unique technical capabilities: correlating code changes with deployment outcomes, linking security findings to fix times, measuring AI impact on actual delivery metrics, not just code generation.
We will pursue this opportunity with the following principles:
Focus on Developers - Developers are key to bottom-up adoption and our dual flywheel strategy.
Works by Default - Basic SDLC metrics and dashboards should be available out-of-the-box in every GitLab instance.
Start with the problem, not the solution - We will identify real user needs before building. The temptation to collect all possible metrics must be resisted unless we understand who needs it and why.
Dogfood Everything - We must be our own first customer, using our analytics to drive GitLab's product development.
Build Foundations for Others - Invest in shared infrastructure that enables product teams across GitLab to quickly deliver analytics features for their domains.
Real-Time When Possible, Historical When Needed - Prioritize real-time insights for operational decisions while maintaining historical depth for trends and benchmarking.
Compete Through Integration - Our differentiation comes from seamless integration with GitLab's SDLC data, not from building isolated best-of-breed analytics features.
Our strategy centers on three interconnected themes:
1. Data Completeness & Quality
2. Intelligence & Insights
3. Platform & Experience
Data Infrastructure & Unification
Developer Productivity & AI Intelligence
Value Stream & Flow Metrics
Platform & Foundations
Dogfooding & Internal Use
Operational Excellence
Against GitHub Copilot Metrics:
Against Atlassian's DX Acquisition:
Against Standalone Engineering Intelligence Platforms (Jellyfish, LinearB, Pluralsight Flow):
The Data Insights Platform (built on ClickHouse) provides the foundation for analytics capabilities. Deployment considerations:
As the data unification work progresses, deployment will become more streamlined and these options will be refined based on customer needs and architectural decisions.