For years, engineering leaders have been drowning in dashboards. But more data doesn’t always mean faster decisions. Today, we’re closing that gap. We’re thrilled to launch Data Hub, a transformative new way of presenting engineering insights that moves beyond static reporting to provide a flexible, curated, and extensible intelligence layer designed to get you to actionable insights faster than ever before.
Why Data Hub?
The modern engineering organization has outgrown “out-of-the-box” metrics. You don’t just need to see your cycle time; you need to know why it’s shifting and how it compares to your industry peers. Data Hub is built to answer your most complex, company-specific questions by giving you total control over how you query, visualize, and benchmark your engineering data.
Data Hub was built on the fundamental idea of providing engineering leaders with flexibility. With Data Hub, you get:
- Purpose-built visualizations and data curation for engineering analytics: We’ve moved beyond generic charts to deliver visualizations engineered for the SDLC. Data Hub introduces entirely new experiences focused on the “why” behind the “what.”
- Root Cause Analysis: Use bottleneck analysis and anomaly detection to spot issues before they become trends.
- Correlation Analysis: Automatically surface how changes in one area – like AI tool adoption – causally impact downstream metrics like code quality or cycle time.
- Companion AI Agents that Expedite Insights: Data Hub features integrated AI agents that work alongside your data to expedite analysis. These agents don’t just provide recommendations, they surface the specific data points behind every insight, and provide the transparency needed for you to act with total confidence.
- Context-aware Data Enrichment: Data Hub leverages our underpinning data platform to enrich raw system data, transforming it into a high-fidelity map of your engineering organization.
- Cross-System Synthesis: We bridge the gaps between tools like SonarQube, GitHub, and AI agents to create a single, unified narrative.
- Human-Centric Metadata: We layer on the context that matters – tagging, team structures, and proprietary Jellyfish insights like Person-Weeks – to ensure your data reflects how your business actually operates, not just how your tools are configured.
A Foundation for the Future of AI
As engineering further shifts to AI-driven development, the ability to create custom, flexible metrics is no longer a luxury – it’s a requirement. Data Hub provides the foundation to measure the impact of tools like GitHub Copilot, Cursor, Claude Code and dozens of others by letting you define and track the bespoke KPIs that matter most to your specific transformation journey.
Stop scrolling through dashboards and start driving insights.
About the author
Ryan Servais is a Product Manager at Jellyfish.
