DevSecOps Data Foundation
Stop making decisions on unreliable metrics. Consolidate your toolchain data and establish a single source of truth for delivery and security performance.
The Real Cost of Fragmented Data
Your engineering organisation runs on data from dozens of tools. GitLab for CI/CD. Jira for issue tracking. SonarQube for code quality. Snyk for dependency scanning. Each tool tells part of the story, but no tool tells the whole story.
The result? Leaders ask simple questions and get complicated answers. "What's our deployment frequency?" depends on who you ask. "How long does it take to fix critical vulnerabilities?" requires pulling data from three systems and hoping the definitions match.
This isn't just inconvenient. It's expensive. Teams spend hours building manual reports instead of shipping code. Decisions get delayed because nobody trusts the numbers. Improvement initiatives fail because there's no reliable baseline to measure against.
What You Get
Toolchain Data Audit
A comprehensive review of your existing data sources, identifying what's being captured, what's missing, and where definitions conflict. You'll understand exactly what you have to work with.
Data Integration Architecture
A documented plan for consolidating your key metrics into a unified data model. Not a theoretical architecture diagram, but a practical blueprint your team can implement.
Baseline Metrics Report
Your first reliable snapshot of delivery and security performance. Deployment frequency. Lead time. Change failure rate. Mean time to recovery. Vulnerability metrics. All calculated consistently, with methodology documented.
Data Dictionary
Clear definitions for every metric, including how it's calculated, where the data comes from, and what it means. No more arguments about whether a "deployment" counts.
How It Works
Phase 1: Discovery (Week 1-2)
We map your current toolchain, interview key stakeholders, and identify the metrics that matter most to your organisation. The goal is understanding your engineering context, not just your technology stack.
Phase 2: Assessment (Week 2-3)
We audit your data sources for completeness, accuracy, and consistency. This includes identifying gaps, reconciling conflicting definitions, and documenting data quality issues.
Phase 3: Design (Week 3-4)
We create the integration architecture and data model that will serve as your foundation. This is collaborative work with your team to ensure the design fits your constraints and capabilities.
Phase 4: Baseline (Week 4-5)
We calculate your initial metrics and deliver the baseline report. This gives you a reliable starting point for measuring improvement.
Expected Outcomes
- A single source of truth for DevSecOps metrics that the whole organisation can trust
- 50-80% reduction in time spent building manual reports and reconciling conflicting data
- Clear baseline metrics that enable meaningful measurement of improvement initiatives
- Documented data architecture that your team can maintain and extend
This Service Is Ideal For Teams That...
- Have metrics scattered across 5+ tools with no unified view
- Spend significant time building manual reports or reconciling data
- Don't trust their current DORA metrics calculations
- Want to establish a baseline before investing in improvement initiatives
- Need to report on delivery performance to leadership but lack reliable data
Deep GitLab Expertise
With 8+ years working inside GitLab, including building analytics solutions for enterprise customers, we bring insider knowledge of GitLab's data model, API capabilities, and common integration patterns. If GitLab is part of your toolchain, you're working with someone who knows the platform inside and out.
This expertise extends to organisations using any CI/CD platform. The patterns are similar; the details differ.
Frequently Asked Questions
We work with any CI/CD toolchain. Our deepest expertise is in GitLab, but we regularly work with GitHub Actions, Jenkins, Azure DevOps, CircleCI, and others. On the data side, we integrate with issue trackers (Jira, Linear), security scanners (SonarQube, Snyk, Checkmarx), monitoring tools (Prometheus, Datadog), and incident management systems (PagerDuty, Opsgenie). If your tool has an API, we can likely pull data from it.
Most engagements run 4-6 weeks from kickoff to baseline delivery. The timeline depends on the number of data sources, the complexity of your toolchain, and how quickly we can get access to systems and stakeholders. We'll give you a specific estimate after an initial scoping conversation.
No. The goal is to work with what you have, not replace it. We consolidate data from your existing toolchain into a unified view. If we identify gaps where you're missing critical data, we'll flag them, but the decision to add tools is always yours.
API access or read-only credentials to your key systems, time with 2-3 stakeholders who understand your current metrics and pain points, and a point of contact for questions during the engagement. We handle the heavy lifting on data extraction, transformation, and analysis.
That's actually the most common starting point. Part of the engagement is identifying data quality issues and documenting what's reliable versus what needs attention. The baseline report will be honest about confidence levels for each metric. Bad data is a starting point, not a disqualifier.
Yes, and that's by design. We document everything: data sources, transformation logic, metric definitions, and known limitations. The architecture is built to be maintainable by your team, not dependent on ongoing consulting. We can also provide a retainer for ongoing support if you prefer.
Ready to Get Started?
Tell us about your devsecops data foundation needs and we'll show you how we can help.
Describe Your Challenge