This article is based on the CloudBees DevOptics announcement at Jenkins World 2017, originally reported by Enterprise Times.
What Is CloudBees DevOptics?
At Jenkins World 2017, CloudBees announced the general availability of DevOptics, a product designed to give engineering and DevOps teams comprehensive visibility into the effectiveness of their software delivery processes. The announcement addressed a gap that has long existed in the industry: while software development teams have numerous tools for tracking project performance and comparing output across teams, no equivalent system existed to provide a holistic, enterprise-wide view of DevOps pipeline performance—until DevOptics.
The Problem: Valuable DevOps Data Is Disappearing
Sacha Labourey, CEO of CloudBees, framed the core problem clearly at the announcement:
“This is about data. We go through a lot of code changes, use a lot of tools, make a lot of modifications but all of the data vanishes.”
Sacha Labourey, CEO, CloudBees
Labourey’s vision is to capture DevOps process data in the same systematic way that enterprises capture business operations data in an ERP system. Rather than letting pipeline events, build results, and delivery metrics dissolve into isolated tool logs, DevOptics is intended to aggregate that data into a persistent, queryable system of record.
He elaborated on the broader organisational challenge:
“DevOps has been adopted in many, many cases as a feature that we replicate across the organisations. It’s a feature at scale not an enterprise solution. Now we are moving towards building a system of record for IT processes.”
Sacha Labourey, CEO, CloudBees
This distinction is significant. Many organisations have implemented DevOps practices team by team, resulting in a patchwork of local optimisations with no unified view of delivery performance across the enterprise.
How DevOptics Works: Jenkins as the Data Foundation
DevOptics does not require teams to replace their existing toolchains. Instead, it leverages Jenkins—already widely used as a CI/CD orchestration layer—as the underlying data store. Build events, pipeline executions, job statuses, and delivery metadata that Jenkins already tracks become the raw material for DevOptics analytics.
DevOptics sits on top of this data layer, mining it and applying analytics to surface insights that would otherwise be invisible to engineering leaders and DevOps practitioners. The architecture is designed to aggregate data across all Jenkins instances in an enterprise, not just a single team or project.
Four Key Capabilities of CloudBees DevOptics
According to the CloudBees product announcement, DevOptics delivers four primary capabilities:
1. Single Source of Truth
DevOptics collects and correlates data across software delivery pipelines into a comprehensive live view, creating a single point of reference for the status of application delivery across the organisation. This eliminates the need for time-consuming release status meetings and manual checklist reviews, replacing them with real-time pipeline visibility.
2. Identifying Bottlenecks and Improvements
The product provides end-to-end visibility into enterprise-wide software delivery value streams, with the ability to drill down to individual commits. This granularity allows teams to identify failed jobs, pinpoint bottlenecks, and surface critical downstream dependencies—providing the context needed to optimise every stage of the delivery process.
3. Enabling Collaboration
DevOptics captures build events directly from development systems, reducing the guesswork involved in diagnosing the cause of blockages and delays. By answering who changed what, where a failure occurred, and when it happened, teams are better equipped to collaborate on corrective action quickly and accurately.
4. Assessing DevOps Productivity
DevOptics enables organisations to derive metrics from across the entire delivery process. Engineering leaders can create benchmarks to identify best-performing teams and highlight areas of poor resource allocation—bringing a level of performance management to DevOps that previously only existed for traditional software development.
AI and Machine Learning: Planned for Future Releases
When asked at the Jenkins World 2017 announcement whether DevOptics would incorporate AI or machine-learning-driven analysis, Labourey was candid: that capability was not part of the first release. CloudBees planned to begin with a defined set of use cases and analytics features, with machine learning and AI-driven insights described as a future direction—though no specific release timeline was committed to at the time of the announcement.
Given that automation is central to DevOps philosophy, the eventual addition of ML-driven anomaly detection and predictive analytics would be a natural extension of what DevOptics aims to do. Automating the identification of pipeline inefficiencies—rather than requiring engineers to manually review dashboards—would align the product more closely with DevOps principles at scale.
Why This Matters for DevOps Teams
The announcement of DevOptics highlights an important maturity gap in how enterprises manage DevOps at scale. Individual teams may have strong local metrics—deployment frequency, build success rates, mean time to recovery—but without an aggregation layer, engineering leadership cannot answer fundamental questions about delivery performance across the organisation as a whole.
DevOptics directly targets this gap by positioning Jenkins, already embedded in most enterprise CI/CD pipelines, as the data collection backbone. This is a pragmatic approach: rather than requiring organisations to instrument new tooling or replace existing systems, it extracts value from infrastructure that is already in place.
The broader question for engineering leaders evaluating DevOptics is whether their organisation has reached the scale at which siloed team-level analytics are genuinely insufficient—and whether investment in a system-of-record approach to DevOps data will translate into measurable improvements in delivery velocity and quality.
Summary
- Announced: Jenkins World 2017 by CloudBees
- Core function: Aggregates Jenkins pipeline data into enterprise-wide DevOps analytics
- Key benefits: Unified delivery visibility, bottleneck identification, collaboration support, productivity benchmarking
- Architecture: Jenkins as the data layer; DevOptics as the analytics and reporting layer on top
- AI/ML: Planned for future releases; not included in initial version
- Target audience: Enterprise DevOps teams using Jenkins at scale
Original reporting by Enterprise Times at Jenkins World 2017.