Organizations of every size are looking for ways to refine their development processes, speed up releases, and deliver higher-quality products. One notable indicator of this interest is the reported impact of DevOps strategies on businesses. CloudZero data shows 86% of professionals see the value of DevOps. The global DevOps market is set to grow too, jumping from $15 billion in 2025 to $38 billion by 2029.
Healthcare providers, for instance, are engaging DevOps-focused transformations to improve operational efficiency and patient outcomes. By unifying development and operations, and reducing friction between various stages of the product lifecycle, DevOps analytics can help get innovations to the market faster.
Source: Research and Markets
Product development must balance speed, quality, user satisfaction, and improvement. However, without measurement, teams cannot improve. DevOps analytics solves this by adding real-time data to the pipeline. It tracks code commits, builds, tests, deployments, production feedback, and more, allowing developers and operations teams to spot potential bottlenecks well in advance.
According to a recent InfoQ report, integrating analytics into DevOps not only improves product stability but also ensures quality assurance remains continuous rather than an afterthought. This analytical approach facilitates immediate feedback loops, reducing the chance of significant defects slipping into production. Instead of guessing, DevOps analytics provides a comprehensive picture of product lifecycle performance to support data-driven decisions.
Rapid software development is growing in healthcare and other regulated areas. DevOps pipelines with rich data help teams detect defects early, polish products faster, and ship updates more regularly.
One metric alone cannot capture DevOps maturity. Instead, organizations should measure several indicators. InfoQ’s DevOps maturity metrics article highlights four key areas:
Source: InfoQ
Especially useful for early-stage DevOps teams, cycle time tracks the time between picking up a task and finishing development before integration. It’s easier to measure than lead time and good for spotting early-stage inefficiencies.
This tracks how often upgrades or new features are released. Regular deployments indicate automated processes.
Throughput is the volume of successfully deployed changes over time. Teams with higher throughput can complete more work faster, indicating great teamwork and workflows.
This number reflects new deployment-related problems or rollbacks. Lower rates suggest better testing and reliable releases.
In regulated fields like healthcare, teams must monitor security and compliance data. The CI/CD pipeline must include compliance checks. Monitoring these factors helps organizations meet strict healthcare data standards and implement services on time.
Healthcare providers work with sensitive data. Mistakes in software can disrupt patient experiences or, worse, compromise data security. DevOps analytics are particularly useful here because they bring transparency to the entire development cycle. As the Devico blog on applying DevOps for analytics product development points out, DataOps—an extension of DevOps for data analytics—enables teams to manage massive amounts of patient information effectively.
Real-time analytics allows for realistic testing of new patient-facing features prior to deployment. Analytics can reveal which components work and which require improvement, whether integrating telehealth or speeding up appointment scheduling.
Collaboration is essential in clinical research, and DevOps analytics can help integrate efforts across departments. Bridging barriers across operations, development, and data science teams not only increases speed but also strengthens data governance. When research teams share and dig into trial data fast, it can help accelerate new therapies.
Let’s look at the digital evolution of Cancer Treatment Centers of America (CTCA), which City of Hope later acquired and integrated. Even before the acquisition, CTCA, a prominent network for comprehensive cancer care, saw clear benefits in leveraging technology more effectively. They aimed to improve patient care delivery and introduce new solutions. This led them to start a significant effort to upgrade their systems and operational workflows.
Kanda Software worked with CTCA during this important period. Our team helped implement cloud DevOps practices and set up managed CI/CD pipelines, streamlining their software development and deployment processes. Kanda also assisted with transforming their data warehousing, which included ETL setup and data cleaning to manage complex patient and operational information more effectively. Separately, Kanda developed crucial management reports; these visually presented key financial data to leadership, enabling better-informed strategic decision-making.
Adopting DevOps methods, enhancing data management capabilities, and providing targeted financial reporting allowed CTCA to innovate faster, build more stable systems, and derive clearer insights across different operational areas. These innovations were crucial to improving their integrative cancer care strategy. This foundation highlights how diverse technological improvements, including DevOps, data management, and specific reporting solutions, can streamline healthcare operations and improve patient care. It also prepared CTCA for further advancements within the broader City of Hope organization following the acquisition.
DevOps is not just a technical revolution but also a cultural change. From the beginning of the software lifecycle, developers, testers, and operations professionals must work together. Effective DevOps approaches are largely dependent on organizational culture. When problems occur, shared accountability reduces blame and promotes transparency.
Automation plays a critical role in DevOps analytics. From continuous testing to automated compliance checks, each stage of the pipeline should produce data that feeds analytics dashboards. As ChaosSearch points out, aggregating logs and metrics into a single, scalable platform eliminates blind spots and makes it easier to spot patterns. Automated processes remove manual bottlenecks and reduce the likelihood of human error.
Analytics should be used during the planning, release, and monitoring phases. KPIs and metrics must be established early on and displayed on the dashboard. Before testing a release candidate, teams must consider code quality, coverage, and performance indicators. If product rollouts fail to meet requirements, teams can stop or fix them.
DevOps is about iteration and continuous improvement. Even after a good release, looking at production data gives ideas for the next development round. These feedback loops help improve everything from code reviews to testing methods. A steady feedback process helps unite operations and analytics, making improvements ongoing not just one-off fixes.
Source: dbt Labs
Getting DevOps analytics going takes planning and know-how. Kanda Software offers services like cloud optimization, analytics dashboards, and smoother CI/CD setups. We team up with businesses and healthcare groups to match DevOps to their needs.
Talk to our experts to learn how DevOps can accelerate your delivery pipeline, reduce costs, and improve software quality.
DevOps analytics provides data to product development teams for decision-making. Companies are learning that an automated, collaborative DevOps pipeline with powerful analytics can enhance releases, improve quality, and accelerate time to market.
Handling sensitive data or meeting big user needs takes precision. Adding DevOps analytics to your process helps hit those marks and improve how users feel. Building a research platform, patient records system, or new app? DevOps analytics will give you real-time understanding of what counts. With the right tools, automation, and team collaboration, you can move quickly and make products that stand out.
