As organizations look for opportunities to put data analytics to work, DevOps is one area that deserves attention. In many organizations, data scientists and software developers are still siloed, but bringing their talents together makes sense. Development informed by data can help teams in a number of ways:

  • Identifying issues before they get too far down the pipeline
  • Monitoring the health of the environment
  • Tracking the usage of applications to ensure that users can make the most of them

Learn how CDW can help you enable DevOps within your organization.

The goal of DevOps is to shorten the time required to get ideas into practice by accelerating the development process in a quality-controlled way. Its methodology depends on a steady, reliable source of clean, organized and indexed data. Generating the right data during the DevOps process, and then using the resulting insights to inform the next iteration, can minimize error and increase the likelihood of success.

Analytics is a natural complement to the DevOps feedback loop, providing valuable insights that inform continuous improvement in decision-making. Collaboration, however, is essential. When developers know what data they need to collect, they can create code that will write logs to a file for analysts’ review. That requires not only joint planning but also clarity about what the goals are — both for development and for the business as a whole.

1. Predictive Analytics Can Forecast Likely Development Issues

Because DevOps depends on automation of test, build and production processes, one of the best ways to incorporate analytics is by monitoring the pipeline to predict the outcome of application changes before they hit the market. Machine learning can determine how code changes will affect an application and the environment it runs on. That makes it possible to quickly identify problems — such as performance issues, application degradation, server overload and other undesirable results — so they can be fixed before a change goes into production.

2. Analytics Monitors the DevOps Environment

Analytics tools also help DevOps teams monitor the health of the overall environment. That’s critical for the speed at which DevOps works. It’s also valuable for organizations with complex environments that implement numerous technologies, such as on-premises servers, containers and cloud resources. Analytics tools make it possible to see all of that in one place and to identify the source of any issues with a root cause analysis. This capability makes it far easier to resolve problems quickly, before they lead to costly downtime.

DevOps, at its core, is a methodology to effectively and efficiently make positive changes. The extent to which it can incorporate analytics furthers the ability to predict the impact of those changes. For positive outcomes, analytics can help DevOps teams replicate the result. When predicted outcomes are negative, analytics can help teams change course.

3. Analytics Helps to Optimize the Use of Applications

Once an application has been deployed, analytics can help staff assess its utilization. Often, organizations build or purchase an application and then discover that it isn’t being used in the way it was intended. Analytics can help staff determine the reason so they can address it. Is the problem one of functionality, or perhaps related to a need for more training?

Virtually all businesses in all sectors can benefit from these types of insights into their DevOps processes. What makes it possible to reach that desired end state is to implement DevOps in conjunction with the appropriate type and amount of data. This can ensure that the insights an organization gleans improve and inform not only development processes but also the business requirements that those processes support.