Silvia Davis, Sr. Product Marketing Manager at Digital.ai; PMP; SAFe SPC; and ITSM Expert
Meet Kim – Kim is a product manager for an airline organization, and one of her main challenges is to predict and ensure that her digital products are delivered on time. As you know, managing your application pipeline is not easy because there are significant risks throughout the entire process.
When speaking to Kim, she mentioned that the application release and deployment process is a high risk for delays. She says, “Releasing an application is like going through the airport security gates. You may have some idea about the wait time, but it can change dramatically from gate to gate and airport to airport. It is hard to predict the wait time before arriving at the security gate.”
Kim is right. Deploying applications to complex environments is unpredictable and risky and may cause critical issues for the business and its customers.
Kim didn’t have visibility into what was in the release and deploy queue and could not predict the delivery time or risk well. On the other hand, the release manager, Joe, couldn’t see and plan his work because he didn’t have any visibility from the development teams on what was being “developed.”
Kim blames the release team, and Joe blames the development team for the same reason: clogged pipelines, lack of visibility, and major application delays.
Siloed and Uncorrelated Data
Kim and Joe are not alone. We have seen this over and over in many enterprises that have complex environments. There’s an increasing number of DevOps tools adopted in every organization. These tools are mostly siloed and with separated data sets, making it hard to identify the major bottlenecks and risk of application delay. You may have a ton of tools, and a bunch of analytics, but not the entire view of the DevOps pipeline.
The 3 Steps to Unclogging your DevOps Pipeline
Let’s now see how we can solve Kim and Joe’s challenges, using these three steps:
Leverage your DevOps data: Integrate your DevOps tools and correlate the data to a single unified Analytics platform. Include operational metrics, exploratory metrics, and predictable analytics that serve the different roles within the software delivery lifecycle.
Data is the foundation to unclog your pipeline! Why?
With a good set of initial end-to-end analytics, you can identify where your bottlenecks are, what areas you can automate, and what applications to prioritize, giving you the ability to remove risks of delays.
Adding AI-Machine Learning models helps you to predict risks and timeframes.
The dashboard below gives visibility into the “Last Tasks and its Impact.” It helps Kim and Joe to analyze what releases and teams are taking more time and allows them to drill down and find the root cause of the delays. It also helps visualize the impact on production and its success, so they can take action to prevent the same application release problems from occurring.
Implement a “fit-to-purpose” release management solution: Orchestrate your application release and deploy.
Many customers use spreadsheets or work management tools to manage hundreds of tasks to release applications. The issue with these tools is that they are not designed to orchestrate multiple teams and their tasks, the change management process and it’s gates, and the risk of change failure.
Using a Release Management tool designed to orchestrate releases and deployment tasks in complex environments reduces operational errors and removes bottlenecks and risks of application failure in production.
Here is an example of an application release view with multiple components being delivered in various environments.
The picture below depicts how Digital.ai Release can combine multiple releases into an application view, giving Kim the visibility of all releases tied to her application. The respective application has multiple releases with various components being deployed using Digital.ai Deploy and Argo. Kim can now see the status in real-time and take immediate action as needed.
Automate all possible deployment and release tasks while ensuring compliance: Define your deployment strategy up front and include the deployment step and the provisioning of the infrastructures, specifically for cloud and container environments.
Many customers use various tools to automate application deployment, but they are all siloed. Also, manual deployment and provisioning are not an option, especially in complex environments that require you to comply with government standards. As mentioned above, use data to identify possible areas of automation, select an orchestration tool integrated into your deployment tools, and automate all possible tasks. It helps bring efficiency and – more critically –reduces the risk of operational errors.
In the diagram below, Digital.ai Release allows Joe to define his deployment strategies per environment and application, integrate with various deployment tools, automate deployment, and have visibility into what is going on. Joe can now take immediate actions, prioritize his pipeline and be more efficient in releasing applications to production.
Going back to Kim and Joe – the good news is the blame is gone!
They now have the Digital.ai DevOps platform that helps them to have visibility in the entire DevOps pipeline, predict when the application will be delivered, and mitigate risks that may come to prevent application delays and application failure in production.