How to Streamline DevOps with Data & Reporting
DevOps is critical to the integration of software development processes within IT Teams. It strives to enable organizations to release tools, algorithms, and software products more quickly and more reliably by creating a common culture that brings together people, processes, and technology.
The people, processes, technology (and in particular data) have a critical role in the success of your DevOps implementation & practices, which is why Neal has created our DevOps Accelerator.
The DevOps Accelerator is designed to help organizations accomplish DevOps projects more rapidly by providing turnkey access to DevOps experts and resources.
The below example step-by-step tutorial details how DevOps Accelerator can help organizations quickly streamline DevOps with data.
Step 1: Data access
The first step is to connect to your DevOps data. Organizations can use many different tools to track activities and tasks, but the best can connect and evaluate data independently. While tools like Atlassian, Azure DevOps, and Jira, and others all offer ways to track this data, ensuring visualization, automation, and rationalization access is critical.
Step 2: Data exploration
After you’ve connected to your data and gained access, you’ll need to explore and understand the structure in which your tool of choice stores data.
In exploration, you’ll want to keep in mind the key triple constraint of projects. Timeline, schedule, and budget all interact to create unique case-by-case challenges for PMs.
Step 3: Data visualization
Neal has created a unique approach to data visualization for ensuring engagement success. This unique approach is available via our DevOps Accelerator offering.
Data visualization is of critical importance for understanding the timeline, schedule, and budget. The first and most utilized construct in project data visualization is the Gantt Chart, indicated above. Gantt charts communicate committed items, progress against those items (tasks), and account for the timeline (with the red bar). This simple data visualization displays high-level project progress. Combined with the status indicator, team-member breakdowns, and KPI cards above, this provides an at-a-glance view of project progress & any issues.
Next, it’s crucial to consider progress detail. Who is doing what work? What is the volume of their work? How have they progressed over time? It is essential to account for these as it allows for further deep diving and identifies the likelihood of on-time completion overall.
The final evaluative component, in addition to the timeline and task progress, is the budget. An organization’ should evaluate their budget over time as hours or dollar values compared against a baseline. As you can see from the visualization below, our project is slightly over budget on a weekly basis, but task completion is satisfactory.
After diving deep into budget details, often, users of these dashboards want to look at DevOps itself. This final data visualization allows for easy, simple navigation into DevOps from data using hyperlinked items.
These visualizations combine to provide the basic foundation of how to analyze a project’s data. You can find visualization demonstrations in our entry on AppSource.
Step 4: Process evaluation & governance
After setting up effective evaluation methods, organizations need to implement the process to enable effective and regular use of the tools. Without process controls, governance dictating use, and the right practice, all the first three steps above will be ineffective.
Neal recommends that anyone trying to streamline DevOps should take a minute to evaluate their overall process and ensure the data analytics outlined in steps 1-3 will effectively add value to the existing DevOps process. If the evaluation identifies any gaps, the organization should map out any process changes that will need to occur to get the most out of DevOps.
Governance (concerning data entry into your tool) is also critically important. Understanding how proper data entry enables the right level of granularity & usefulness in your data output is critical.
Step 5: Instantiating process in practice
After you understand your process and map out how to change it to be more effective with data, the final and most challenging step is execution. Sticking to process changes is difficult and requires constant discipline and regular checkpoints to ensure everyone is adhering to proposed changes. Maintenance and ongoing operations are critical to getting the most out of your success, with periodic process and governance checkpoints required to ensure lasting success.
What can you expect to see from streamlined DevOps?
From a streamlined & data-based approach to DevOps, you can see:
- Improved project adherence to timeline, schedule, and budget.
- Reductions in sprint & task (schedule) slippage.
- More readily available reporting.
- Increased developer engagement with sprint planning & SCRUM ceremonies.