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Part 3: Process Mining to Implement Better DevOps

Part 3:  Process Mining to Implement Better DevOps

As a software project manager or developer, you know that to really understand what is going on, you need the details.

Process mining delivers the details automatically using information collected from your project’s tools.

In the previous articles on process mining, we looked at a specific example of how a project used its past performance data to ensure meeting a critical deadline.

Key benefits from process mining for organizations, projects, and teams are:

  • Maximizes process performance: Data is collected broadly in survey mode across the entire organization to understand how the organization’s processes are performing.  This survey information can be linked directly into management dashboards to provide an automatically-updated overview of the organization’s software development status.  For projects of special interest as indicated by the survey mode data, more detailed data can be collected in a second pass.  These twin depth-and-breadth capabilities provide information to support organization leaders’ awareness without “death-by-PowerPoint” briefings.  It further enables leaders, project managers, and technical personnel to “dive deep” into project-specific details.  The linkage between how the work is done (the processes) and what is being produced (product characteristics) are transformed from intuition to data collection and analysis.
  • Minimizes project disruption due to data collection: Since transactional data is captured automatically from tool environments, project personnel experience none of the typical data collection distractions.  Process mining is actually more efficient than most native reporting utilities for complex queries created by most tools.
  • Maximizes reliability of analysis based on complete, high-integrity data: All transactions are captured, which means that everything going on in a project is visible.  The data comes directly from the tools and represents events in real time without intervention, interpretation, or adjustment by subjective parties.  Data integrity and completeness increases the reliability of the analysis, leading to better process changes.
  • Minimizes the time from activity execution to performance impact analysis: Data is captured as it occurs rather than waiting to report intervals, which means that performance analysis occurs as quickly as the organization or project needs it.  Of course, since being right the first time is always better than fixing a problem, the ability to identify trends and respond with minimal lag time decreases rework and waste.

In our final article of this series, we will look at another real-life process mining example and focus on the steps to get started.

Process Mining to Implement Better DevOps: Part 4

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