Disciplined Agile (DA) emphasizes the use ofautomated measuresto enable data-driven decision-making in projects. This is aligned with the Disciplined Agile Manifesto and principles, which focus on pragmatism, the use of relevant metrics, and fostering continuous improvement within teams.
In Disciplined Agile, metrics are viewed as tools that provide objective insights into project health and progress. The use of automated measures helps to minimize human error and bias, and enhances the reliability of the data being collected. Automated metrics can include lead time, cycle time, throughput, defect rates, and other quantitative data points that can be generated from automated tools (such as Jira, Azure DevOps, etc.). These metrics allow teams to make informed decisions swiftly and adjust their practices and processes based on actual data.
The DA mindset also promotes outcome-oriented metrics rather than activity-oriented ones. This means teams focus on the end results and the value being delivered, rather than just measuring tasks completed or the amount of work done. By favoring automated and objective measures, DA ensures that teams base their decisions on accurate and timely information rather than subjective opinions or potentially misleading scalar measures.
This is also supported by the DA principle that encourages “Optimize Flow Across the Entire Value Stream” – which relies on data-driven decision-making to effectively manage and optimize processes. Overall, automated measures align with DA's goal of achieving agile goals in a pragmatic, context-sensitive manner.
