It is nearly 2024! Re-read Saturday is taking a one-week hiatus so I thought we would re-publish the conclusion of our re-read of Daniel S. Vacanti’s Actionable Agile Metrics for Predictability: An Introduction (buy a copy today). There are four concepts that I would like to revisit as we conclude. They are:
- Measuring flow means understanding the process. Predictability and metrics require teams and organizations to spend the time and EFFORT needed to understand the processes used to deliver work. While at a very granular level, the work needed to deliver a new or changed feature is different from the one you have done before, if we take even one step back we can observe and measure a smaller set of common steps. The value of understanding your process is that understanding allows measurement, experimentation, and change so you or your team can deliver more value.
- Cycle time is a tool for developing and using predictability. There are many metrics and measures used in software organizations. Many of these are useful, yet very few of them are as obvious and unobtrusive as the length of time needed to complete a piece of work. Every human in the workplace understands the calendar and the clock. Measuring how long work takes forces organizations to understand how work enters and then exits the process. This provides the team with the data so they can answer questions about how long work will take and when specific pieces of work might be completed without blind guessing.
- Avoid averages. Very few processes deliver normally distributed values (you remember the bell curve). Much of the standard statistics often used to describe metrics make the assumption of normality. Statistics I have seen in metrics presentations typically include averages, means, standard deviations, and less often, concepts like control limits. Unless you can plot the data and prove the distribution of the data falls into a normal distribution, avoid using those statistics. Vacanti’s book uses the simple concept of percentage lines on scatter plots (for example, if you drew an 85% line, 85% of the observations would fall on this line or below). The approach allows the person reading the chart to develop service-level agreements and forecasts.
- Use data to improve how you work. Continuous process improvement is either an explicit or implicit goal in every organization. Unless we generate more value or revenue some other organization (you can substitute person or team) will find a way to eat your lunch. In week 18, I used the punchline “define your process, get some data, and then do something with it” to draw attention to the need to pursue continuous improvement.
Constraining the wrap-up to four items means that I left a ton of items on the floor: cumulative flow diagrams, Little’s Law, histograms, flow debt, and Monte Carlo analysis are just of few of the important items discussed in Actionable Agile Metrics for Predictability: An Introduction. You need to read the book and our Re-read because a simple wrap-up can only scratch the surface of the value in the 300 pages of this book. Start using the ideas in the book. Using the ideas in this book will change how you work. Period!
PS – As usual, I agree with Steven Adams who commented last week. You should read Chapter 17, the case study even though we did not include it in the re-read. Also, SPaMCAST 486 (March 18, 2018) will feature an interview with Daniel Vacanti.
All Installments
Introduction and Game Plan
Week 2: Flow, Flow Metrics, and Predictability
Week 3: The Basics of Flow Metrics
Week 4: An Introduction to Little’s Law
Week 6: Workflow Metrics and CFDs
Week 8: Conservation of Flow, Part I
Week 9: Conservation of Flow, Part II
Week 11: Introduction to Cycle Time Scatterplots
Week 12: Cycle Time Histograms
Week 13: Interpreting Cycle Time Scatterplots
Week 14: Service Level Agreements
Week 16: Introduction to Forecasting
