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Chapter 11 of Daniel S. Vacanti’s Actionable Agile Metrics for Predictability: An Introduction (buy a copy today) is titled Interpreting Cycle Time Scatterplots.  Daniel Vacanti opens Chapter 11 with a reminder that, “your policies shape your data and your data shapes your policies.”  As a leader or coach, this missive is a reminder that you need to use data visualization as a trigger to ask what policies are in place and WHY they cause your data to look the way they do.

There are many common patterns that are useful triggers for discussion.  The trends Vacanti references in Chapter 11 include:

The Triangle

The triangle is generated when arrivals exceed departures or when flow debt is accumulated (flow debt is defined in Chapter 10

An example of the triangle pattern (note – I added the arrow to explicitly show the pattern): 

A Cluster of Dots

I have often called this pattern the “what heck is going on” pattern.  When clusters emerge is easy to jump to conclusions, but a better strategy is to ask the question: “What is going on?” One of the possible outcomes of the analysis of a cluster is that some policy is causing unexpected behaviors in certain circumstances. For example, a conversion I was recently involved with generated a spike in overtime in the last few iterations as unexpected difficulties arose after the conversion date was committed to the stakeholders. Vacanti suggests that this pattern can occur when “making forecasts truth.”

Gaps

Gaps in the cycle occur when nothing leaves the process.  Gaps can occur for many reasons, including scenarios such as batch transfers, interruptions or even stranger situations (for example, government shutdowns).  Visualization helps teams and organizations to ask ‘why’ and ‘what’ questions. We need to ask questions; just because this pattern exists does not mean a problem exists.  One scenario where I often see this pattern is when work is completed only at sprint or iteration boundaries.  The graph below is similar to a pattern I recently saw when a team cut over from continuous delivery to releasing at the end of every sprint.

Variability

Variability of predictability can be caused by ad-hoc processes, uncontrolled work entry or interactions with other teams or organizations. In this scenario, questions should target generating an understanding of the causes of variability.

Visualizing data makes it easy to identify patterns.  Patterns are not an end but the beginning of a conversation to determine what is actually happening and what you want to do about the pattern.

Previous 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 5: Introduction to CFDs

Week 6: Workflow Metrics and CFDs

Week 7: Flow Metrics and CFSs

Week 8: Conservation of Flow, Part I

Week 9: Conservation of Flow, Part II

Week 10: Flow Debt

Week 11: Introduction to Cycle Time Scatterplots

Week 12: Cycle Time Histograms

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