Scrum is an Agile framework centered on empiricism. But what does empiricism really mean? In Scrum, it refers to basing decisions on observation and experimentation.
A key empirical practice is empirical process control which allows inspecting and adapting the Scrum process itself. Without process control, Scrum fails to realize its full potential.
In this article, we explore empirical process control in Scrum. You’ll learn what it is, why it’s important, and how to implement it.
With effective process control, your team can continuously improve their way of working as process control powers the empirical heart of Scrum. Adaptability becomes natural making your Scrum practice thrive.
What Is Empiricism in Scrum?
Empiricism means working in an evidence-based manner. In Scrum, teams make decisions based on observation and experimentation rather than strict plans.
Scrum utilizes three key empirical pillars: transparency, inspection, and adaptation. Transparency refers to aspects of the process and work being visible. Inspection involves frequently reviewing and analyzing the current state of work. Finally, adaptation means using empirical evidence to adjust the process for the next iteration.
Together, these pillars enable an empirical, iterative approach. The team inspects progress and adapts based on empirical evidence versus defined processes. You constantly experiment and respond to change, enabling flexibility. This empowers teams to deliver faster by eliminating waste and optimizing workflow.
Overall, empiricism is the backbone of Scrum as you rely on observation and inspection of actual events rather than defined processes which empowers adaptation, learning, and continuous improvement.
The Three Empirical Pillars of Scrum
To iterate, Scrum is built on three foundational empirical pillars that enable flexibility and learning which are transparency, inspection, and adaptation.
1. Transparency
Transparency in Scrum means critical aspects of the process and work are made visible to those responsible for the outcome.
For example, the Product Backlog, Sprint Backlog, and daily progress are openly shared with the team. This transparency allows the team to make informed decisions based on observing the actual state of the project.
2. Inspection
Inspection involves frequently inspecting key Scrum artifacts and progress to detect any undesirable variances or issues.
Practices like the Daily Scrum, Sprint Review, and Sprint Retrospective serve as vital opportunities for inspection. The empirical data gathered from inspection is then used as input to course correct.
3. Adaptation
Adaptation entails using the empirical evidence from transparency and inspection to adjust processes and best practices for the next iteration.
The Sprint Retrospective enables this adaptation by having the team brainstorm improvements to their workflow. Adaptation allows continuous improvement based on experience.
Together, these three pillars enable an empirical Scrum approach where progress is regularly inspected against goals, and the process adapts based on observations rather than predetermined rigid plans empowering teams to be flexible, learn, and efficiently deliver value.
What is Empirical Process Control in Scrum?
Empirical process control is a Scrum practice that applies the empirical pillars to the Scrum process itself.
The goal is to inspect and adapt the way Scrum is implemented by your team. This means regularly inspecting your Scrum framework and practices and then adapting based on empirical evidence to improve how you work.
For example, in the Sprint Retrospective, the team might notice that Daily Standups are not productive. By empirically investigating why, they may discover too much time gets spent reporting status rather than coordinating.
The team can then adapt their Daily Scrum process to focus more on impediments and dependencies. This empirical process control improves the way of working.
Without controlling the Scrum process empirically, teams miss opportunities to optimize their practice of Scrum. They risk becoming rigid and not evolving their workflow. Empirical process control enables continuous inspection and improvement.
In essence, it applies the empirical pillar of adaptation to the Scrum process itself which results in flexible processes that get better with each iteration as teams learn.
What is Empirical Process Control Used For?
Empirical process control is critical for optimizing and improving a team’s Scrum implementation over time. By regularly inspecting and adapting their Scrum process, teams can realize several key benefits:
1. Continuous Improvement
Empirical process control enables continuous improvement of the Scrum process. By inspecting their practices during retrospectives, teams can identify areas for improvement.
Adaptations can then be made based on empirical evidence from Sprints. This ongoing refinement allows the team to get better with each iteration.
2. Customize Scrum for Your Team
There is no one-size-fits-all version of Scrum. Empirical process control allows each team to evolve Scrum and customize it to their unique context. The framework can be optimized for how your specific team works best based on empirical insights.
3. Increase Agility
Applying empirical adaptation to the Scrum process itself makes that process more Agile. Just as Scrum teams respond to change in projects, process control allows responding to change in process through short feedback loops.
4. Eliminate Waste
Empirical scrutiny enables the removal of wasteful or low-value Scrum activities over time. Activities that regularly prove ineffective can be adapted or dropped based on the team’s experience. This eliminates inconsistencies between standard Scrum and your team’s ideal workflow.
5. Reinforce Helpful Practices
Retrospective insights derived from process control help sustain useful Scrum ceremonies over time. Good behaviors and norms that the team finds productive can be reinforced while degrading practices are stopped based on evidence.
6. Onboard New Team Members
Documenting changes and insights from process control helps onboard new team members as key learnings and empirical evidence for adaptations are captured. This transfers institutional knowledge on team norms and effective ceremonies to new members.
7. Enable Process Improvements
Insights from process control provide input to the Product Backlog for Scrum process improvements as an enabler for greater team productivity. Improving Scrum efficacy helps the team deliver value.
Examples of Empirical Process Control in Scrum
Here are some examples of how teams can leverage empirical process control:
1. Inspecting the Sprint Retrospective
During a retrospective, the team realizes their sessions have become ineffective. Few improvement ideas are generated anymore. By empirically investigating why, they identify two root causes:
- The format has gotten stale after a year of similar exercises
- The team doubts if identified actions are implemented
They adapt by varying the retrospective format to re-engage participants. Also, they agree to review past action items at the start to close the loop.
2. Assessing the Definition of Done
The team isn’t releasing releasable increments. Digging deeper, they find acceptance criteria are unclear upfront. They adapt by strengthening their definition of done. Now each story must have test cases and peer review prior to release.
Related: Learn the Difference Between Acceptance Criteria and Definition of Done
3. Examining the Role of Scrum Master
The Scrum Master notices they spend minimal time on enabling the team and most time in meetings. The team adapts by delegating some ancillary work to others. This frees up the Scrum Master to coach and improve team practices.
4. Inspecting Product Backlog Refinement
The team determines Backlog refinement isn’t effective since discussions tend to rehash. They agree to change the format to break down large stories first, without debating details upfront. This adaptation saves time.
5. Evaluating the Sprint Cadence
Based on several Sprints where teams struggled to meet commitments, the team wonders if 1-week Sprints are too short. They decide to experiment with 2-week sprints and find it improves focus and consistency. The team adapts accordingly.
These examples illustrate how empirical process control helps inspect the Scrum process itself and then make improvements based on findings.
Difference Between Defined Process Control and Empirical Process Control
Defined process control and empirical process control take fundamentally different approaches to managing and improving processes. Some of the key differences between them are:
1. Predetermined vs Adaptive
Defined process control means having preset processes that teams consistently follow, and changes require formal approval.
In contrast, empirical process control allows teams to adapt processes through inspection and feedback, and change emerges through experiments and learning.
2. Rigid vs Flexible
Defined processes are rigid. Here teams strictly adhere to defined steps and deviations from standards require justification.
Empirical control on the other hand enables flexibility to modify processes that aren’t working. Teams can customize frameworks for their context.
3. Top-down vs Bottom-up
In defined control, change is top-down and senior leaders dictate processes like requirements gathering, testing, and reviews.
Empirical control relies on bottom-up change driven by feedback from team members directly doing the work.
4. Predictive vs Adaptive Planning
Defined control emphasizes extensive predictive planning and analysis of future work with teams estimating all tasks upfront.
Empirical control focuses on planning for the near-term. The team adapts as they learn, keeping just enough ahead to maintain workflow.
5. Process Assurance vs Continuous Improvement
The aim of defined control aim is standards conformance, and it uses process audits and quality control to provide process assurance. The aim is standards conformance.
Empirical control improves processes through short feedback cycles. The focus here is continuous improvement versus merely meeting standards.
Conclusion
Ultimately, empirical process control is essential for realizing the full benefits of Scrum. By applying empiricism to how Scrum is implemented, teams can inspect and adapt their practices based on experience which results in processes that improve through measurable learning, not rigid conformity.
Empirical process control enables your team to customize Scrum to your context, remove waste, and continuously evolve. With the right process control, Scrum becomes a flexible framework that helps unlock higher performance, responsiveness to change, and remarkable productivity.
Empiricism gives Scrum its true power, and empirical process control unleashes the empirical potential for your team.
FAQs
Is Empirical Process Control a Scrum Principle?
No, empirical process control is not one of the five official Scrum principles which are:
– Commitment
– Focus
– Openness
– Respect
– Courage
However, empirical process control is a key Scrum practice that aligns with and enables several Scrum principles like openness, focus, and respect. It epitomizes Scrum’s empirical, iterative nature.
What is The Empirical Rule of Scrum?
The empirical rule of scrum refers to Scrum’s empirical process control foundation. It means:
– Controlling processes through transparency, inspection, and adaptation
– Improving by empirically scrutinizing actual events and outcomes
– Basing decisions on observed evidence versus preplanned processes
– Valuing flexibility and continuous learning over rigid procedures
In essence, it is the rule that processes must be empirically inspected and controlled to maximize agility.
