Empirical Process Control

Agile Delivery - Empirical Process Control

Empirical process control is the foundation of Agile Delivery. It is based on continuous improvement and visible progress. This means data from the past is used to plan for the future.

The resulting progress becomes visible, which helps in quickly reacting to new insights.

Low Rating:

  • Project goals and the development process are treated as fixed constraints.
  • The Customer is not directly and continuously involved, a requirements specification and/or proxy roles stand in their place
  • Technical questions are mostly decided in ad hoc fashion or fire-fighting mode
  • There is no room for experiments, the main management focus lies on resource utilization

High Rating:

  • Business and customer focused goals are known and directly represented by stakeholders
  • Assumptions are validated through experiments
  • Decisions are risk-driven and use the value of options and learning windows. The last responsible moment is honored
  • Insights from retrospectives lead to effective actions that possibly involve people outside the team boundary

Core Ideas

Value the last responsible moment

In an empirical approach options have value. Decisions remove potentially valuable options. Since every experiment can generate new insights that lets us develop a better understanding, we try to decide at the last responsible moment. Besides creating room for experiments the last responsible moment also enables us to react to shifting goals.

Know your business and customer focused goals

Before we can start working empirically we need to know what success and failure would look like. This means we have to know our goals. We recommend to start with a focus on the complete product from a business and customer perspective: What do our customers need and value? What quality criteria are important to them?

Validate assumptions through experiments

We formulate hypotheses about these goals and how to achieve them. Then we try to validate our assumptions with experiments. This creates empirical data that we measure and compare against our goals. We use these results for our next hypotheses and experiments.

Sector Rating Aspects

These are aspects that can be used to assess your performance and maturity in this sector. For a detailed explanation please have a look at our how to page.

Aspect Adoption Lifecycle
Process and progress data is visualized (e.g. burn-up / burn-down charts, Kanban Boards) Late majority
Data from the past is the primary driver for forecasts of the future (e.g. velocity, throughput) Late majority
Short iterations drive development (e.g. Sprints) Late majority
Iterations have defined goals that are actively tracked Early majority
Business representatives are directly involved in development Early majority
Continuous improvement is an important driver (process, tools, communication are topics of retrospectives, not only business-related features are addressed) Early majority
Categorize problems after their Cynefin domain to find out where iterative-incremental approaches have value (e.g. Agreement & Certainty Matrix Liberating Structure) Early majority
Goals are used on different flight levels and interlinked (e.g. strategy/portfolio-focused, coordination/product-focused, operational/work-focused) Early adopters
DevOps mindset is established (3 Ways of DevOps) Early adopters
Fail fast, fail safe culture is established (small experiments, safe environments) Innovators
The broad use of the last responsible moment creates learning windows (technical abstractions, re-prioritization of features, use of technical adaptors, …) Innovators