Evidence-Based Agile

Moving from dogma to data

Agile is often diminished by surface-level implementation. Teams adopt the ceremonies without the mindset. They track velocity without understanding what drives it. Evidence-based agile uses scientific methods to answer specific questions: What are we trying to achieve? How will we measure it? What does the evidence say about our current approach?

Real examples

Optimising sprint completion. A large enterprise struggled to meet sprint goals. They tested breaking large stories into smaller pieces. Teams using smaller work items saw a 15% improvement in sprint completion with fewer mid-sprint disruptions.

Measuring team effectiveness. Working with Christiaan Verwijs on the framework behind Columinity, we identified the factors that drive team performance: Responsiveness, Continuous Improvement, Stakeholder Concern, and Autonomy.

Validating product decisions. Systematic testing guided feature priorities. One feature produced minimal engagement change. Another unexpectedly drove a 30% increase. Without testing, the team would have invested in the wrong feature.

Metrics that matter

Teams with bi-weekly stakeholder reviews saw 25% higher customer satisfaction. Sprint consistency improved 35% when responsiveness was measured. Recurring problems dropped 30% when retrospective outcomes were tracked. Autonomous cross-functional teams reduced completion times by 20%.

The bottom line

Agile without measurement is guesswork. Measurement without action is bureaucracy. Evidence-based agile treats every practice as a hypothesis and every sprint as an experiment.