Catalog
method#Product#Delivery#Governance#Software Engineering

Pareto

A prioritization and analysis method observing that a small share of causes often produces a large share of effects (the 80/20 rule). It helps focus effort on the most impactful levers.

The Pareto method identifies, via simple analyses, the few factors that drive the majority of an outcome.
Established
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

Issue trackers (e.g., Jira, GitHub Issues)Analytics and monitoring tools (e.g., Grafana, Google Analytics)Product roadmap tools (e.g., Aha!, Productboard)

Principles & goals

Concentrate effort on a few high-impact causes.Use valid data for prioritization instead of gut feeling.Iterative review: regularly refresh Pareto analyses.
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Wrong prioritization from biased or incomplete data.
  • Neglecting rare but critical causes.
  • Overreliance on simple metrics instead of deeper analysis.
  • Use clear categorizations and consistent metrics.
  • Iteratively review results and refresh data regularly.
  • Combine Pareto with root-cause analyses for sustainable solutions.

I/O & resources

  • Dataset of categorized incidents, defects or requirements
  • Metrics for frequency, cost or benefit
  • Stakeholder estimates and contextual information
  • Prioritized list of causes or actions
  • Visualization (Pareto chart) for communication
  • Recommended quick wins and longer-term actions

Description

The Pareto method identifies, via simple analyses, the few factors that drive the majority of an outcome. It helps teams prioritize problems, requirements, or actions by focusing on the most impactful levers. It is commonly used in analysis and decision-making processes.

✔

  • Faster identification of high-impact leverage points.
  • More efficient resource use via targeted prioritization.
  • Improved decision basis through data-driven focus.

✖

  • Simplification: not all relevant factors follow the 80/20 pattern.
  • Dependence on data quality and completeness.
  • May overlook short-term side effects when focusing solely on counts.

  • Share of issues from top-3 causes

    Measures the percentage of incidents caused by the three most frequent causes.

  • Effort-to-impact ratio

    Compares estimated effort with expected benefit for prioritized actions.

  • Volume reduction after intervention

    Measures decrease in tickets/defects/requests after implementing a measure.

Support ticket reduction via hotspot fixes

A SaaS provider fixed two frequent bugs and reduced 70% of support requests within a month.

Product roadmap focusing

A product team focused releases on the 20% of features with 80% predicted value and achieved faster revenue growth.

Fixing critical modules

By targeted refactor of a high-change module the defect rate decreased significantly.

1

Collect, categorize and clean data.

2

Create Pareto chart and identify top drivers.

3

Prioritize actions, assign responsibilities and measure impact.

⚠️ Technical debt & bottlenecks

  • Unclear or non-standardized categorization rules in ticket systems.
  • Missing automated reporting pipelines for Pareto metrics.
  • No historization of analyses, causing loss of trends.
Data qualityStakeholder alignmentMeasurability of impact
  • Prioritizing by ticket count only, although individual tickets cause high business damage.
  • Adopting a one-off analysis as permanent policy without monitoring.
  • Uncritically mixing data from different sources and drawing wrong conclusions.
  • Confusing correlation with causation among top drivers.
  • Overlooking hidden dependencies between categories.
  • Making premature decisions without validation measures.
Basic data analysis and visualizationExperience in prioritization and stakeholder facilitationUnderstanding of business impact metrics
Transparent data basis for prioritizationHigh impact with low effortFast decision cycles
  • • Availability of relevant and consistent data.
  • • Time pressure for quick decisions.
  • • Limited capacity for deeper analyses.