#Observatory Best Practices – Analytics & Insights Guide
Observatory is designed to help teams understand testing outcomes, not just visualize data.
Following these best practices ensures your dashboards remain clear, accurate, and actionable as your project scales.
#Think in Questions, Not Charts
Before creating a panel or chart, always start with a question:
- Are we ready to ship this release?
- Where are most failures coming from?
- Is automation actually reducing manual effort?
- Which areas of the product are risky?
If a chart does not clearly help answer a question, it likely does not belong in Observatory.
#Panel Design Best Practices
#Keep Panels Purpose-Driven
Each panel should answer one theme, not everything.
Good examples:
- Release Health
- Execution Quality
- Automation Effectiveness
- Defect Trends
- Repository Coverage
Avoid mixing unrelated metrics in a single panel.
#Limit the Number of Charts per Panel
- Ideal range: 4–8 charts
- Too many charts dilute insights and slow comprehension
- If a panel grows too large, split it into two focused panels
A panel should be scannable in under a minute.
#Name Panels for Outcomes, Not Data
❌ “Execution Charts”
✅ “Execution Stability – Sprint 24”
❌ “Defect Metrics”
✅ “Defects Blocking Release Readiness”
Outcome-driven naming improves stakeholder clarity.
#Chart Selection Best Practices
#Choose the Right Chart Type
-
Bar / Stacked Bar
- Comparisons across categories
- Status, priority, severity breakdowns
-
Line Charts
- Trends over time
- Execution progress, defect trends, coverage growth
-
Pie / Doughnut
- Distribution at a single point in time
- Avoid using for trends
-
Radar Charts
- Multi-dimension comparison
- Use sparingly for high-level views
Avoid forcing data into visually attractive but misleading charts.
#One Insight per Chart
Each chart should communicate one primary insight.
❌ A chart showing:
- status
- priority
- severity
- automation
all together
✅ Separate charts for:
- Execution status distribution
- Failures by priority
- Automation coverage
Clarity beats density.
#Data Source Discipline
#Use the Correct Data Source
-
Repository
- Static structure and coverage
- Priority, severity, automation, test type
-
Executions
- Runtime behavior
- Status, failures, defects, requirements
-
Requirements
- Coverage and traceability
- Do not mix execution noise here
Avoid combining static and execution metrics in the same chart.
#Be Careful with Aggregations
Remember:
- Executions are point-in-time
- Repository data is structural
- Releases represent time windows
Always validate:
What exactly is being counted here?
Misunderstood aggregation leads to misleading decisions.
#Release & Execution Analytics Best Practices
#Track Trends, Not Snapshots
- Prefer line charts for execution quality over time
- Compare releases instead of single test runs
- Look for direction, not absolute numbers
Example:
- “Failures are decreasing release over release” is more valuable than
- “This release has 12 failures”
#Separate Execution Progress from Quality
Do not mix:
- Completion percentage
- Pass/Fail quality
A release can be:
- 100% complete and
- low quality
Use separate charts for:
- Execution completion
- Execution outcome quality
#Avoid Common Observatory Mistakes
Observatory is for decision-making, not just reporting.
#❌ Overloading Panels
More charts ≠ better insights.
#❌ Ignoring Context
Always consider:
- Release scope
- Test run size
- Change volume
#❌ Rebuilding the Same Charts Repeatedly
Create reusable panels and evolve them instead of starting from scratch.
#Using Observatory for Different Audiences
#QA Teams
- Execution trends
- Failure clustering
- Automation effectiveness
#Engineering Teams
- Defect trends
- Failure severity
- Regression impact
#Management / Stakeholders
- Release readiness
- Quality trend lines
- Risk hotspots
Design panels with the audience in mind.
#Maintenance & Evolution
#Review Panels Regularly
- Archive outdated panels
- Update descriptions when scope changes
- Remove charts that no longer add value
Observatory should evolve with your product.
#Final Guiding Principles
- Purpose over polish
- Trends over snapshots
- Clarity over density
- Decisions over dashboards
When used correctly, Observatory becomes a quality compass, not just an analytics screen.
#Next Steps