Context Stability
Context Volatility: Hidden Interactions Between Slicers and Measures
Volatile context is caused by slicer interactions, hidden filters, and ambiguous paths.
Rendering diagram...
Two slicers interact to produce unexpected results.
TL;DR
- • Small filter changes can create big result swings.
- • Volatility is a design issue, not a user issue.
The problem (layman)
- • Slicers interact in ways that are hard to predict.
- • AI does not understand which filters are dominant.
Why it matters
- • Volatility causes inconsistent answers and confusion.
- • Automated explanations become unreliable.
Symptoms
- • Changing one slicer unexpectedly changes another metric.
- • The same question yields different results based on hidden filters.
Root causes
- • Bidirectional relationships and ambiguous filter paths.
- • Measures that override filters without disclosure.
What good looks like
- • Slicer behavior is tested and documented.
- • Measures report the filters they apply.
How to fix (steps)
- • Create a context matrix for key slicers and KPIs.
- • Reduce bidirectional relationships.
- • Add context inspection to AI outputs.
Pitfalls
- • Over‑reliance on visual testing.
- • Assuming users will “figure it out.”
Checklist
- • Context tests for top slicer combinations.
- • Explicit filter documentation.
- • Reduced bidirectional filters.