Quick answer: A list of 200 unique crash reports is overwhelming. Clustering by stack signature, device, build, and locale surfaces actionable patterns from the noise.
Crash data is a haystack. Clustering finds needles; flat lists hide them.
Cluster by stack hash
Top 5 frames hashed. Identical hashes group; each group is one bug type. 200 reports collapse to 20 bugs.
Slice by attributes
Per-cluster: device distribution, OS distribution, build versions. Patterns surface ('only on Pixel 6', 'only on builds > 1.4').
Time-series
Per-cluster occurrence over time. Sudden spikes indicate regression; slow growth indicates degradation.
Triage by severity x frequency
Severity * frequency = priority. Crash-on-startup at low frequency beats crash-on-side-feature at high frequency.
“Patterns are the value in crash data. Single reports are usually less informative than the clusters.”
Build a crash dashboard with attribute slicing. The slicing is what makes the data actionable; raw lists rarely are.