A manager tells you: Something is wrong with the order process. Can you investigate?
You look at the data and notice that orders requiring manual review have increased from 8% to 12%. Is that enough to start an investigation? Not yet.
A pattern is not automatically a problem. It becomes worth investigating when you can establish that the change is real, relevant and potentially consequential. Diagnostic analytics uses historical patterns, drill-downs and hypothesis testing to move from an observation toward an explanation.
Start with three questions.
Is the pattern real?
Check the time period, data quality, definitions and whether a system or process change could explain the movement. Is it unusual?
Compare it with a meaningful baseline rather than simply reacting to one period. Normal business patterns can vary by season, segment or operating conditions. Does it matter?
A 4-point increase may be operationally insignificant in one process and expensive in another.
Now turn the observation into a testable question:
Did the increase in manual reviews come primarily from a particular customer segment, product type or process change?
That gives you a direction for the analysis instead of producing ten unrelated charts.
You may discover that the pattern is real but harmless. Or you may find that a small overall change is concentrated in one segment where the operational impact is significant.
The analyst's job is not to investigate every unusual number.
It is to determine which patterns deserve evidence, which can be explained quickly, and which require deeper investigation.
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