Data Contextualization

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Data contextualization refers to the capture and packaging of relevant and useful information used in incident root cause analysis. This may include correlation (i.e. What other performance metrics behaved abnormally at the time of the incident?), co-occurrence (i.e. What else happened on other systems at the time of the incident?), seasonality (i.e. Was the incident anomalous given the timeframe?), as well as other information and insights derived log data and information that is relevant for the particular application domain being managed (for example, what jobs were being executed at the time of the incident?). The goal is to help incident investigation teams more quickly makes sense of data that in isolation is hard to understand. See also Context.

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