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Big Data Insights – IT Support Log Analysis

By Pradeep Mavuluri

This post brings forth to the audience, few glimpses (strictly) of insights that were obtained from a case of how predictive analytic’s helped a fortune 1000 client to unlock the value in their huge log files of the IT Support system. Going to quick background, a large organization was interested in value added insights (actionable ones) from thousands of records logged in the past, as they saw both expense increase at no higher productivity.

As, most of us know in these business scenarios end-users will be much interested in out-of-knowledge, strange and unusual things that may not be captured from regular reports. Hence, here data scientist job not only ends at finding un-routine insights, but, also needs to do a deeper dig for its root cause and suggest best possible actions for immediate remedy (knowledge of domain or other best practices in industry will help a lot). Further, as mentioned earlier, only few of those has been shown/discussed here and all the analysis has been carried out using R Programming Language components viz., R-3.2.2, RStudio (favorite IDE), ggplot2 package for plotting.

The first graph (below one) is a time series calendar heat map adopted from Paul Bleicher, shows us the number of tickets raised day-wise over every week of each month for the last year (green and its light shades represent less numbers, where as red and its shades represent higher numbers).

Herein, if one carefully observe the above graph, it will be very evident for us that, except for the month of April & December, all other months have sudden increase in the number of tickets raised over last Saturday’s and Sunday’s; and this was more clearly visible at Quarter ends of March, June, September (also at November which is not a Quarter end). One can think of this as …read more

Source:: r-bloggers.com


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