In Sopra Steria we manage the IT infrastructure and applications of big clients. We process millions of service tickets and infrastructure events. This massive stream of data comes from monitoring tools such as Zabbix, Nagios, Solarwinds, and higher level frameworks:
I love mountains. Some of my dear ones say that this is only because they resemble histograms, which I love more. Not true (ha ha), but I must agree that visualizations done properly brings plenty of satisfaction. Histograms, when prepared
Suppose all you had was a history of user requests to the service desk. Would you be able to determine how many of those requests were honest?
I am looking at distribution of a certain data set (left). It has two peaks (this is called ‘bimodal’) therefore I suspect that those are two overimposed populations. How do I split the data, to rediscover the original two populations
Here is quite intriguing research with the data of our Sopra Steria IT operations (ITSM, AIOps, and Infrastructure Management). I’ve been faced with an interesting situation in an IT Applications Management project for a large corporate client. In such a