By: Anindya Neogi
Originally Published on July 27, 2015 / Updated on March 28, 2018
In an IT environment, machines and humans contribute to generating huge volumes and variety of data. If we can tap into this data through Analytics, we can glean deep insights for the IT Operations users.
BigData in an IT environment consists of metrics, logs, transaction traces, events, tickets etc. Tickets are an interesting type of data in this environment and often a key source of insight because they contain a mix of machine and human generated data which accumulate over its lifecycle, from creation to closure.
Tickets are created in multiple ways – e.g. an end user may report a problem at a call-center via voice transcription or email, an event can be generated by the IT monitoring system leading to a ticket being auto-created, an Operations user may inspect a monitoring event and act on it by creating a problem ticket to be solved by a Subject Matter Expert (SME).
Depending on how it is created and the lifecycle, a ticket contains a mix of machine generated data and natural language descriptions and comments about the problem, how it is being handled, and even the final resolution. What if we could analyse this rich data to answer the questions of IT Operations users and help them to be innovative at work ?
There are 3 types of users who may benefit from the analysis of tickets. Let’s give them some hypothetical names for the discussion —
(a) Ann, an Executive responsible for running the Central IT or may be the Applications within a Line of Business in the organisation
(b) Joe, the Service desk analyst responsible for routing tickets to the right SME
(c) Mark, an SME responsible for troubleshooting a set of IT systems or Applications when he gets a ticket assigned.
Let’s dig into the questions they want answered —
Ann wants to see a dashboard of problem hotspots and trends that tell her a summary of where she needs to focus her resources to help make the IT environment more healthy. For e.g. she may find out that most of her team is spending time on network related issues currently, where she has less SMEs, or the database server for her critical application is increasingly impacted by storage problems, which means she has to spend more on storage procurement.