Big Data in ITSM

When we talk about big data in the world of IT service management (ITSM), we’re talking about two very different things: ​

  • Big data tools/services that IT provides to business – to number crunch business operations data.
  • Big data within IT operations – to handle and leverage complex IT operations data.

Big data services for business operations

In an increasingly competitive, data-driven world, business managers are looking for effective ways to manage and interpret business data, especially big data.​ Digitalized business operations, such as e-commerce websites and mobile banking apps, produce huge amounts of data, which makes managing them close to impossible by using traditional computational models.

​Business managers know that somewhere in this ocean of data there are valuable strategic business insights to be found – and they want to find them. ​Business managers​ need IT’s knowledge and expertise in analyzing and manipulating data to uncover this insight. Unfortunately, the data is too big, too fast, and too fuzzy for mainstream business applications to handle. So, new technology architectures such as MapReduce must be deployed.

This means new supporting infrastructure will be required. For IT leaders, it’s an opportunity to step out of the shadows and establish the IT department as a key contributor to the business by providing tools and services that will help businesses make sense of the data and gain strategic advantages. Thankfully, businesses can use the IT Infrastructure Library (ITIL® ) V3, which contains best practices for managing service life cycles. This can help IT plan out the development and launch and manage big data services that will be useful to the business. 

Big data within ITSM

Inside IT, the quantity and variety of data that ITSM has to work with has also exploded. Although it’s not on the same scale as big data in business operations (and thus doesn’t need new computational architectures to deal with it), there is still sufficient volume and variety to make it a real headache for IT. With the growth in the size and complexity of IT estates, the number of data sources and their types have also expanded in ​these ways:

  • IT asset data from numerous sources
  • More complex incident records, capturing data
  • Problem records
  • Change records
  • Service requests
  • Links between support ​life cycle records (incident/problem/change)
  • End user contact details
  • Knowledge articles
  • How-to videos
  • Social collaboration sessions
  • Instant Messaging (IM) sessions
  • A larger number of process models to support a larger service portfolio
  • Systems monitoring data
  • IT customer interactions from web and mobile support portals

The challenge for ITSM is to pull all this diverse data together and make it both available and useful to different IT teams and the broader end user community. ​Regardless of the size of data, gaining insight and value from data analytics is crucial to business development. By federating all ITSM data from different sources, the data can be stored, analyzed, and shared to gain widespread value across IT operations and the business. By gathering ​all data found across ITSM, business managers can analyze the data to generate actionable knowledge and wisdom.

IT leaders are looking for ways to improve IT performance, cut costs, and support business with new innovations. Service managers want to keep pace with the changing business demands. IT managers want to narrow down infrastructure problems early, so they can fix the problems before there is any impact on the business. Despite the challenges in big data, there are definitely many benefits of having good analytics​​.


The IT department faces two challenges, digitalization and explosion of data.​ Business units want new services to help derive strategic value from big data, while IT has its own internal struggles associated with managing diverse data from multiple sources. These are two different problems with two different solutions. To face this challenge, IT needs a combination of the right people (data scientists), processes (the ITIL service life cycle approach), and tools (big data tools and an ITSM solution that federates/analyzes data from multiple sources).

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About the Author: Shannon Lewis

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