Are Your ITSM Operations Suffering from Bad Data?

Bad data monster
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You’ve probably already noticed that IT service management (ITSM) tools are often the focal point of ITSM-pro frustrations – with the high levels of tool churn still an industry constant. Thankfully, the more astute among the ITSM industry are quick to highlight that it might not be the tools – but rather issues related to people and even process. With possible root causes potentially related to: tool-implementation planning, design, and delivery issues; or the absence of organizational change management during the tool’s delivery.

Despite this, there’s definitely a lot of blame aimed at ITSM-tools when customer ambitions and expectations aren’t met – but is the ITSM industry collectively missing something else? With that something being: “bad data.”

It’s time that we looked at the issue of bad data, and thus this article – the first of three – considers the impact of bad data in ITSM and what your organization should be doing about it.

How Bad Data Hurts Companies

Bad data can cost companies dearly. I love this opening line from a 2017 “The Cost of Bad Data” article (although it might be a little over the top for ITSM):

“Bad data has bankrupted major companies, started wars, and even caused entire civilizations to disappear.”

Then a 2016 IBM statistic – that bad data costs the US $3.1 trillion per annum is pretty scary (and, sadly, there doesn’t seem to be a more recent estimation freely available).

It’s a very relevant issue and, as per my second paragraph above, why aren’t more organizations concerned about the quality of their ITSM data (and the impact it has)?

Bad Data is Not a New Issue

A decade ago, a Gartner newsroom article  stated that: “…‘dirty data’ or poor data quality is an often-overlooked business issue and it can have a large negative impact on a business.” With Gartner adding that:

“…data quality has many facets, including:

  • Existence (whether the organization has the data)
  • Validity (whether the data values fall within an acceptable range or domain)
  • Consistency (for example, whether the same piece of data stored in multiple locations contains the same values)
  • Integrity (the completeness of relationships between data elements and across data sets)
  • Accuracy (whether the data describes the properties of the object it is meant to model)
  • Relevance (whether the data is the appropriate data to support the business objectives)”

With the first two of these a good starting point for data-quality improvement.

It’s obviously good to start addressing the issue of bad data, but surely much more needs to be done to ensure that current instances of bad data are eliminated within ITSM tools and that measures are taken to ensure good data quality going forward?

Common Data Management Challenges

These challenges are what we commonly see with our customers – and they can be viewed from a number of different perspectives.

Starting with the data-owner POV (people such as business application owners, process owners, service owners, and technology owners), there’s often:

  • No design expertise
  • No information architecture
  • No technical ITSM tool knowledge
  • No reliable reporting
  • No data management processes

Then there’s the data-user POV (for instance, in design and planning, operations and maintenance, robots and automation, customer service, help/service desk, and partners). Here:

  • Data cannot be trusted
  • Bad data causes quality issues
  • Bad data impacts daily work
  • Bad data decreases performance
  • Bad data lowers customer satisfaction
  • Bad data impacts employee satisfaction

And then there’s the data-provider POV (for instance, in design and planning, operations and maintenance, robots and automation, and partners), where there’s:

  • No agreed data models
  • No instructions for data maintenance
  • No data management procedures
  • No reporting on missing data
  • No reporting on deviations
  • No help or support available

These three bulleted lists might look scary. But considering such challenges is a necessary step in understanding what could be adversely affecting your ITSM-related data quality – and then the activities and decisions that leverage the data.

There’s good news though – there are solutions to these challenges. And thankfully these solutions aren’t heavily reliant on manual effort. The ITSM industry has available to it tools and methods to drive better data quality. I’ll be writing more on this but, in the meantime, please do not hesitate to contact me if you would like to know more.

Product Manager at

Mikko is the Product Manager at JustinLabs for Data Content Manager, a NowCertified ServiceNow application for modeling, managing and auditing data; and has been working as a solution consultant and architect since 1999 prior to working as product manager. Most experienced in IT service management, ITIL, and IT4IT, together with multiple tool and best practice certifications.

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