The DCM Blog

Is Poor Data Quality the Reason for Ineffective ITSM Tools?

By Mikko Juola

December 10, 2018
Business team blaming frustrate young colleague; negative emotion concept

The IT service management (ITSM) industry is full of stories about ITSM tools that haven’t lived up to customer expectations – with the constant high level of tool churn the visible symptom. There might be a variety of root causes for the discontent with such ITSM tools, from tool dissatisfaction (related to: ITIL-alignment, usability, manual activity, flexibility, or customization), through failing to deliver the anticipated benefits, to the tool being end-of-life or simply outdated.

Then it might not actually be the tools themselves, with possible root causes more people-related, such as: poor tool implementation planning, design, and delivery, or the absence of organizational change management during its delivery.

So, there’s a lot of blame being thrown around – to explain why ITSM-tool customer ambitions and expectations haven’t been met – but are we collectively missing something by blaming the tool (and occasionally people)?

With that something: “bad data.”

This article looks at the impact of bad data in service management and what your organization should be doing about it.

The Impact of Bad Data

Bad data really can cost. Take, for example, this glorious opening line of a 2017 “The Cost of Bad Data” article:

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

Then many of the highest-ranking (in Google) blogs related to bad data quote a 2016 IBM statistic – that bad data costs the US $3.1 trillion per annum. Sadly, a more recent estimation is hard to find.

It sounds scary. If not a little over-the-top for ITSM (and enterprise service management). But it’s a relevant issue and, as per my second paragraph, why aren’t organizations more concerned about the quality of their ITSM, or service management, data?

And Bad Data is Nothing New

A decade ago, you could have read this Gartner newsroom article which states 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 the article adding that:

“…data quality has many facets, including:

  • Existence (whether the organisation 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)”

And that the first two of these are a good starting point for data-quality improvement. But surely, organizations need to do much more in ensuring that current instances of bad data are squeezed out of their ITSM tool and that measures are put in place to ensure data quality going forward?

The Common Challenges for Data Management

These challenges can be viewed from a number of perspectives. Starting with the data-owner point of view (for instance, business application owners, process owners, service owners, and technology owners):

  • No design expertise
  • No technical service management tool (e.g. ServiceNow) knowledge
  • No information architecture
  • No data management processes
  • No reliable reporting

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

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

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

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

These bulleted lists probably look scary. But it’s a necessary step in understanding the things that are likely adversely affecting your service-management-related data quality and the activities and decisions that leverage the data.

The good news, though, is that there are solutions to these challenges. And solutions that aren’t heavily reliant on manual effort. We have the right tools and methods to get started with better data quality. So do not hesitate to contact us to know more.

Read more in this series:

Mikko Juola

Mikko Juola

Chief Product Officer at Qualdatrix


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