The DCM Blog

5 Challenges to Address for Better CMDB Data Quality

By Pekka Korpi

August 16, 2023
5 Common CMDB Data Quality Challenges

Comprehending the impact of CMDB Data Quality, especially its absence, can be difficult. It is a big problem since poor data quality is often the main reason ITSM systems, like ServiceNow, don’t meet expectations.

We are deeply involved with data quality daily. Our tool, Data Content Manager, is built to address data quality issues in ServiceNow. While most of our clients use DCM to enhance the quality of their CMDB—often by aligning with the Common Service Data Model—DCM is versatile enough to improve any data on the platform and enforce data models for any use case.

From our experience working with organizations committed to data quality improvement, we understand the challenges of initiating this journey. Convincing stakeholders to invest in something intangible can be challenging. Nevertheless, while investments in data quality might not introduce immediate new functionalities, adequate data quality is essential to enable most capabilities of the ServiceNow platform.

Let’s look at some prevalent high-level challenges companies often grapple with concerning data quality in the CMDB and ServiceNow overall.

#1 Lack of Ownership & Commitment

Probably the #1 challenge organizations face in their data quality improvement endeavors: the absence of clear data ownership.

For instance, a Configuration Manager usually depends on numerous Business Application owners to oversee the data for their respective apps. Yet sometimes, these Business Application owners aren’t even aware of their responsibility to manage this data. Compounding the issue, they might not be frequent users of ServiceNow. So, they might not know what to do when asked to update their data, even if they wanted to.

To bring clarity to these roles and expectations, we recommend the C-O-P (Consumer – Owner – Provider) model:

  • Data Consumers are processes or individuals that utilize the data for specific objectives. They should articulate their needs, enabling Data Providers to cater to these demands.
  • Data Owners are responsible for overarching concepts, data models, and the processes to upkeep the data. Their role involves collaborating with Data Consumers and Data Providers to outline requirements, shape data models, and offer guidance.
  • Data Providers are the processes or individuals tasked with generating and maintaining data. They collaborate closely with Data Owners to satisfy the Data Consumers’ needs and often serve as domain-specific experts.

By leveraging this straightforward model, you can demystify roles and accountabilities.

Click here to learn more about establishing ownership in your CMDB.

Agreement

#2: The Pitfall of Overlooking Data Consumers

Building on the previous point about roles, we want to highlight a common oversight: neglecting Data Consumers. They might be hard to find or not perceived as experts in data quality, leading some to think their input is less relevant. This perspective is misguided.

Data Consumers can be found in all kinds of roles and processes. For example:

  • Service Desk agents who analyze initial root causes and correct assignments to second-level support.
  • Change managers who try to understand the impact of planned changes.
  • Problem analysts, who further investigate root causes and try to find permanent solutions to problems.
  • A Request fulfillment process that uses CI data to automate a workflow.
  • Financial Management trying to allocate infrastructure costs to correct Business Units or Cost Centers.

These Data Consumers interact with the data daily, making them the best judges of their requirements. Engaging them in detailed discussions is very important to understand their needs.

It is also essential to understand that there is no point in storing data in the CMDB that nobody needs or uses. If you can’t find a Data Consumer for a particular data set, chances are that nobody uses it.

When requesting resources and priority for the CMDB work, you need to know who the Data Consumers are and how their work, and the business, is affected by the data they use. It is necessary to gain traction and management support.

Read more about preparing a Business Case for Data Quality Management.

#3 Unrealistic Scope & Resources

Occasionally, we encounter individuals responsible for “delivering the CSDM.” Such a task evokes empathy from us, as the notion of a single person delivering “a CSDM” is a misconception on many levels.

While aligning with the CSDM might be project-based, it’s neither a one-off project nor something a lone individual can achieve. Aligning with CSDM is an ongoing journey, shaping how you structure data in your CMDB over time.

This alignment is pivotal for harnessing the full potential of the ServiceNow platform. If you want to enjoy the full capabilities of ServiceNow, now and in the future, you cannot ignore the CSDM.

It is essential to approach this alignment pragmatically since even reaching the initial ‘Crawl’ phase can be a substantial undertaking, demanding considerable effort and time.

We recommend taking small steps at a time. Begin with a committed team and aim for incremental achievements. Celebrate these minor victories, emphasizing their significance and impact.

An incremental approach clarifies the mission to the team and stakeholders. It builds momentum, making securing resources easier and establishing credibility for the journey ahead.

To facilitate this step-by-step approach, we’ve crafted a CSDM Content Pack to complement our Data Content Manager product. This Content Pack includes pre-designed Blueprint templates, ready for immediate use, and you can tweak them to fit your specific requirements.

Explore the CSDM Content Pack for Data Content Manager

Working Together

#4: Too Generic Data Quality Metrics

As we’ve already established, Data Quality isn’t a solitary endeavor; it requires collective effort. In larger organizations, the number of participants can be vast. For instance, with 1,000 Business Applications, there might be well over 100 designated application owners.

Surprisingly, many might be unaware of their duty to update the CMDB. Some might even be unfamiliar with the very concept of CMDB and may not know what ServiceNow is or how it fits into the picture.

We have already highlighted the Consumer-Owner-Provider model to clarify roles and engage stakeholders. However, there is another persistent challenge: Motivation. How can you inspire these individuals to update their data actively?

We believe the answer lies in personalization. While overarching data quality KPIs are available, like those showcased in Data Content Manager’s Audit Overview dashboard or ServiceNow’s CMDB Health, these metrics are too broad for any individual to influence noticeably. The vastness of the underlying data means that individual efforts barely make a dent.

However, with Data Content Manager, KPIs can be tracked down to the individual level. This granularity ensures that when someone completes their tasks, they immediately witness an improvement in their KPIs.

This approach significantly boosts motivation and paves the way for, for example, gamification and recognizing individuals based on their unique contributions.

Read How to Make CMDB Data Quality Personal.

#5: Vague, Ineffective Communication

When it comes to securing resources or rallying people, communication is paramount. However, the way you communicate can make a world of difference. Let’s compare two approaches to requesting resources for Data Quality improvement:

  1. We suspect our CMDB might have data quality issues, but we’re uncertain about the specifics. We believe aligning with CSDM is essential, but we’re unsure of the starting point. However, we’re confident that investing in this area will yield positive outcomes.
  1. While auditing our Business Applications in the CMDB using Data Content Manager and benchmarking against the CSDM Crawl Phase Requirements, we found discrepancies. Of our 1,387 Business Applications, 622 don’t meet the requirements, and 280 lack valid owners. Addressing these gaps is crucial to prevent potentially significant incidents, such as this example, that resulted in a 2 million loss.

The second approach is more compelling. The distinction lies in the precision of the problem statement. The problem is defined clearly by utilizing Data Content Manager to scrutinize a specific segment of the CMDB. This specificity, as highlighted in our earlier discussion on scope, means that the problem is quantifiable, allowing for progress tracking and goal setting.

You can perform an initial analysis in a non-production environment at no cost, provided the data in that instance is similar to production. We offer a free Guided Trial for this purpose.

Explore our Free Guided Trial.

Communication

Procrastination Doesn’t Improve Data Quality

In our experience, we’ve encountered a myriad of challenges organizations face. The ones discussed above are ubiquitous. However, we hear things ranging from a lack of awareness to a complete denial of problems. We hear,

  • “It’s too difficult,”
  • “We lack the necessary resources,”
  • “There’s no management support,”
  • or “We don’t have the skills or tools.”

Sometimes, although luckily not very often, people hope problems will magically resolve on their own.

Real or not, these challenges shouldn’t be barriers to initiating improvements. Addressing the five main challenges in this article is a significant leap in the right direction.

Data Content Manager assists in overcoming each one of them. It is always a good time to begin, regardless of your maturity level.

See, When is the right time for DCM?

As mentioned earlier, we offer a no-cost trial of our tool and are committed to ensuring that your Trial produces optimal outcomes with minimal effort.

Next Steps to Improve Your CMDB Data Quality

The next steps can be very straightforward. Schedule a private demo with us, and we’ll show you how Data Content Manager can turn your Data Quality efforts from sporadic firefighting projects into a systematic way of working where you find your data issues before they cause expensive problems.

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