In our previous blog, we introduced Service Desk Data Diagnosis, our take on building solid ServiceNow ITSM business cases to make work flow at your service desk. By analyzing data structures and quality from your existing systems, hidden patterns and opportunities come to surface that you can use to improve productivity and reduce costs. After analyzing thousands of incidents across service desks, our data experts have identified three problem areas—the hidden gems—and their solutions. These can really help you mature your service desk operations.
Service desk incident prioritization is traditionally done through assigning a ticket a low, medium, or high These prioritizations are typically also linked to SLA assignment, with resolve time expectations linked to the assigned priority. The system is relatively simple and clear in its intent, but it does not meet expectations when exposed to the complexities of active service desk environments. Data analysis performed on real-world service desk data sets has shown us that the vast majority—often over 90%—of incidents are medium priority. This has two causes. First, the medium priority is used as a catch-all category, covering a way too wide spectrum of topics and situations to properly reflect the connection between priority and SLA. It does not tell you whether an incident leans more towards medium-low or medium-high. This lack of detailed information actually leads to our second cause, as this translates to the service desk agents. A vague and unintuitive prioritization system causes agents to pick a default priority, in this case medium, for the majority of incidents. Low and High priorities are only used for outliers.
Solution: Implement an impact/urgency priority matrix
In order to properly document how each incident priority relates to other incidents, service desks should move towards implementing impact/urgency matrixes. These are provided out-of-the-box by ServiceNow and work with a more granular prioritization, including clear arguments for why a ticket is assigned a certain priority. This system gives service desk agents the opportunity to provide more information relevant to prioritization without being required to make SLA decisions.
Every service desk works with incident categories and sub-categories. These categories are intended to group similar incidents in clear, understandable categories. However, especially as the number and granularity of categories increases, separation and differentiation between categories fades. How do companies find the correct balance between granularity and clarity? Data analysis has shown that companies often suffer from having many categories that are barely used, as well as having clusters of very similar categories. The inflation of categories leads to higher onboarding costs and more complex knowledge management as intuitiveness of category selection decreases.
Solution: Introduce incident clustering & category prediction
The practice of manually defining categories and subcategories based on expert opinions often leads to the problems described above. A wealth of service desk data available at your fingertips offers opportunities to standardize incident assignment across departments. This will save time and resources as well as increase data quality and service consistency. Included in Service Desk Data Diagnosis (SDD) is an incident similarity analysis. This will cluster tickets by similarity to facilitate problem identification and possible resolution. A similarity analysis also provides insights into categorization quality and is used to correctly assign incidents to categories based on similarity. These data quality improvements set your service desk up for success, as they will enjoy better ticket categorization through ServiceNow’s Predictive Intelligence that predicts the assigned ticket category based on the categorization improvements from the SDD.
Free text fields for incidents allow service desk agents to provide ticket-specific information that cannot easily be captured in categorical or numerical columns. However, the freedom inherent in these text columns also causes data quality to quickly deteriorate once agents are forced to use these fields to capture structured information that is better suited for targeted text fields. This phenomenon is often found in ITSM systems that provide only a few options for customizing displayed fields to match defined workflows, leading to so-called ‘phantom fields’. These phantom fields are often part of scripts defined by management to aid agents in resolving tickets in a standardized manner where the existing system fails to do so. The inclusion of phantom fields adds unwanted noise to each incident and adds complexity in resolving these incidents.
Solution: Configure your ServiceNow instance to match your processes
When providing text fields to service desk agents, making these fields as narrowly defined as possible is the key to ensure consistency and clarity. Most Service Management systems, however, feature a limited collection of available columns that are standardized across customers. ServiceNow, however, is different. Adapting a dedicated instance to your organization’s Service Management processes allows the system to work for your agents, instead of your agents exerting effort to work with the system they’re given. A closer match between process and system removes the need for phantom columns and increases efficiency.
Taking advantage of the opportunities provided by analyzing existing data before investing in a ServiceNow implementation will lead to significant cost savings. Plus, it will set a strong foundation for your implementation. So, don’t wait any longer and let us help you uncover the hidden gems in your service desk data!
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IT Service Management