Visualize the Lifecycle of Your Tickets to Identify IT Process Inefficiencies

Visualize the Lifecycle of Your Tickets to Identify IT Process Inefficiencies
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Sometimes it pays to sweat the small stuff. Organizations predominantly use IT business analytics to view trends on the macro scale, but zooming in on the details can often reveal opportunities for streamlining processes and achieving efficiencies. For example, studying an IT ticket in isolation can uncover process deficiencies or exemplify why certain incident types tend to cause problems.

With a team handling several thousand incident tickets monthly, it is important to prioritize which categories of tickets may have avoidable inefficiencies. Possible relevant metrics to use include a high number of reassignments, high volume incident categories, or higher than average mean time to repair (MTTR). Using these filters, IT leaders can prioritize which tickets to isolate to better understand where IT inefficiencies, bottlenecks or common sources of friction occur. High-quality, customizable, ad hoc dashboards and visualizations are needed to then make these observations highly visible to all key stakeholders.

Tracking the lifecycle of IT tickets demonstrates how macro trends arise from actions — often unconscious actions — taken by IT teams and the automated systems they use. This information can then give IT leaders the buy-in they need to track down and solve troublesome business pain points.

Search for common issues within the ticket lifecycle

The purpose of isolating IT tickets via analytics is to be open to discovery. But it can also be beneficial to start from the perspective of possible ticket-related issues you could be looking for. These include the following:

  1. High rates of reassignment, possibly those with a circular relationship, i.e., the ticket bounces around
  2. Unnecessary assignment to specialized L3/L4 teams
  3. Unnecessary triage for recurring or low-level requests that could instead be automatically assigned or have an automated resolution
  4. Re-assignments to teams that tend to sit on the ticket for an extended period before resolving or kicking the ticket to another team
  5. High priority incidents not receiving the immediate attention they deserve
  6. High rate of incidents affecting business critical applications or services
  7. Redundant tickets
  8. Tickets repeatedly closed as unsolved

To identify these types of issues, appropriate metrics can help sift through and isolate those worthy of deeper scrutiny. Possible metrics that can be used to identify particular problem ticket categories include:

  1. # of reassignments
  2. Clusters of incident types assigned to specific high-level MIM team
  3. Easy-to-resolve requests by volume
  4. Ticket time between assignment and re-assignment
  5. SDFI (Service delivery friction index)
  6. Outage time or mean time between incidents for mission critical apps/services
  7. # tickets closed out because of redundancy, relationship to issue already resolved
  8. # tickets closed as unsolved

The right metrics compile tickets into problem categories and presents candidates for isolating individual ticket cases.

Visualizing a single ticket’s journey through IT business analytics

After sorting tickets into specific problem categories, you can begin to identify and highlight possible individual outliers or example cases of your worst performers.

This stage of the analysis allows you to examine common ticket issues from a ground-level perspective. It can reveal process-related IT inefficiencies in the form of a compelling narrative describing how one ticket was handled badly and how to prevent the occurrence of similar issues.

As an example, one Numerify client was having issues with their ticket resolution process and was not sure where to make targeted improvements. To reveal the possible cause, a “group hold time” metric was used, which showed the IT groups that held onto tickets without resolving them magnified by the volume of time overall that these tickets were held. One group held tickets for, on average, a staggering 80 days. 

Using a reassignment flow diagram similar to the example above, it was established that the ticket had a high rate of assignment and was held onto by the specific team for several days before being reassigned. This micro-level perspective illustrated that there were some process deficiencies that could have led to the ticket being improperly assigned or that the team in question may not have known the correct process to resolve the ticket more speedily.

Machine learning and artificial intelligence (ML/AI) can sometimes be used in these analyses to identify “red flag” ticket behaviors or problem indicators like open field entry text keywords (analyzed using NLP) that may have been overlooked. One epic group in another client organization, that had a high SDFI, had a pattern of database-related requests that would go unmet for extended periods of time. Had the group been able to promote their own database schema changes processes would have been swifter and more efficient.

Isolating tickets reveals opportunities to optimize the resolution process or understand the root cause of certain incidents that tend towards IT inefficiencies. It can also reveal a need to adjust the organization’s approach to accountability, or to invite deeper analysis of a problem category to uncover its full impact or potential root causes

Analyzing ticket lifecycle, therefore, gives IT leaders the POV necessary to instantly identify certain problem sources and their possible solutions from both a top-down and bottom-up perspective. The devil really is often in the details.

This is just one example of how you can use your IT process data to reveal inefficiencies and other opportunities. Learn more in our recent webinar: “Smarter Incident Management with NLP driven Incident topic clustering ”

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