Ticket Category Tsunami Warning

Previously, we discussed life-cycle analysis as a data blind spot in service management.

If you’re like most organizations, you’re currently tracking the top 10 ticket categories each month. You may even be comparing the top 10 categories for this month against the top 10 categories from last month. And if you’re really into metrics, you may have expanded that top 10 list into a top 20 list (or more).

One thing you’ve probably noticed is that your top 10 list doesn’t change too much month to month. Password resets, printer issues and that good ol’ reliable critical business application generating the majority of your tickets tend to be perennial mainstays of the top 10 lists of service managers worldwide.

And yet your service desk isn’t able to run as if on autopilot in spite of the fact that you know these top 10 categories, is it? While your top 10 list is important, what’s more important is the category or categories of tickets lurking in the shadows, growing behind the scenes, and eating up more and more of your analysts’ time — all while being undetected by you and everyone else as a potential problem … or, more optimistically put, an area for improvement.

Ask yourself the following:

• How many new categories have appeared in your top 10 list not just in the past month or even year, but in the past 5 years? Have you seen some new ones appear as technologies have evolved, etc.?
• What’s created a bigger problem for you in the past: an issue you’d been consistently tracking in your top 10 list, or something that seemed to surface suddenly and without warning?
• Would you be prepared if your 11th ticket category generated nearly the same number of tickets as your 10th category?

Never forget that the top 10 list you’re looking at is a retrospective of 30 days, so if something new were to pop up on your list this month, not only would it really catch you by surprise, but it would already be a full-blown problem, not just an issue you could’ve headed off at the pass. On top of this, there could be many more “silent” categories growing in size and significance in the meantime that are not breaking into your top 10 list … yet.

While the top 10 ticket category metric is better than no metric at all, the better metric to watch is your top 10 ticket categories by growth. This calculation takes the number of tickets received in the most recent past period, compares that with what’s been received in the current period, then calculates the percentage increase in each of the various categories.

By watching this metric, you can glimpse into a sort of service-desk crystal ball and observe a problem that might be off the radar now but could easily manifest itself as a tsunami of tickets in the future.

Case Study

A midsize education organization was preparing to roll out a new set of desktops as part of its three-year infrastructure-renewal plan. Given the large number of desktops that had to be replaced, the desktop team decided to perform the refresh in chunks, rolling out about 100 desktops per week at remote sites and planning to significantly increase that number when refreshing the larger locations.

Unbeknownst to the desktop team, the new hardware had a device-driver conflict with a software application that was used by a small but important subset of users. Each time desktops were refreshed, the help desk would get a few phone calls regarding this issue.

Had the rollout continued as planned, when the refresh at the larger locations took place, the help desk would have been caught off guard, flooded with requests related to this issue.

All the while, however, a proactive service-desk manager was not only craftily monitoring which ticket types were most common in the environment, but also calculating which types were seeing the most growth.

It was quickly discovered that this software application, which only generated a few calls per week prior to the rollout, was suddenly generating a number of calls that was growing by leaps and bounds.

With a little research into ticket history and a chat with the desktop team, it was discovered that the new desktops were causing the problem. A solution was developed and implemented, and future desktops were replaced at the larger locations without impacting the application or the service desk. Ticket tsunami avoided.

In the next chapter, we will talk about how monitoring your metrics can give you a data blind spot in service management.