Panning for Golden Insights from the Historical Backlog

Pablo Vergne March 14, 2018
- 3 min read

On the surface, determining the status of your service desk historical backlog seems easy. But the scenario becomes more complex when looking at historical data for the week, month, or year. InvGate defines a backlog as “an accumulation of jobs waiting to be solved or completed at any given time”, i.e., pending incidents and requests.

Calculating accumulated pending jobs and requests is crucial to effectively managing your team, avoiding the chaos of a purely reactive support mode, and making sound business and employee-related decisions. The evolution of the historical backlog is unique to the individual team, and the value, or ‘gold’, is found within the intel of the data.

Recalculating the historical backlog

First things first - how do we correctly determine the status of the backlog on a given day or time? A typical mistake made by IT managers is inaccurately setting the starting and closing date for requests. For example, imagine that you want to know the status of your backlog as of last Wednesday. You might think Wednesday’s backlog is the difference between requests created and closed on Wednesday, but this statement is not 100% correct. You may have created five requests and closed seven, because some of the jobs completed were generated prior to Wednesday. According to this calculation your backlog for Wednesday would be -2, but it’s clear that this is a completely inaccurate and illogical conclusion.

 So, how can we dig into the data analytics to find the number of open requests by Wednesday regardless of the creation date? To get the accurate metric and avoid common IT mistakes, we must measure all open requests accumulated at a given point in time. 

A complicated calculation

The number of accumulated open requests at any given point requires considerable technical difficulty. Depending on how granular you want the data to be - by day, time, segment, or service - it can change the results of the data collection.

Imagine the following common scenarios:

  • How do I calculate the backlog for another time frame other than today?
  • What if I need data for all the requests on a given day?
  • How do I check backlog fluctuations throughout the week?
  • What if I want the data segmented by hour, or agent responsible for the task?

This order of the chronological data generates the historical backlog within the help desk software, and diving into the historical backlog allows you to sort through requests (open and closed) and determine trends, if SLAs are being met, and what employee or process changes are needed (change management) to become more efficient. This order of chronological data generates the historical backlog.

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Where’s my gold?

Your backlog isn't just about open and closed requests. The data speaks to your team’s efficiency and uncovers common pitfalls and mistakes. Reviewing the data on a regular basis helps your team’s performance assessment. A smaller backlog means less pending requests and therefore more jobs completed, and would indicate a high level of efficiency within your team. If your historical backlog grows exponentially, it’s a red flag that there are issues within the department or perhaps with the processes.

The historical backlog is a gold mine as it enables us to understand the past and the current status of our accumulated incident ticket list. Accumulation of open jobs means a proportional resolution time, a marked drop in resolution quality due to ‘fire fighting mode’ (reactive support), and even lost revenue. Understanding these data trends will help you make better key IT management decisions such as reallocating requests or analyzing improvements to the resolution process to avoid scenarios similar to the one described. Harnessing the power of historical data allows better future planning and ensures firm strides on the road to success.

How do you measure and report on your IT historical backlog? Would you add anything to our list? Please let me know in the comments! 

Read other articles like this : Reporting, ticketing system, Service desk, Incident Management, Change Management, data analytics

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