Collecting accurate IT and Audio Visual (AV) support ticket data is one of the critical first steps to effective service and data analytics. Without accurate ticket data, reports and insights generated from the reports are unreliable or even wrong. Some members of our AV Managed Services team sat down to discuss how our team collects accurate ticket data for later use in our root cause analysis and client data analytics. The transcription of the video can be found below.


Hi there. My name is Travis Rodney. I’m the Technical Services Manager here at Mechdyne. I have a couple of colleagues with me. I have Cary Struebing here on my right Technical Services supervisor, and Arturo Leon, another Technical Services Supervisor here.

Today we’re talking about data analytics and the importance of that. So we’ve seen in some of our other videos that this is a very important thing for us. It’s one of our big differentiators. Today, I want to talk a little bit about how do we get the data and what’s so important about that information?

So, Arturo, can you tell me a little bit more about, you know, the data that we gather and how you make sure that that data is, is good information, because they talked about garbage in garbage out, right, we don’t want to have bad stuff going in.


So we really rely on our ticketing system here that we use, we actually do a couple of things, we take two approaches to make sure that we’re getting the right data, and one of them is training our teammates, making sure that everybody is using the same nomenclature and the same, putting in the similar detail notes.

Because at the end of the day, that that’s when we’re doing our root cause analysis that really helps us out and helps us define where, where the issues lie. One of the things that we do in our tickets, and all our teammates do is we use a CTS format. And the CT stands for I’m sorry, the C stands for the current situation, what, what is the current situation when the hardware is frozen is not displaying anything.

The T stands for troubleshooting, what troubleshooting steps that you take. And then the S stands for, if you couldn’t get the results, when are you coming back as pacific time data coming back to resolve that, to make sure that everybody in the team uses that format.

We do training to all of our teammates, once a month, just do a refresher. And we, we also have a process quiz that they take once a week to that refreshes that as well to to make sure that they’re using the right nomenclature, that they’re creating the right tickets as well, making sure that it’s an incident versus a work order.

The other thing that we do is we maintain and update our ticketing system. So we’re decommissioning and installing new rooms all the time we bring in new equipment, technology changes from day to day. So we want to make sure that all those manufacturers and products are in our ticketing system so that when teammates go and create a ticket, they they’re using the right  device, because we want to make sure that when it comes down to the root cause analysis that we’re pinpointing to that specific device if we need to.


Yeah, and, you know, thinking about what you’re talking about, I can imagine, you know, having those notes in the ticket is great. Also, like when you go to handoff from one technician to another, you know, instead of those two technicians having to talk and kind of be face to face or get in a meeting, if all of those details are in the notes already, that handoff to that technician is very, it’s very easy, all of the information is in the ticket.

So there doesn’t have to be a real heavy hand off, it’s just new technician goes to the ticket reads the troubleshooting steps in the setup, starting in step three of the troubleshooting tree, he now stops, you know starts in, in start in step seven, because he already knows that, you know, one through six have been done.

So great. So Carrie, what about you? What kind of things are you doing on your side, as far as you know, helping out with data analytics and making sure that that data is good and, and clean?


I think it all starts with our operational process documents. And these operational process documents there, they pretty much fit all of our clients, right? So you know, you can go to one client, and it fits the ticketing system that they have, you can go to a different client and also fits with them. And it doesn’t matter what kind of ticketing system they have.

And you know, our teammates can go to the process documents. And they can look at the examples. And you know, they could go off of that. And it tells you exactly how to create the ticket whether it’s an institution or a work order, or in some instances, a call depending on the ticketing system. And then you know, they can look at the nomenclature, what we call it, and they can follow the nomenclature and they input the tickets that way and that, you know, when you put the tickets in that way, it makes it much easier to parse the data.

And in the way the reporting is done, it parses the data and it just makes the reporting a whole lot easier.


Great. Well, that’s some great information about data analytics. For any more additional information, go to and learn about more of our services. Thanks a lot for viewing.

Do you need help collecting accurate Ticket Information? Contact our team today.