Dominik Gärtner, Head of Analytics at Consulting4IT, has over a decade of experience in the IT industry. Before joining Consulting4IT in 2021, he spent many years working in IT support, among other roles. That’s why he knows what it’s like to deal with a never-ending flood of incidents on a daily basis. In his latest article, he writes about the challenges of day-to-day support work, reservations about new software in the service desk, and why he believes data analysis is the most effective solution.
In this article, I’d like to share my knowledge and experience on how service desk teams can improve their service and streamline their work by leveraging IT data and analytics. I see almost every day with our clients the enormous value this adds, but I also experience the “persuasion” required to take this path. When you put it that way, it sounds absurd: Why does anyone need to be convinced to make their work easier?
Service Desk teams, I know how tough it is for you!
Well, I gained my first IT experience over the years working in support, so I know how things work there. As a result, I’m also familiar with the mental hurdles that service desk teams face when you try to discuss new tools with them. Work at the service desk is typically dominated by the constant struggle to keep up with the flood of incidents. Anything that even remotely suggests making the daily workflow more complex will not be well received.
In most cases, teams are already overwhelmed just having to use the ITSM tool to document incidents. After all, they really just want to provide help when problems arise; everything else is usually a bit of a hassle—but necessary nonetheless. On top of that, they almost inevitably need a solution for remote sessions—or two. Plus separate repositories for documentation, a knowledge base, email, MS Teams, smaller troubleshooting tools, and much more.
So I understand very well why we are often met with resistance. This is completely understandable and needs to be addressed accordingly.
That’s why I hold many discussions with service desk teams during workshops. I have them describe their daily workflow, examine the tools and methods they use, and then try to highlight where IT analytics can drive improvements through transparency and automation. One of the most popular topics: the collection of incidents that actually occur on a recurring basis and can therefore be addressed through automation. This is closely followed by the ability to obtain data from the client without needing a remote session. The last point was always incredibly annoying during my time in support. You receive a ticket and need more information to process it, such as logs. However, the requester cannot provide this information on their own, so a remote session is required. You request one and agree on a time for the following day. That appointment is then canceled at the last minute, and you find a new one, two days later. So the ticket has now been open for three days without anything significant having been done to resolve it. Frustrating for everyone involved. There’s a better way.
What is IT Analytics?
IT analytics refers to the active use of IT data. This involves the automated collection and processing of data using appropriate software, followed by the interpretation or analysis of the data by trained IT professionals with the necessary expertise. The result is knowledge that is used to improve our ability to act. In this process, data is not only collected from central systems but also specifically where IT services are consumed: at the client. This creates a new, comprehensive view of the IT infrastructure and services. Where server monitoring shows only one side of the coin, maximum transparency is achieved by including clients in the data collection. Even if you are not the operator of the central service yourself—as is the case with Microsoft 365, for example—you can still track how well this service is received by users and how it is being consumed.
What are the options for using it at the service desk?
I’m trying to help people understand that it takes creative energy to get the most out of data. Data is just data. It’s only when you develop ideas for how to use it that it becomes valuable knowledge. The good news is that some people have already come up with great ideas for how to use it, and we put those ideas into practice every day with our clients. So no one has to start from scratch with a blank sheet of paper. However, the possibilities are practically limitless.
Visualization of client data in the incident management process
Having IT analytics data presented in a structured format results in a significant improvement in the ability of each individual service desk agent to take action. The delivery and visualization of this data via software integrates seamlessly into the workflow and displays real-time data for the affected client. This display of specific client parameters can be customized. Data can be aggregated, thresholds defined, results displayed accordingly, and much more. The service desk agent can thus see the most important parameters regarding the client’s health at a glance. Questions about the PC the user is currently using are no longer necessary. The software answers this for us, as the answer to this question is already contained in the data. Is the user connected via Wi-Fi or LAN? Another question we no longer need to ask. Are they currently working from home or at one of our locations? We can see this immediately—no question needed.
I think that covers the basics. You don’t have to go to the trouble of gathering or requesting information; instead, it becomes available when you create a ticket. If the ITSM tool has the appropriate integration, you can even import it into your documentation with just one click.
When I was still working in support, I really wish I’d had something like this. Instead, I had to go through the tedious process of gathering information (think of the remote session scenario), then manually analyze it to draw my own conclusions. That meant, for example, comparing logs, screenshots, and live data all at once, only to conclude that the system was using the wrong network connection. I could have seen that immediately with IT Analytics.
Automation of recurring solutions
We’re talking here, so to speak, about the next stage in the evolution of the knowledge base. Known issues and their solutions aren’t just documented—they’re directly translated into automated actions. This means the service desk agent doesn’t have to search for them somewhere and then manually follow the steps; they simply click and wait for the result. This also has the added benefit that new employees can immediately contribute productively to troubleshooting, ensuring quality. The next step is to make these defined solutions independently accessible to the user via AI. The software detects a specific issue on the client, offers the user support through a prompt, and then simply executes the automated solution defined for that scenario.
“Such solutions can also be made available through a self-service portal, where users can initiate the automated process on their own.”
The Core – Support for Problem Management
Not every incident can be resolved immediately by the service desk. Some requests turn out to be more complex issues that require in-depth troubleshooting or even reveal a problem with far-reaching consequences that affects multiple clients. This is where the greatest value of an established IT analytics solution comes into play. By collecting data from all clients, this data can be used to accelerate error analysis and identify root causes. Comparisons can be made between clients—for example, to identify commonalities, uncover malfunctions in different components, and thus identify underlying issues.
An example might make the potential clearer:
A user contacts the service desk to report that they cannot use an application. An error message appears when the application is launched. The IT staff member has not encountered a case with this specific error before. Analysis and research yield no results. The ticket is therefore escalated to the next level and handed over to the application experts. The centrally deployed software shows no errors on the server, so the entire client database is accessed there, and the affected client is compared with the rest of the infrastructure. This might reveal, for example, that other systems are also affected, although no tickets have been reported for them yet. The only difference in the cluster of affected systems is that they have an older patch version of the OS and .NET than the rest. Perhaps there is a connection here that isn’t immediately apparent? An initial course of action could therefore be to update the patch versions.
To make it easier to understand, I’ve chosen a fairly simple example, but it should clearly demonstrate the potential. As a result, more complex issues can be resolved more quickly, and additional tickets can be proactively avoided. It’s a win-win for both IT staff and users: fewer tickets and faster resolution times.
No resources for IT analytics? No problem.
Even if you’ve recognized the potential and want to implement IT analytics, there’s often one major hurdle: resources. Time and manpower are limited and valuable assets in IT. Providing the expertise needed for data analysis and continuously developing that expertise isn’t exactly easy. Who has the time to proactively monitor data every day or provide ad-hoc support with analyses? After all, it means pulling firefighters away from current fires so they can focus on where fires might break out next. Hard to imagine.Managed service offerings provide a solution here. They offer virtually immediate access to analytical expertise and real added value from day one of use. Another benefit stems from the fact that the central service provider brings knowledge from a wide range of customer environments. As a single company, you could never achieve this on your own. Thus, a provider’s customers benefit from collective knowledge. Problems that arise with one customer and could also arise with another customer—because they share the same conditions—can be proactively avoided.
Conclusion: IT Analytics as a Smart Solution
The use of IT data through analytics helps solve problems by providing maximum transparency into complex structures. This transparency enables decision-making, which is then translated into targeted actions. Essentially, this is already what a service desk does today. Information and data are collected, interpreted, and analyzed, and measures to resolve issues are derived from this analysis. Without analytics, however, the process leading up to the derivation of measures is unnecessarily lengthy and complex—something that should be avoided in the interest of efficient resource utilization. Why make things unnecessarily difficult when current technology can make the work easier?
My message is therefore: “Work smart, not hard”—IT analytics is the smart solution.