When it comes to implementing AIOps tools successfully, the key is using quality data for specific objectives.
Traditional network monitoring tools are great for providing IT teams with real-time status alerts. But that gift can feel like a curse when IT teams are left scrambling to address alert storms and manage massive data streams. That’s where artificial intelligence for IT operations (AIOps) can make a difference.
With the power of big data analytics and machine learning, AIOps can actually filter through and respond to that data, resolving issues, automating routine operations, and reaching insights beyond that which human engineers can provide. Although AIOps is still in its infancy, the success of currently-available tools already demonstrates that these capabilities will serve an increasingly crucial role in the IT toolbelt. It’s time for IT teams to take a look at their current operations, and figure out exactly how to make these new AIOps tools work for them.
The key to implementing AIOps is to understand the specific challenges your enterprise IT department is facing. AIOps is a still-developing technology, and no one tool will be able to execute on every possible directive — so you may need to prioritize your network goals.
Many IT teams spend an outsized amount of time estimating network capacity and scaling needs. AIOps can solve current resource allocation issues as they arise, and in some cases can actually predict future business needs based on historical patterns. The result is more easily configured workloads, as AIOps tools learn and calculate from scaling parameters like CPU consumption.
Understanding disruptive anomalies in the network can take up time for enterprise IT teams. But AIOps can actually learn to predict outages before they occur, based on weak signals that human oversight would miss. In some cases, AIOps can even pinpoint the root cause of the issue and automatically find a solution.
In general, AIOps excels when it comes to handling large amounts of data. At the same time, many business IT teams are overwhelmed with security alerts, which can easily become a “storm” of false positives. This volume sometimes exceeds what humans can handle, and AIOps is the best choice for sifting through these alerts, learning which ones are valid, and even resolving security issues without human intervention.
Despite the many advantages of AIOps tools, there are some challenges to successfully implementing an AI solution. One major potential pitfall is data quality. If you feed poor or limited data into an AI system, the results will simply be less accurate. It’s also unadvisable for different teams to work from siloed datasets, as this represents a missed opportunity to build a richer, smarter tool across the board.
The data quality issue often comes into play when businesses layer AIOps solutions over existing monitoring tools as a smart scanner. This can seem easier at first, especially if the enterprise has already made an investment in monitoring tools. But if you don’t have a reliable tool in place, it won’t be able to feed useful data to the AI. In addition, this piecemeal solution means IT will still have to manage alerting rules, and the AI will only sort through issues, instead of directly resolving them.
One challenge you won’t have to face? The worry that AIOps will replace your IT team. Currently, AIOps technology still relies heavily on humans for management and oversight. It will assuredly save enterprises both time and money, but it isn’t a hands-off miracle cure.
The best way to incorporate AIOps into your IT operations is to prepare. A thorough, expert network assessment will help your team recognize potential trouble spots, and may uncover unexpected opportunities to leverage AI capabilities.
In addition, IT will need to audit existing network data and ensure it will produce the results you need. These tools need access to historical patterns and an understanding of key indicators for both positive and negative events. What are the dependencies among network components? What can you glean from logs, devices, and API outputs? Even datasets outside of IT operations could be useful for helping the system learn and predict. Over time, the AI learns what to monitor for, and what kinds of automated responses are appropriate.
In general, the best advice is to start small and build in phases. Don’t expect AIOps to solve every problem you have on the first day. Start by teaching the system to perform a key capability, and pay attention to trouble spots in the process. When you start small, it’s easier to catch mistakes and recognize the ways in which your IT team may need to adjust their workflows going forward. Even if you at first leverage AIOps to take care of routine tasks like system updates or alert management, it can make a huge difference in your team’s workload.
If you’re ready to incorporate the power of AIOps in your IT operations, the prudent option is to work with a network professional who can help you select and implement the AIOps tools that are right for you. At Turn-key Technologies (TTI), we have more than three decades of experience helping IT professionals implement and manage new tools.
Our experts will work with your staff to make sure that your AIOps implementation is a successful one. With a host of products available, it’s crucial that you choose one that suits your IT team’s needs and capabilities. And perhaps most crucially, you should recognize that no single AIOps tool can solve every network problem. Talk to the professionals at TTI today and learn how we can help you achieve network solutions that meet your business objectives.
Please, rotate your device