There’s no shortage of hype around the transformative potential of artificial intelligence (AI). Our new blog series identifies three approaches to AIOps—diagnostic, assistive, and automated—and helps you to decide if they’re the right choice for your organization. First, let’s look at different kinds of diagnostic AIOps.
What is Diagnostic AIOps?
Basic forms of diagnostic AI have existed since the 1960s and 1970s, and today, these systems are central to AIOps (Artificial Intelligence for IT Operations). Put simply, diagnostic AIOps systems provide critical information about IT environments, which IT pros can leverage to make informed decisions. Let’s look at some common applications and assess whether they’re right for you.
Anomaly Detection Systems
Anomaly detection algorithms ingest millions of data points and can spot patterns that deviate from established norms, allowing organizations to pinpoint potential issues before they escalate. Network monitoring tools, application performance management systems, IT security incident detection tools, and systems health monitoring applications all tend to use some form of anomaly detection. One of its most useful functions comes in managing alert fatigue. Effective anomaly detection triggers alerts when a significant deviation occurs, helping ensure that IT teams can focus on genuine issues.
Is it right for you? If your organization processes large amounts of data and requires efficient monitoring to identify and address potential issues promptly, anomaly detection systems can be a game-changer.
Event Correlation Systems
Even with anomaly detection in place, encountering a host of distinct alerts can be overwhelming. Traditionally, IT pros had to conduct detailed investigations to determine whether they all stemmed from the same problem or if each required individual attention. Event correlation algorithms analyze and group related alerts, helping users to identify underlying issues more efficiently. These systems also work to provide solutions. If the tool discovers that a virtual machine is struggling due to under-provisioned resources, it can initiate a fix, perhaps providing a runbook to automate remediation (more on this later in the series). It may also provide manual instructions—for example, directing a technician to disconnect a cable from one port and reconnect it to another.
Is it right for you? If your organization experiences frequent and complex alerts from interconnected devices, implementing event correlation systems could significantly enhance your ability to identify and resolve underlying issues.
Root Cause Analysis Systems
To find solutions to problems, we need to understand the cause. Root cause analysis systems utilize advanced machine learning algorithms to delve into complex data sets to find the source of IT issues. By examining symptoms and correlating them with historical data points, these tools facilitate a more accurate problem-solving process. This not only helps in resolving current incidents but also provides insights that can prevent future occurrences. The outcome? A more stable IT environment.
Is it right for you? If your organization experiences frequent IT system issues and struggles to ascertain the cause, AI-powered root cause analysis can enhance stability and performance.
Predictive Analytics Systems
Machine learning: it’s all in the name. By analyzing data from past events, predictive analytics algorithms can work to anticipate issues before they occur. This not only helps in minimizing downtime but also optimizes resource allocation. IT pros can plan ahead for enhanced operational efficiency.
Is it right for you? Predictive analytics can strengthen your organization if it experiences frequent service disruptions or is seeking to optimize resource allocation.
Make Sure Your AI-Powered Tools Are Integrated
It's essential for the tools you use in your IT management to be closely integrated to avoid silos and fragmentation. When different systems don't communicate well with each other, important information can get lost or overlooked, making it harder to see the full picture of your IT environment. Teams might have to piece together data from various sources instead of having everything work together smoothly. By ensuring that your tools are connected and share information easily, you can:
- Make better decisions
- Improve collaboration
- Create more effective IT management processes
The modern full-stack observability solution will include all of these functions, which are closely integrated to allow you a coherent view of your environment.
Understand the Costs and Benefits of Integrating Diagnostic AIOPS
The use of AI and ML is a conscious choice that organizations must make, and it’s not a natural fit for every situation. Implementing machine learning involves many considerations beyond just creating an algorithm for specific data. Questions arise about scalability, the diverse contexts in which the solution will be applied, the necessary computing resources, the accuracy of the model (avoiding model drift), and the associated costs. The fact remains that today’s IT environments are simply too complex for IT professionals to manage without some form of AI assistance. The question is, how is it best deployed in your organization?
Stay tuned to the SolarWinds blog for our next article, tackling assistive AIOps. For now, learn how AI is changing tech recruitment.
