AI and Automation in the NOC: A Guide to Current and Future Capabilities

Right now, troves of raw data sit in digital warehouses begging to be analyzed and distilled into clear instructions—instructions that would be more accurate and complete than a human engineer could ever be expected to create consistently. Instructions that would enable those humans to devote their attention to more important projects while helping to better prevent issues that threaten IT infrastructures and the businesses that rely on them.

AIOps—Artificial Intelligence for IT Operations—is already hard at work in the NOC doing just that. This very second, machine learning systems are plucking critical data points from the massive volumes of data generated across a typical IT environment and then marrying that data with automation to act on it—performing basic tasks that, until recently, only human engineers could do themselves.

For a few years now, INOC has been applying automation to take on the repetitive, low-risk tasks that pull technical specialists away from more important (and frankly more exciting) work. More recently, we've started started arming ourselves with vastly better data processing and machine learning power to augment and replace more—and more complex—manual tasks traditionally handled by humans.

Perhaps the most impactful recent advancement is AI-driven event correlation. We can now let machines correlate event data much faster than humans ever could and identify the subtle indicators of approaching issues within a torrent of otherwise noisy data. The outcome is measured in significantly faster and more proactive response rates—and thus, happier customers and end-users.

For example, here are just a few outcomes we've recently been achieving for our supported NOC clients thanks in part to our investments in applied AI and automation:

This combination of automation and machine learning brings the power and promise to genuinely transform how IT operations teams organize and operate. And as time goes on, automation will steadily continue to replace even more manual activities better suited for machines.

To assuage the lingering and understandable anxiety of human replacement, let’s be clear: the humans won’t be gone. They’ll just be freed from support tasks they’d likely rather not do anyway to focus on solving bigger problems and work on revenue-generating projects.

As a NOC service provider using these tools ourselves, we’re often asked whether it’s possible yet to build a fully autonomous or “dark” NOC. The answer today is still no. But the capabilities available today—the products of progress that have been made in pursuit of a more autonomous future—are already powerful enough to dissolve many of the human and technical limitations underpinning long-standing challenges to running a high-performing NOC operation. Short of a fully autonomous NOC, the value AIOps brings right now is immense and growing rapidly.

Here, we explain exactly how AIOps is helping NOC teams overcome some of the biggest operational challenges standing in their way right now. Rather than repeating the typical hype and hyperbole about AI, we bring the discussion down to earth and into the modern NOC, giving you a clear and concise explanation of AIOps in the NOC support context and what your organization likely stands to gain from applying its power to its support function.

📄 If you'd like a deeper dive into automation/AIOps in the NOC, we invite you to download our free white paper on the subject below or request a NOC consultation with our Solutions Engineering team.

Watch an excerpt of our webinar with BigPanda on how AIOps is being applied within the NOC (watch the full webinar here):

What is NOC Automation, Exactly?

First, a brief primer on the concept of automation in this context:

Machine learning enables us to expand what can be automated beyond the realm of the basic and repetitive. With its incredible processing power and the ability to “learn” as AI can, it automates—either fully or partially—more “knowledge-based” activities, such as:

• Correlating alarm data
• Generating and enriching tickets
• Managing notifications and escalations

Thinking Clearly About the Autonomous NOC

The promise of what AIOps might (and likely will) be able to do in the future can blur the expectations of what’s actually possible today. 

Let’s be clear about what’s currently possible and what has yet to come:

While AIOps holds the power to deliver many new capabilities, the immediate applicability in NOC support takes the form of augmenting support processes by automating low-risk tasks and improving the accuracy of others.

Just how big of a role these tools can play is also an important point around which to set reasonable expectations. It might be tempting, for instance, to expect AIOps to run support operations autonomously. A full NOC operation—24x7 staff interacting with multiple internal teams, each offering a variety of skills and customer knowledge, and a network of third parties (cloud and SaaS providers, data centers, circuit providers, field support)—still plays a substantial role in maintaining infrastructure availability and performance to ensure customer satisfaction. 

If a fully autonomous future is even possible, it’s far enough in the distance to disregard for now. Yet, as machine learning tools consume and process more data, they will inevitably become smarter and more capable, allowing for more in-depth analysis while pinpointing issues more quickly and predicting problems sooner.

New Solutions to Old Problems

A 2018 survey by 451 Research found that 64% of IT infrastructure teams saw their workloads increase from the previous year. Studies like this and our observations from speaking with IT professionals each day make it clear that as environments get larger and more complex, workloads continue to grow without a corresponding increase in the resources needed to manage them. 

Part of the excitement surrounding AIOps isn’t just that machines can now take on this growing workload; it’s that the machines can do some of that work significantly better and faster than humans ever could, thereby solving some long-standing problems that have plagued NOCs for years.

One of those legacy problems is alarm noise. AIOps can analyze and correlate millions of data points—many more than any human team ever could—to dramatically improve the signal-to-noise ratio, reveal real problems, and intelligently group events to isolate the root causes.

Another legacy problem AIOps helps solve is outage prediction and prevention. With the ability to analyze so much data, AIOps can identify subtle anomalies and recognize patterns that would otherwise pass right through the fingers of human engineers. With their incredible processing speed, these tools can pick out subtle indicators of impending problems to help predict and prevent them before they occur. It’s a genuinely transformational capability that can save untold amounts of money in prevented outages.

Taken together, these capabilities remove the hurdles standing in the way of the NOC doing its primary job: improving uptime and performance, accelerating incident response, and preventing outages—all while simplifying the NOC operation itself.

How AIOps is Enhancing Core NOC Processes Today

Nearly every imaginable operational issue within the NOC is rooted in at least one of its foundational processes:

• Event Monitoring and Management;
• Incident Management;
• Problem Management, and
• Change Management.

A problem in any of these processes can trigger a cascade of issues that impede the NOC from doing its job in others.

For example, if the NOC can’t operate efficiently, Incident Management processes can’t be executed quickly to detect and fix issues. Connecting event data with past configuration changes becomes impossible. Ultimately, valuable opportunities to improve infrastructure and application availability and support operations are missed. The resulting costs are high, both in financial expense, customer reputation, and team morale.

By tapping into analysis capabilities that far exceed what even the best human experts can achieve and automating certain tasks, AIOps eliminates the underlying inefficiencies that cause so many operational problems while bringing a whole new level of data analysis capacities. The patterns it reveals within torrents of data across an entire IT environment aren’t vanity metrics; they provide clear, actionable intelligence to inform NOC support decisions.

Now, let’s unpack the value of AIOps by explaining what advantages it can bring to each of those core processes.

Overcoming Key Operational Challenges

In addition to looking at AIOps through the lens of what the NOC does, we can also understand its utility through the challenges it can help NOCs overcome. 

Let’s explore five specific hurdles many modern organizations stumble over and how AIOps is poised to help with each.

1. Navigating hybrid, interrelated, and dynamic infrastructures

In today’s application-rich environment, systems have become more interdependent on one another. The result is a highly complex infrastructure ecosystem: a combination of traditional and cloud applications and infrastructure, along with technologies such as containerization, serverless computing, microservices, and orchestration tools.

Add in network technologies such as SND and NFV, and you’ll quickly find yourself in an environment with multiple devices and systems and high levels of interdependence to support various organizational services and applications. A single outage in these interrelated and interconnected environments can reduce organizational knowledge and prevent engineers from accessing needed systems. 

Without good visibility, pinpointing the root cause of incidents—let alone understanding the overall impact of an outage that spans multiple environments—becomes impossible.

2. Reducing alert noise 

NOC engineers typically field—and can often be overwhelmed by—both genuine alerts and many false positives. High alert noise distracts support teams from focusing on real issues. Over time, alert fatigue sets in. In the best case, the team takes a long time to find the actual issue. More than likely, however, too many alerts have the same effect as no alerts and are simply ignored.

Also, most threshold alerts are static. For example, an alert may be created if CPU usage is above 90% for five minutes. A lack of contextual awareness (batch job execution, for example) for threshold alerts leads to creating unnecessary incidents, adding to the NOC staff workload without improving infrastructure availability.

High alert volumes ultimately result in the same situation: higher MTTR and a poor reputation for the NOC, as the critical alerts are not acted on quickly.

3. De-siloing systems and teams

The typical enterprise (or IT-complex) organization uses multiple tools to monitor and manage their complex environments, each collecting IT operations data and retaining that data in silos. (This may happen because DevOps teams use tools specific to their environment or because new tools are added when new technologies are adopted.)

With these separate data environments, it becomes very difficult to understand underlying infrastructure or application issues in current technology stacks with high levels of interdependence; siloed systems make centralized insight impossible. 

Organizations expend significant resources to maintain these disparate systems yet continue to lack a single-pane view for monitoring and managing incidents. Typically, each support team within the organization will investigate issues independently. The NOC then combines these teams’ tools and processes, eventually coalescing the data and analysis to determine the root cause. This leads to significant delays in incident resolution. Senior management escalation and involvement in troubleshooting are common in these siloed environments, causing significant loss of productivity for the organization.

4. Breaking through the limitations of traditional ITOM systems

Traditional ITOM systems depend on an accurately populated CMDB with clearly defined relationships and dependencies between parent and child configuration items. 

However, in most support organizations, the CMDB quickly becomes out of date, given the usually incomplete implementation of reliable change management processes and procedures. Thus, depending on the CMDB when executing NOC processes such as Event Monitoring and Management and Incident Management is fraught with inaccuracies.

How We've Integrated AIOps and Automation Into Our NOC Support Platform: Ops 3.0

The platform of tools and systems for delivering service is a crucial factor in the success of any high-performance NOC. It’s what enables the NOC’s speed, accuracy, and consistency. At INOC, we’ve significantly invested in developing and refining our platform to deliver comprehensive NOC services to clients and partners 24x7x365. 

Our latest and third major iteration of the platform is INOC Ops 3.0.

This platform serves as our operating system—the intersection of technology, operations, and service delivery. Its design enables us to ingest alarm feeds from various sources, auto-correlate those events into a single ticket, and present that ticket through a single pane of glass for efficient Incident, Problem, and Capacity Management.

In short, the Ops 3.0 platform enables our team to increase its accuracy and speed while reducing delays in human involvement. It frees NOC engineers to spend less time in the runbook and more time on strategic client projects.

Below is a high-level schematic of the platform and how it connects to — and integrates with — a client's infrastructure and existing NMS, ITSM, and communications tooling.

Open larger image in new tab »

The workflow generally moves from the left to the right of the diagram as monitoring tools output alarm and event information from a client NMS or ours into our platform, where a number of tools process and correlate that data, generate incidents and tickets enriched with critical information from our CMDB, and triage and work them through a combination of machine learning and human engineering resources. ITSM platforms are integrated to bring activities back into the client's support environment, and the system is integrated with client communications.

While our platform has many parts, its overall function can be summed up in just a few sentences:

Here's a simplified visual representation of the platform to reflect the incident lifecycle:

At the core of INOC's platform is a powerful AIOps engine that serves as a sort of central nervous system for alarm, event, and incident management. This system leverages machine learning to automate routine tasks and extract meaningful insights from vast datasets. It performs multifaceted analysis, including alarm correlation across various sources, deep alarm inspection, and data enrichment using CMDB information, enabling more informed and rapid response to incidents.

Here's a quick list of the key ways we've AIOps and automations into our Ops 3.0 platform—reiterating many of the points we've covered in this guide:

Final Thoughts and Next Steps

Effective use of automation and machine learning technologies depends on understanding where to apply them within the multiple processes that a NOC supports. 

• Event Monitoring and Management is the obvious starting point, with AIOps helping reduce alert noise at the event analysis stage.
• Incident Management is enhanced when AIOps suggest the probable cause. 
• With the ability to analyze historical event, incident and performance data, AIOps can identify possible root causes in support of Problem Management.

A successful AIOps initiative provides customers with a much-improved experience, but implementation requires a strong foundation in NOC best practices and a well-developed organizational structure. This includes good knowledge management and training practices.

For AIOps solutions to become more intelligent and truly useful over time, resulting in a more automated NOC, domain expertise—operational and technical—needs to be “encoded” into the tools; logic needs to be reviewed, tested against results, and refined constantly. A continual service improvement program that includes quality control and assurance and detailed operational and technical reporting insight is key to getting good value from the AIOps solution.

A centralized monitoring and incident response team can serve as experts and offer high-quality support to dynamic organizations. Such a team can provide centralized management of these tools, developing standard responses—including automation—to incidents using AIOps where possible. Proper application of AIOps in the NOC can reduce event noise, help identify and resolve incidents quickly, and prevent problems from impacting customers

Wondering how your organization can benefit from AIOps? Download our free white paper to learn more about what we cover here and get a worksheet you can use to contextualize the advantages. When you’re ready to take the next step, reach out to schedule a time to talk through these insights and opportunities.