How AIOps Enables Rapid Response to Operational Uncertainties

It’s a well-known secret that, even in the best of circumstances, IT systems, networks, and software applications are designed and tested to expected usage patterns and capacities. Some services get tested to spiking load conditions, like preparing e-commerce applications for the holiday periods or education applications for student exam cycles. These are expected business scenarios and strong IT teams get ready for them by stress testing, monitoring applications, configuring alerts, and having incident response procedures in place.

Recommended: Understanding IBM’s Role In The 5G Revolution: AiThority Interview With Joe Berti, VP Offering Management, AI Applications At IBM

But what happens when there is a sudden, unexpected surge in usage for one or more applications? This can come from a media event like, for example, your marketing team getting caught off guard when Oprah Winfrey mentions your product on television. Or, maybe your company completed several acquisitions, and there are many concurrent incidents for the network operations center to manage.

Then there is a major crisis like COVID-19, which created significant operational changes and uncertainties. Banks were faced with increased demand online as consumers seek l****. Government systems experienced substantial spikes as people file for unemployment. Corporate networks and systems were overloaded with everyone working remotely. And, unlike most IT incidents, the pandemic affects all global regions simultaneously, and for an extremely prolonged duration.

Strategies for Handling Volatile Operating Conditions

IT departments are accustomed to managing the unexpected, but there is a new reliance around digital business continuity that most were not prepared for. When faced with unpredictable conditions, the best teams consider both a defensive and offensive strategy. That’s exactly what’s needed when it comes to monitoring infrastructure, multi-clouds, networks, databases, services, applications, and IoT devices during uncertain times.

Moogsoft Publishes “Observability With AIOps For Dummies”

CIOs and IT leaders looking to boost IT’s effectiveness are evaluating  AIOps platforms to improve incident resolution times and assure the performance of their strategically important applications.

Now, there are a bunch of different tools and platforms claiming AIOps capabilities. As with other enterprise tools, the differentiating ones leverage existing infrastructure, play well with others (vendor agnostic), are fast to configure, easy to use, and quick to demonstrate business impact. Best of all, they help make human processes smarter and faster. The mix of open box machine learning capabilities and actionable platforms can provide IT engineers the data, tools, and confidence to handle new uncertainties and risks.

This is why AIOps is becoming a central part of IT strategies, but it’s not the first step.

That strategy should start with centralizing operational data and alerts and doing event correlation across different IT domains. Instead of individual systems storing information and sending out alerts to different people, centralization allows all the data to be accessed, cleansed, correlated and analyzed in one place.

The Impact of Centralizing IT Operational Data

Let’s look at the value of centralizing IT operational data from two vantage points.

Even when we focus on a single application or business process, its IT operational data is captured in many different cloud events, monitoring tools, and log files. That’s because many applications today connect to various services, databases, and other integration points that should all be monitoring and logging. Just having one place for everyone working in IT operations to go and inspect this data reduces effort and complexity when incidents occur. In addition, it creates a level of transparency when everyone responding to incidents can review the same view of the data.

BigPanda Achieves AWS DevOps Competency Status

Second, by centralizing the data, it enables the use of open box machine learning algorithms to process the data in real-time. Alerts and information from multiple systems are sequenced and then correlated. By the time someone becomes aware of the issue, the event correlation algorithms are already done, and teams have a comprehensive view of what occurred.

Third, by centralizing the data over extended time periods, it enables machine learning algorithms to highlight trends that might cause degraded performance. A common example is, a database slowly growing in size may degrade query performance slightly, but the impact may be more significant in specific applications that query this data repetitively.

Improving Recovery Times and Root Cause Analysis  

But the real business value in centralizing IT operational data is when the AIOps platform enables IT to resolve issues faster, find root causes, including root cause changes, easier, and communicate with impacted users more effortlessly.

Tradier Teams With Q.Ai, A Forbes Company, To Bring AI And Big Data Analytics To Customers And Retail Investors

CIOs and IT leaders struggle with maintaining high performing business systems and responsive IT teams. IT leaders invest in multiple monitoring tools, IT workflow platforms, and a mix of ITIL, agile development, and DevOps practices. When there are major incidents, they’d like to have the means to resolve issues quickly, but unfortunately, that happens infrequently.

When multiple alerts go off at the same time and signal different IT ops teams, coordination and collaboration are required to determine the cause of the problem and the appropriate procedures to resolve it. That all takes time and lengthens the recovery duration.

Centralizing the data is only as good as the platform that sits on top of it. It needs to simplify spotting issues, make it easier for IT operations to recover, streamline incident management workflows, trigger the automation of recovery steps, enable communications with impacted users, and provide the analytics to perform root cause analysis.

This requires a platform that goes beyond just centralizing the data. Here are some additional requirements for AIOps platforms:

• Open box machine learning algorithms should enable knowledgeable engineers and developers to configure how the algorithms make sense of data coming from different environments.
• Artificial intelligence should correlate the oversupply of alerts coming from multiple monitoring tools into a time-sequence of major events that highlight potential root causes.
• IT operators should be able to use consoles to analyze the data efficiently and trigger the appropriate procedures to resolve issues.
• Integrations and automations with ITSM, development, and collaboration tools ensure that people are updated frequently and seamlessly during an incident and its postmortem.

Centralizing Operational Data can be Implemented Incrementally

To some folks, centralization sounds scary if you equate it with doing a lot of upfront work before you can take advantage of the data and capabilities. But to create a centralized AIOps platform, CIOs shouldn’t need IT scripting steps to centralize log files, developers coding to connect APIs, or DBAs supporting proprietary data lakes. Once the data is centralized, it shouldn’t require data analysts building operational dashboards or data scientists experimenting with machine learning algorithms.

Collaboration Between Marketing, IT And Engineering Can Tell The Full Innovation Story

Instead, CIOs and IT leaders should select platforms with built-in integrations and then have IT teams leverage agile methodologies to quickly get value from them.

Using out-of-the-box integrations implies that the platform can start pulling in data and alerts without a lot of development or configuration work. It should connect to commonly used monitoring tools such as AppDynamics, CloudWatch, Datadog, Nagios, New Relic, Splunk, and SolarWinds. Once the data is centralized, it must support integrations with collaboration tools like Jira, Cherwell, PagerDuty, ServiceNow, and Slack. Lastly, it must also be connected with devops tools that can trigger environment changes such as infrastructure as Code platforms like Ansible, Chef, and Puppet, and CI/CD platforms such as Jenkins.

Once there are baseline integrations, agile IT operation teams should look to configure the machine learning algorithms, automation, and communications for each application. An agile process should start with the most critical applications more susceptible to variable and changing usage patterns.

An agile IT team leveraging machine learning to recover from issues faster? That sounds like a team playing both offense and defense with one playbook and ready to respond to uncertain times.