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ITOps vs. AIOps
First things first. AIOps is artificial intelligence for IT operations. In short, AIOps uses AI, data, and machine learning to automate IT operations. This includes event correlation, anomaly detection, and root-cause identification.
Short for information technology operations, ITOps ensures that an organization’s technology services run smoothly. ITOps includes implementation, management, delivery, and support of IT services. ITOps encompasses multiple — often siloed — functions such as network management and technical support.
AIOps platforms apply AI, big data, and machine learning to enhance efficiency and automate routine tasks, allowing skilled teams to focus on complex issues instead of manual work. AIOps encourages visibility and data sharing across teams, helping to eliminate silos and reduce the need for specialists. In essence, AIOps brings intelligence, flexibility, and agility to ITOps.
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Use AIOps to manage growing IT complexity
Use AIOps to automate manual and routine activities, allowing you to scale capabilities without increasing staff. Data volume and service complexity continue to grow as technology evolves and customers demand more services. As you undergo digital transformation to reap the scalability and cost benefits of cloud and hybrid-cloud environments, use AIOps to help support alert management, incident management, and service availability.
The rate at which data volumes are increasing isn’t slowing, making it nearly impossible to evolve while maintaining high levels of service availability. The risk you face: Incidents go unnoticed in the sea of data and system alerts.
Organizations embrace AIOps so their IT teams can spend less time finding and resolving IT incidents and more time focused on initiatives to drive innovation.
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How does AIOps work?
AIOps works by ingesting data from multiple sources and using advanced machine learning algorithms to perform triage and analysis. During triage, the system eliminates the “noise” in the data to identify and group data into suspicious events. This facilitates anomaly detection, allowing IT teams to identify potential incidents before they become outages. AIOps automatically escalates alerts, providing contextual insight into how to address them quickly, significantly reducing downtime.
AIOps performs event correlation, collecting and analyzing observability, topology, and change data. This incident intelligence allows teams to identify problems in real time to prevent and resolve outages proactively. The result: streamlined processes, maximum task efficiency, and fewer outages.
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Characteristics of AIOps platforms
When evaluating AIOps, consider the six critical characteristics of AIOps platforms.
Be sure your AIOps platform:
- Ingests data from all observability, monitoring, and change data sources
- Provides topology that captures dependency mapping from sources, including change-management databases (CMBDs)
- Correlates related events associated with an incident
- Detects incidents in real-time and surfaces the probable root cause
- Helps define and perform remediation activities
- Provides detailed analytics and reports for continuous improvement
AIOps platforms with these characteristics can help ITOps, NOC, and SRE teams detect, investigate, and fix incidents before they escalate to outages that impact end-users and customers.
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Primary use cases for AIOps
Get the most from your efforts to use AIOps to optimize IT operations through AI-driven insights and automation. Some of the most popular AIOps use cases include:
Aggregate and add context to monitoring data
Enhancing the efficiency of ITOps, NOC, and SRE teams depends on gathering data from a variety of monitoring tools, whether commercial or custom. Alone, monitoring tools often produce overwhelming and unclear alerts. AIOps platforms streamline their output by filtering out the noise, deduplicating, and normalizing the data. AIOps further enhances the data’s value with operational context often missing from the original alerts.
By consolidating the refined data into a single actionable alert, AIOps eliminates the need to juggle multiple monitoring systems, saving time and reducing redundancy. With the ability to track planned and unplanned system changes from sources, including CI/CD and change management tools, AIOps helps identify changes that might cause IT disruptions.
Enhance CMDBs with topology data
Complex connections between nodes, servers, network devices, and applications make it challenging for ITOps, NOC, and SRE teams to distinguish between related events and identify root causes from symptoms alone.
AIOps platforms ingest topology data from diverse sources, including CMDBs, application performance monitoring (APM) maps, and virtualization tools. Given that CMDBs are often out-of-date, it’s crucial for AIOps to access a broad range of data sources.
Once integrated, AIOps creates a detailed topology model and updates it regularly. This proactive updating is vital to maintain accuracy. An outdated model can hinder timely incident detection. For example, a lapse in recognizing a system configuration change can escalate from a minor issue into a significant service disruption. Imagine if you managed a hospitality organization and your booking system became inoperative during peak demand. Now, imagine the significant setbacks, customer dissatisfaction, and potential revenue loss.
Perform event correlation
Event correlation becomes pivotal once you’ve gathered, cleaned, and aggregated ITOps data. A central element of AIOps platforms, event correlation uses AI and machine learning to analyze data and identify connections between alerts. For instance, if a specific VM cluster sends multiple alerts within a short time, event correlation groups them as a single incident and assigns priority derived from individual alert signals.
Advanced AIOps platforms further refine event correlation with business context. For example, AIOps might tag one of several incidents as business-critical if it impacts a significant customer base or an essential service like payment processing.
Detect, triage, and assess root cause in real time
Event correlation implies three critical stages of the incident lifecycle: detection, triage, and investigation.
- Event detection: ITOps, NOC, and SRE teams often don’t find out about issues until users or customers file support tickets. AIOps platforms use event correlation to promptly merge related system alerts into a single incident, allowing teams to address issues before they escalate into significant outages that affect users.
- Incident triage: AIOps platforms enhance incidents with business and operational context, expediting triage. Using enriched context, teams can resolve the incident immediately, assign it for further investigation, or forward it to domain experts or L3 teams. Timely triage prevents unnecessary delays and speeds resolution.
- Root-cause analysis: Traditionally, incidents were often linked to IT infrastructure failures. Today, planned and unplanned changes are the primary culprits. AIOps platforms digest topology for infrastructure-related causes and change data for change-related causes. Equipped with this data, ITOps, NOC, and SRE teams can address most incidents directly, resolving up to 94% without escalation.
Remediate and resolve incidents automatically
When ITOps, NOC, and SRE teams can’t correct incidents automatically, it leads to repetitive manual fixes and diverts attention from other critical tasks. Strong AIOps platforms integrate with diverse runbooks and commercial and homegrown auto-remediation tools.
Cleaning noisy data and adding context enhances the quality of incident data, streamlining routing and resolution. When teams can’t address or auto-remediate an issue, the AIOps platform should direct the incident to collaboration tools like ITSM/ticketing systems or chat platforms. AIOps platforms must be compatible with such tools to ensure you can mobilize the right experts efficiently, trigger advanced workflows, and expedite incident resolution.
Provide advanced analytics and reports
Practitioners, managers, and leaders need to understand the quality of their observability and monitoring data at different stages of the incident lifecycle. They also need insight into the tools generating the data, team productivity, and their incident management workflow efficiency.
AIOps platforms must provide interactive ITOps dashboards, reports, metrics, and KPI measurements. In addition to a robust set of out-of-the-box dashboards, reports, and KPIs, AIOps platforms must support the customization of reports for business units, application or service owners, geographies, and other segments.
A secondary benefit of AIOps is easy-to-consume analytics and dashboards that can help ITOps, NOC, and SRE managers communicate the value their teams create to critical stakeholders, supporting organizational transparency.
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Benefits and capabilities of AIOps
Be sure your AIOps platform delivers the following capabilities:
Integration with your current IT stack: Organizations rely on different for observability, monitoring, and more. These tools may represent years of investment and integration within ITOps processes. Ensure your AIOps platform integrates seamlessly with existing tools and offers APIs to avoid complicated deployment or tool updates.
Data preparation and cleansing: No surprise: The quality of input directly affects AIOps output. Simply feeding alerts without normalization and enrichment can lead to ineffective results. Make sure your AIOps platform can efficiently normalize, enrich, and tag vast amounts of IT alerts.
Change analysis: Changes in your IT environment can lead to incidents. AIOps tools should be adept at ingesting change data, correlating it with observability alerts, and automating root-cause analysis to provide incident context.
Explainable AI: Transparency fosters trust. Ensure that the logic of AIOps is clear and editable. Allow teams to understand, modify, and preview changes without needing specialists.
Reporting: Being at the center of ITOps, strong AIOps tools contextualize and present data or integrate with preferred business intelligence platforms to aid in tracking and refining metrics.
Democratized AIOps: Organizations differ in their modernization stages and vary in IT structure— from centralized ITOps to dispersed DevOps units. Effective AIOps platforms cater to diverse stakeholders, offering clear dashboards and reports to support informed decision-making across all levels.
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The five stages of AIOps maturity
Understand your maturity stage to assess the effectiveness of your AIOps and identify areas for enhancement. The five main AIOps stages include:
- Stage 0 — Chaotic
- Stage 1 — Reactive
- Stage 2 — Proactive
- Stage 3 — Preventive
- Stage 4 — Semi-autonomous
BigPanda has helped hundreds of organizations improve their AIOps maturity, regardless of their current stage. Customers have reduced IT alert noise by more than 95%, used advanced AI and ML to detect issues before incidents occur, and automated incident-response workflows to ensure the highest service availability.
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Do you need to optimize monitoring and observability before deploying AIOps?
No. AIOps can enhance and fill gaps in monitoring efficacy using AI, ML, and automation. Attempting to optimize monitoring tools for real-time insights can lead to an excessive accumulation of tools to address the dynamic tech landscape. Likewise, it can be challenging to discern each tool’s actionable information and real value.
“Don’t wait to start your AIOps journey once you are overwhelmed with alerts. Start early to get a single pane of glass to understand which monitoring tools you really need.”
– Sanjay Chandra, Vice President of Information Technology, Lucid Motors.
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Why choose the BigPanda AIOps platform
Built from the ground up for large-scale and complex IT environments, BigPanda can ingest alerts from any monitoring source you use and enrich them with topology data and valuable context. Effective event correlation allows ITOps teams to recognize critical incidents in real time.
BigPanda also ingests and correlates change data, allowing responders to identify suspicious changes in the environment that cause incidents quickly. BigPanda accelerates remediation and reduces MTTR by automating key incident management steps, from ticket creation to automation of runbooks.
BigPanda enables you to maintain service reliability, speed incident resolution, maximize IT investments, and scale incident management. Explore how BigPanda AIOps can process diverse datasets, support multiple use cases, and connect diverse teams.
