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ITOps vs. AIOps solutions
According to a recent survey by Enterprise Management Associates, IT outages can cost large enterprises more than $1.5 million per hour. AIOps offers a solution. With an effective AIOps platform in place, enterprises can decrease the frequency and cost of outages by 30% and reduce their duration to under an hour.
AIOps means artificial intelligence for IT operations. AIOps platforms, also known as event intelligence solutions (EISs), apply artificial intelligence (AI) and machine learning (ML) to IT operations to automate, streamline, and optimize IT processes. These processes include event correlation, anomaly detection, and root-cause identification.
Short for information technology operations, ITOps includes the processes, services, and people an IT department manages to ensure the smooth functioning of an organization’s technical infrastructure. ITOps consists of the implementation, management, delivery, and support of IT services. ITOps encompasses multiple, but often siloed and fragmented, 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 enhances visibility and data sharing across teams, helping to eliminate silos and reduce the load placed on more senior specialists. In essence, AIOps brings intelligence, flexibility, and agility to ITOps.
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Use AIOps tools to manage growing IT complexity
As IT environments grow more complex, fragmented, and fast-moving, the application of generative AI in ITOps is becoming a necessity. Data volume and service complexity continue to grow as technology evolves and customers demand more services. The rate at which data volumes are increasing isn’t slowing, making it nearly impossible to evolve while maintaining high levels of service availability. You risk missing critical alerts amid the sea of data and system alerts.
As your organization undergoes digital transformation to cloud and hybrid-cloud environments, AIOps helps support alert management, incident management, and service availability. AIOps platforms turn fragmented tools, teams, and data into decisive actions and can automate many manual and time-consuming ITOps processes.
These capabilities allow enterprises to scale their ITOps capabilities without increasing staff. AIOps allows enterprises to connect fragmented data, workflows, and teams in real time, eliminating blind spots, fostering collaboration, and enabling proactive incident resolution and more efficient operations.
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How does AIOps work?
AIOps uses advanced AI and ML to instantly correlate and analyze multisource IT data and automate and accelerate incident triage and investigation. AIOps solutions eliminate “noise” to identify and group data into suspicious events. Noise reduction facilitates anomaly detection, and allows IT teams to identify critical incidents before they become outages. AIOps platforms can automate alert escalation and provide contextual insights 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, resulting in streamlined processes 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:
AIOps enhances observability and monitoring tools
Enhancing the efficiency of ITOps, NOC, and SRE teams depends on gathering data from various monitoring tools. Alone, monitoring tools often produce overwhelming and unclear alerts. AIOps platforms enhance observability and monitoring tools by filtering out the noise, deduplicating, and normalizing the data they produce. 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 tool redundancy. With the ability to track planned and unplanned system changes from sources including Continuous Integration/Continuous Delivery (CI/CD) and change management tools, AIOps helps identify changes that might cause IT disruptions.
AIOps enhances 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 cause 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, up-to-date topology model. 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.
AIOps can correlate alerts from across the IT infrastructure
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.
The modern IT stack creates overwhelming alert noise. The average enterprise uses more than 20 observability and monitoring data sources. When incidents occur, ITOps teams must manually comb through massive amounts of alerts. Many of these alerts are low-quality and unactionable because they lack the necessary context for operators to understand what’s happening, why, and how to respond.
AIOps can ingest alerts and events from multiple monitoring tools or sources, including infrastructure, network, application, and cloud-native monitoring and observability tools for cross-domain analysis. These platforms must also be able to correlate, group, and reduce duplicate alerts from monitoring tools, reducing time-consuming and unnecessary manual work.
Alert correlation is key to reducing the overwhelming number of alerts that modern enterprise applications and infrastructure create so that operators can focus on what matters the most. The BigPanda Event Enrichment Engine ingests alerts from multiple data sources, consolidating siloed observability, change, and topology data into a unified view. AI-powered event correlation deduplicates, filters, normalizes, and processes these alerts to eliminate unnecessary noise and provide IT operations teams with a complete picture of your IT environment.
Reducing alert noise is a critical capability of AIOps solutions and plays a massive role in efficient incident response. BigPanda customers often reduce alert noise by 80% within eight weeks of implementation, frequently exceeding 90% or more over time. Gamma, a leading European supplier of communication services, adopted BigPanda and reduced alert noise by 93%.
“Within two weeks, we had a substantial reduction in alerts — and better alerts. An instant bang for the buck.” Dan Bartram, Head of Automation and Monitoring, Gamma
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 the root cause of incidents in real time
Event correlation improves 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: 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 and resolve them 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 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 can support the customization of reports for business units, application or service owners, geographies, and other segments.
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What are the capabilities and benefits of AIOps?
Be sure your AIOps platform delivers the following capabilities:
Integration with your current IT stack: Organizations rely on different tools 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 normalization: Inconsistent formatting makes it difficult to get consistent data and glean valuable insights. AIOps can filter, deduplicate, and normalize complex IT data at the point of ingestion and translate it into a consistent taxonomy in real time.
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 your AIOps solution is clear and editable. This transparency allows 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. These analytics and dashboards help ITOps, NOC, and SRE managers communicate the value their teams create to critical stakeholders, supporting organizational transparency.
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 four phases of AIOps maturity
Understand your maturity stage to assess the effectiveness of your AIOps and identify areas for enhancement. The four main AIOps stages include:
- Phase 0 — Set a baseline
- Phase 1 — Reduce alert noise
- Phase 2 — Establish actionable incidents
- Phase 3 — Improve mean time to resolution (MTTR) with AI
BigPanda has helped hundreds of organizations accelerate their AIOps maturity, regardless of their current stage. Our customers use AIOps from BigPanda to reduce IT alert noise by more than 95%, detect issues before they become incidents, and automate 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
As IT outage costs increase, it’s critical to identify, prioritize, and resolve incidents quickly to minimize the impact on your organization. AIOps from BigPanda makes every responder an expert by aggregating data across your IT stack into actionable incidents. With these insights, your responders can detect, triage, and prioritize incidents in seconds.
BigPanda enables you to maintain service reliability, accelerate incident resolution, maximize IT investments, and scale incident management. BigPanda is designed and built from the ground up for enterprise-scale, complex IT environments. Our platform can ingest alerts from any monitoring source 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 quickly identify environmental changes that cause incidents. By automating key incident management steps, such as ticket creation and runbook automation, BigPanda helps accelerate incident investigation and remediation and reduce MTTR.
To learn more, check out our latest e-book: From firefighting to prevention: Transform IT incident management with AIOps.
