Break silos: Three steps to full-context ops

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Every day, operators receive mountains of alerts to sift through. Prioritizing alerts based on impact and severity can seem impossible. And constantly evolving IT environments increase complexity by orders of magnitude. Knowing which alerts to prioritize is extremely difficult, especially without the critical context to make those alerts actionable.

“An alert without context is just noise — and incidents without context are not a priority,” said Jon Brown, senior analyst with Enterprise Strategy Group by TechTarget, in a recent webinar.

Simply giving operators more data isn’t the answer. The difference is context. Anyone who has worked in IT will tell you that a flood of data without context is an efficiency vampire, draining energy and resources from even the best teams. When combined with siloed teams, data lacking context creates information gaps, unrealistic expectations, and immense stress for operators. In addition, it impacts service availability and operational efficiency.

The antidote lies in delivering all the information teams need — in context, quickly, and upfront — to better understand what happened, why, and what to do about it. This comprehensive view of every incident is the foundation of full-context operations.

“Adding context to enrich alert data leads to more effective prioritization,” said Paul Bevan, Research Director of IT infrastructure at Bloor Research. “This results in faster problem resolution and fewer service disruptions.”

Full-context IT operations remove silos, streamline collaboration, and reduce workload so teams can move faster, avoid surprises, and give every operator a complete picture of incidents.

Without full-context ops With full-context ops
Operator stress due to data overload without insight into how information is related Reduced stress levels with actionable information based on trends and patterns across data sources
Communications breakdowns and information silos between teams Improved collaboration, workflows, and knowledge across teams based on greater visibility and data access
Delays and manual effort due to decentralized information, knowledge, and processes Increased uptime and efficiency based on every operator having a holistic view of incidents to resolve them more quickly
Wasted time waiting for information from other teams or tools Improved incident resolution with ready access to all necessary information in an actionable format
Inability to identify incident root cause quickly Accelerated triage and investigation times

What are full-context operations?

Full context correlates monitoring, topology, CMDB, change, and historical data across sources and dimensions to provide a unified, actionable view of an alert and incident. Alerts with context become actionable. Incidents with context can be quickly prioritized and remediated.

Full context enables teams to anticipate issues as they develop and proactively detect, identify, and resolve incidents before they become outages. Full-context operations provide the data, insights, and processes to make ITOps faster, more consistent, and sustainable.

By standardizing data and processes, and adding context to every incident, teams can more easily anticipate and collaborate on issues, allowing them to proactively identify potential problems for better service reliability and operational efficiency.

AIOps uses full-context operations to eliminate IT silos

Full-context operations tackle the pain points of disjointed information, delayed decision-making, and reactive firefighting in IT operations. AIOps platforms, when properly designed and implemented, can connect data, workflows, and teams in real time. Eliminating blind spots, fostering collaboration, and empowering proactive incident resolution leads to smoother operations and happier users.

There are three steps to achieve full-context operations.

Step 1: Connect information across teams

AIOps empowers full-context operations by transforming raw data into actionable insights. But full context relies on eliminating data silos.

  • Standardize alert formats and integrate observability tools: Joining and normalizing observability and monitoring data involves converting alert formats into a standardized schema that includes critical information such as severity, alert type, and affected system. Standardization allows seamless integration and interoperability between tools like Splunk, New Relic, or AWS CloudWatch.
  • Enrich alerts with relevant information from multiple sources: AIOps enriches alerts with contextual information like application names, server locations, and service impact. This context ensures that alerts are actionable and include comprehensive details that help identify and resolve incidents more efficiently.

Step 2: Augment and scale staff with AI

AIOps platforms allow teams to cope with the growing volume of data and handle incidents more efficiently without increasing headcount. In fact, by adopting effective AIOps event correlation, your organization can reduce alert volume by more than 95%.

Reducing alert volume empowers teams to focus remediation efforts on the incidents that need their expertise the most. Doing so also increases team efficiency while lowering MTTR and improving service availability. Make every team member an expert by providing historical and AI-powered insights and analysis. This information dramatically reduces investigation time by putting the information and context team members need at their fingertips, scaling the impact of each individual.

Step 3: Simplify and automate workflows

Communication is critical for effective incident response. AIOps can also facilitate collaboration, knowledge sharing, and collective decision-making among teams during critical incidents. Two key capabilities make the difference:

  • A common platform and collaboration workflows: Ensure that your ITSM and ITOM teams share a platform designed to facilitate collaborative incident management and resolution. A common platform where teams can assign tasks, share notes, and update incident status within the same interface streamlines workflows, ensures alignment on the resolution process, and reduces MTTR.
  • Automation of repetitive tasks: Using AI to automate workflows with rote, repetitive tasks allows teams to focus on resolving high-priority issues rather than sifting through irrelevant alerts.

Deliver full-context operations with BigPanda AIOps

BigPanda offers the only resilient and scalable AIOps platform that centralizes knowledge across data sources and dimensions to reveal potential relationships within incidents and accelerate detection, response, and resolution times. With BigPanda’s unique ability to supply full context for every IT incident, our customers report up to a 50% reduction in MTTR.

“We enrich our data through BigPanda, enabling better correlation and more insightful alert tags. We have full-context visibility into our inbound alerts and can now get the right teams involved immediately for a far faster resolution time.”
– Mark Peterson, Supervisor IT Operations, Cambia Health Solutions

Learn how BigPanda can transform your ITOps practice with full-context ops in our latest e-book, “Full-context ops: Turn data into patterns, insights, and actions.”