Accelerate & Automate Incident Recovery with AIOps

Automating incident recovery has inculcated rhythm to systems. But ITOps need more than automation. And, that is the acceleration of automated incident recovery. 79% reported in a survey that adding more IT staff to address IT incident management is not an effective strategy. Incident recovery needs accelerated intelligent automation. The two core outputs when accelerated are better and faster Incident Diagnosis and Resolution. AIOps with ML/NLP can provide a better incident context, impact assessment, triage data and tools at one place. 

What didn’t work in the Traditional Incident Resolution Process?

Network engineers and service desk personnel are tasked to process numerous incidents. These include various diagnostic activities, manual operations, repetitive tasks, opening up multiple dashboards, and verifying metric data from multiple tools. In many cases, the process also involves coordinating with other IT personnel to bring relevant teams together to march towards problem resolution and incident closure. These operations and human interactions increase mean time to detect (MTTD) and mean time to resolve (MTTR) for incidents, resulting in SLA breach, customer churn and lost revenue.

In the process, these teams have to deal with other challenges such as:

  • Unknown Incident Impact
  • Overwhelming number of ticket bounces and handoffs
  • Too many detours to check triage data (metrics and logs)
  • Manual diagnostic and resolution operations
  • Tedious process to exchange logs, diagnostic results with SMEs
  • Delays due to knowledge access from vendor portals or external sources

Let’s take a look at how a typical flow of incident recovery looks like.

Automatic Incident Recovery accelerated with AIOps

AIOps with ML/NLP technology can provide valuable insights and expedite the incident resolution process. Here is the process that takes you from an automatic to accelerated incident recovery process.

Step 1: Monitoring tools detect an issue and raise an alert

Step 2: AIOps solution processes the alert and creates an appropriate ticket, after correlation. As soon as the ticket is created or raised, it will be automatically picked by the ML/NLP engine, analyzed and updated with key insights like assignment group, sentiment of the ticket and the suggested KB article.

Step 3: AIOps uses the context to recommend remediation steps/workflows based on historical learning. It can also suggest recommended action to be performed for resolving this issue. We can go a step further and even set up workflow remediation to resolve the issues faster. As the number of incidents increase the accuracy of the model will also increase which helps in bringing down the MTTR.

Step 4: Enterprises uses RPA or homegrown script to automate the remediation workflows

Step 5: Once this is available and the recommendation gains confidence, the AIOps system can invoke the automation flows automatically.

Step 6: Once the situation is remediated the monitoring tool will clear the original alert and AIOps will automatically close the ticket. Classification methods are used to enrich the incident with contextual data. This helps the ITSM teams resolve or triage the incident quickly.

Other articles that might interest you:

Top 3 NLP Use Cases of CloudFabrix AIOps Solution

CloudFabrix Incident Room: AI Powered Modern Digital War Room

AI Powered Modern Digital War Room: CloudFabrix Incident Room

Incident Rooms is a solution for automating end-to-end Incident management. The solution also presents a holistic view of key asset data, metrics data, historical data and AI-driven recommendations to enable expedited and streamlined processing of the incident. Incident Rooms increases productivity of operations and SOC/NOC teams by pulling together and assimilating key asset/metrics data from multiple sources and top it off with ML-driven recommendations. Some of the benefits of Incident Rooms are:

  • Improved Operational Efficiency
  • Reduced Mean Time to Diagnose/Resolve (MTTD, MTTR)
  • Reduced alert noise by alert deduplication
  • Efficiently handle large volume of incidents by correlating and to actionable problems
  • Centralized operational portal for alerts or incidents originating from multiple systems: Automated Tools, Workflows for Diagnosis & Resolution
  • Visually mark, compare, time-synchronized key metrics
  • Instant insights and knowledge base from similar, related incidents
  • Pinpoint Anomalies and Unusual Changes
  • Context and Time Aware Assets, Metrics, Logs

Streamline your operations by pulling together all relevant data from across multiple sources in one place with AI-driven recommendations to help expedite processing time. Faster resolutions mean less downtime for your business which is why it’s so important to make sure you have a plan for these inevitable events before they happen.

Srinivas Miriyala
Srinivas Miriyala
Srinivas Miriyala is the Director Product Marketing & Business Development at CloudFabrix Software. He has over 20+ years of product and business development experience, with 10+ years in leadership roles spanning Enterprise Software, Mobile and Telecom industries . Srini holds three US patents and has global experience working in U.S, Europe & Asia markets building strategic partnerships and rolling out products through System Integrators, carriers and device manufacturers. His current role involves driving AIOps product messaging, GTM strategy development and execution for the Europe and APAC markets.