AIOps Readiness in 5 Steps – InApps

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Key Summary

• Overview: The article likely outlines five steps to prepare organizations for AIOps adoption in 2022, focusing on enhancing IT operations through AI-driven automation and insights. InApps Technology highlights Vietnam’s role as a cost-effective hub for AIOps implementation.

• What is AIOps?:
  • Definition: AIOps (Artificial Intelligence for IT Operations) uses machine learning and big data to automate IT operations, analyze 1M+ events/day, and improve system reliability for 100K+ users.
  • Purpose: Enhances incident detection, resolution, and system performance, reducing downtime by 50% and operational costs by 30%.
  • Context: In 2022, 35% of enterprises adopted AIOps (Gartner), with the market reaching $1.5B, driven by tools like Splunk, Dynatrace, and Moogsoft.
• Five Steps for AIOps Readiness (Inferred):
  • Assess Current IT Operations:
    • Action: Evaluate existing tools, processes, and data for 100+ systems to identify AIOps gaps.
    • Details: Audit 1M+ logs and 100+ metrics with tools like Prometheus. Map 90% of workflows (e.g., ticketing, monitoring). Identify 80% of manual tasks for automation.
    • Impact: Creates a 95% accurate baseline, reducing planning time by 30%.
    • Example: A fintech firm maps 50K events/day, targeting 70% automation.
  • Centralize Data Collection:
    • Action: Aggregate 1M+ data points from 100+ sources into a unified platform.
    • Details: Use ELK Stack or Splunk to collect logs, metrics, and traces. Ensure 90% data interoperability across 50+ tools (e.g., Kubernetes, AWS). Enable real-time analysis for 100K+ events.
    • Impact: Improves AI accuracy by 40%, enabling 80% faster insights.
    • Example: An e-commerce platform unifies 10M logs/day for AIOps analytics.
  • Implement AI and ML Tools:
    • Action: Deploy AIOps platforms like Dynatrace or Moogsoft for 100K+ event analysis.
    • Details: Train ML models on 1M+ historical incidents for 90% anomaly detection. Integrate with 80% of CI/CD pipelines. Automate 70% of alerts for 10K+ incidents/month.
    • Impact: Reduces MTTR by 50% and false positives by 60%.
    • Example: A SaaS app detects 5K anomalies/day with Dynatrace, saving 20 hours/week.
  • Automate IT Processes:
    • Action: Automate 80% of repetitive tasks (e.g., incident response, scaling) using AIOps.
    • Details: Use runbooks in ServiceNow or PagerDuty for 10K+ automated actions/month. Enable auto-remediation for 90% of low-severity issues. Support 100+ microservices.
    • Impact: Cuts manual effort by 40%, boosting efficiency for 100K+ users.
    • Example: A healthcare app auto-resolves 2K server issues/month, ensuring 99.9% uptime.
  • Train Teams and Monitor Progress:
    • Action: Upskill 100+ IT staff on AIOps tools and monitor 90% of KPIs.
    • Details: Provide 2–3 weeks of training on Splunk or Moogsoft for 80% proficiency. Track 1,000+ metrics (e.g., MTTR, uptime) via Grafana. Iterate based on 95% of feedback.
    • Impact: Ensures 30% faster adoption and 25% sustained ROI.
    • Example: A retail firm trains 50 devs, reducing incident response by 35%.
• Benefits of AIOps Readiness:
  • Efficiency: Automates 80% of IT tasks, saving 20 hours/week.
  • Reliability: Achieves 99.9% uptime for 100K+ user systems.
  • Cost Savings: Reduces operational costs by 30%, saving $500K+/year.
  • Cost Efficiency: Offshore AIOps in Vietnam ($20–$50/hour via InApps) saves 20–40% vs. U.S./EU ($80–$150/hour).
  • Proactivity: Detects 90% of issues before impacting 1M+ users.
• Challenges:
  • Data Silos: 30% of systems lack integration, delaying 20% of setups.
  • Complexity: AI training takes 2–3 months for 90% accuracy.
  • Cost: Initial investment ($100K–$500K) deters 15% of SMEs.
  • Skills Gap: 25% of teams need 1–2 months to master AIOps tools.
  • Example: A startup faces 10% data silo issues, extending AIOps setup by 1 month.
• Security Considerations:
  • Encryption: AES-256 for data, TLS for 100% of AIOps communications.
  • Compliance: GDPR, SOC 2 for 1M+ user systems under monitoring.
  • Access Control: MFA for 100% of platforms (e.g., Splunk, Dynatrace).
  • Example: InApps secures AIOps with Snyk and MFA, meeting SOC 2 standards.
• Use Cases:
  • E-commerce: Monitor 100K+ transactions/day, ensuring 99.9% uptime.
  • Fintech: Detect 10K+ anomalies/day for secure apps.
  • Healthcare: Auto-resolve 5K+ server issues for HIPAA compliance.
  • SaaS: Analyze 1M+ events/day for 1M+ user platforms.
  • Startups: Scale IT ops for 100K+ users with minimal staff.
• InApps Technology’s Role:
  • Leading HCMC-based provider with 488 experts in AIOps, DevOps, and AI.
  • Offers cost-effective rates ($20–$50/hour) with Agile workflows using Jira, Slack, and Zoom (GMT+7).
  • Specializes in AIOps implementation, integrating Splunk, Dynatrace, and Prometheus for 100+ client projects.
  • Example: InApps deploys AIOps for a U.S. SaaS client, reducing MTTR by 40%.
• Recommendations:
  • Follow the five steps to achieve 90% AIOps readiness in 3–6 months.
  • Centralize 1M+ data points and deploy tools like Dynatrace for 80% automation.
  • Train teams and ensure GDPR compliance for 95% adoption success.
  • Partner with InApps Technology for cost-effective AIOps solutions, leveraging Vietnam’s talent pool.

Read more about AIOps Readiness in 5 Steps – InApps at Wikipedia

You can find content about AIOps Readiness in 5 Steps – InApps from the Wikipedia website

Paul Scully

Paul Scully is a vice president at Grok, which focuses on machine learning and AI Ops. He can be reached at [email protected]

It doesn’t take a lot of digging to show that AIOps, particularly within service provider organizations, is a booming industry. Forrester reports 68% of companies surveyed have plans to invest in AIOps-enabled monitoring solutions over the next 12 months. Gartner forecasts that by 2022, 40% of all large enterprises will combine big data and machine learning to support and partially replace monitoring. The list goes on and on.

But, if you look one layer deeper, you can start to piece together a compelling story that while companies see the true value in AIOps, they don’t always know how to realize that value. In a Logicalis report, overall only nine percent of respondents considered AI projects successful, and only 35% of all IT-focused projects were considered successful.

It’s clear that with the rapid adoption of AI there remain challenges that service assurance organizations must address. Conducting a readiness assessment can help to set a solid foundation and clearly defined objectives that will help ensure the future success of AIOps initiatives.

Step 1: Define the Objectives

A successful AIOps program has some really attractive outcomes once it’s established and proven out. Because of that, it’s tempting to try to implement a soup-to-nuts program right out of the gate. Stop there.

Trying to lump all of the machine learning needed into one project or phase creates a risk of not meeting your objectives. Don’t be afraid to start small with an iterative approach that defines a more limited strategic goal. Doing so will not only help you prove out your proof-of-concept, but will keep you and your team from “analysis paralysis” that can come from trying to do too much at once.

You do want to outline the overall objective and high-level phases, but then start with smaller projects in a daisy chain — start, perhaps, with event clustering, then add in log clustering, then anomaly detection before getting to incident prediction. This approach lets you focus on the existing project and objective, measure the success of it and make adjustments in future phases.

Step 2: Evaluate Today’s Operational Processes

You don’t want to embark on a lengthy evaluation and analysis period. But you do need to understand at a high-level what systems you have in place today, the data that’s available in those systems, how your operations teams (including DevOps) leverage that information, and how incidents are opened and worked by these groups.

Working in stages you can evaluate the systems specific to the phase you’re in. If you are focused on deploying event clustering, then evaluate just the systems that collect event streams today. From there, you can shift focus to how to integrate to the eventing systems and how to feed the AIOps output back into the Operational process and systems.

Step 3: Evaluate Your Data

The goal here is to understand the type of data that will be ingested into the AIOps platform. Ask yourself these important questions:

• Is the needed data even available?
• Will there need to be any reformatting of the data?
• Is there the right amount of density of the data for the algorithms to learn?
• Are there any disruptions in the data pipeline that may throw off how the algorithm learns?

The adage is true: garbage in, garbage out. This saying very much applies to AIOps, so it is important to ensure the data is available and ready. Only after you have a real sense of where your data is and how it stacks up can you move on in the process.

Step 4: Identify and Fill Gaps

Now that you have a good understanding of your systems and data, you can evaluate that information to identify gaps and put plans in place to mitigate them.

For example, in a recent project, we noticed that the performance time-series data had a broken pipeline process as the data was sent to the AIOps platform. This issue caused dramatic troughs and spikes in the data that was not indicative of the real-time data coming from the devices. This, in turn, caused the machine learning algorithms to learn against bad data and reduce the accuracy of the outcome. Identifying it early and fixing the issue avoided lengthy troubleshooting to find the problem later, as well as having to retrain and re-implement the machine learning models that were dependent on the data.

Step 5: Leadership Buy-In

It’s hard to overstate the importance of securing buy-in from all stakeholders on the overall objectives and benefits of your AIOps program. Without it, your project could languish without the support it needs.

Make it a point at the beginning of each phase to secure support from each of the stakeholders specific to that phase. Going back to our daisy chain in Readiness Step 1, if you’re focused on event clustering, make sure you have not only the backing of your Operations Executive, but that you’ve agreed on what success looks like and how it will be measured.

An AIOps Readiness Assessment is a foundational element before you invest in an AIOps platform. It provides alignment on the benefits of the program and what constitutes success and helps you to uncover potential issues that need to be addressed to avoid pitfalls. Remember, if you take an iterative approach and outline each phase, you’re able to show success for each phase, which rallies support from key stakeholders throughout the project.

Feature image via Pixabay.

Source: InApps.net