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INOC Launches Advanced Ops 3.0 Platform at Metro Connect 2024, Showcasing Transformative AIOps and ITSM Capabilities for Network Operations
CIO Review
INOC, an ITsavvy company and global leader in 24×7 Network Operations Center (NOC) services, proudly announced today the introduction of its Ops 3.0 platform at Metro Connect 2024. This platform marks a significant milestone in the evolution of NOC operations and service, delivering an unparalleled combination of automation, efficiency, and visibility into network operations.
NORTHBROOK, Ill., Feb. 26, 2024 /PRNewswire-PRWeb/ -- INOC, an ITsavvy company and global leader in 24×7 Network Operations Center (NOC) services, proudly announced today the introduction of its Ops 3.0 platform at Metro Connect 2024. This platform marks a significant milestone in the evolution of NOC operations and service, delivering an unparalleled combination of automation, efficiency, and visibility into network operations.
Core Innovation: The AIOps Engine
Central to the Ops 3.0 platform is its cutting-edge AIOps engine, which employs machine learning and automation to dramatically enhance incident management within the NOC. Among other capabilities, this technology strategically prioritizes incidents by immediately evaluating the business impact and the severity of alarms, ensuring that critical issues receive immediate attention.
Integrated with INOC's Configuration Management Database (CMDB), this AIOps engine correlates alarms and generates incident tickets while also identifying and automatically resolving transient incidents that do not require significant intervention. This advancement significantly streamlines operations by reducing the volume of tickets engineers must manually address. Tickets that do require attention are automatically associated with and enriched by relevant data from the CMDB, equipping engineers with comprehensive information to expedite resolution.
Through a diligent onboarding process that gathers critical data points to populate the CMDB and enable these efficiencies, INOC ensures that each client's IT environment is accurately mapped within the platform, leading to dramatically more effective and informed NOC service delivery soon after going live.
In addition to better reacting to issues when they occur, the Ops 3.0 platform also anticipates them through advanced trend and pattern analysis. The platform uses historical and real-time data to identify potential issues, allowing engineers to address the early signs of problems before they materialize as incidents. This includes predicting traffic congestion, detecting unusual activity that may indicate a security threat, and identifying infrastructure components at risk of failure.
"With Ops 3.0, we're leveraging our unique position in the NOC services industry to bring machine learning and automation directly into the NOC operations environment," said INOC President, Prasad Ravi. "We can finally harness and act on the vast amount of data generated in today's infrastructure environments, marrying this insight with automation to augment and even take over tasks traditionally managed by engineers. We're already measuring the outcome in significantly faster and more proactive response rates—and thus, happier customers and end-users. We're not talking about marginal improvements; we're fundamentally changing how networks are monitored and managed, significantly reducing human effort in the process."
Integration and Visibility Enhancements
The platform integrates with many widely used Network Monitoring Systems (NMSs) and IT Service Management (ITSM) tools. This integration enables clients to immediately inherit INOC's advanced automation capabilities, facilitating a smoother transition of monitoring responsibilities and enhancing workflow automation without requiring clients to develop these capabilities independently.
The Structured NOC
Supporting the Ops 3.0 platform is INOC's operational framework, the Structured NOC, which simplifies communication and strategically channels support activities to their appropriate tiers, thereby minimizing high-tier support activities by 60% to 90%. The framework integrates secure connectivity, advanced alarm analysis with autocorrelation for incident prioritization, critical incident response, advanced incident management for swift diagnosis and troubleshooting, and efficient support request handling through a service desk. It also includes client and customer communication for updates and escalations, supported by a client experience management team that oversees onboarding, change management, project management, and quality assurance.
Extending AI-enabled Capabilities: Conversational and Ticket-Assist AI
Furthering INOC and ITsavvy's commitment to applying AI to enhance IT operations is a successful ongoing partnership with a major AI service provider to bring machine learning, particularly generative AI, into the help desk. This partnership has led to the deployment of an advanced chatbot capable of deflection and resolution assistance to reduce manual attention in addressing known issues. The chatbot, by leveraging a comprehensive knowledge base, offers end users immediate, actionable guidance for resolving break-fix issues and other support inquiries, significantly reducing help desk call volumes.
At the heart of this initiative is a dual-focus strategy: providing guidance for self-resolution and assisting human ticket agents with data-driven recommendations to expedite issue resolution. This approach not only enhances the end-user experience by offering quicker resolutions but also optimizes IT support workflows, leading to a notable 30% ticket deflection rate. Each interaction enhances the system's understanding, allowing for more accurate and efficient resolutions over time. Such efficiency gains underscore the practical deployment of AI to streamline IT operations, reflecting a shift towards more proactive and predictive service models.
"Applying machine learning and automation extends beyond immediate problem-solving," said ITsavvy's CTO, Milind Shah. "It represents our larger vision of becoming an AI-enabled technology solutions provider. We're not just improving operational efficiencies; we're redefining the standards of IT service management and network operations to deliver superior IT services and set new benchmarks for the industry by embedding AI into our core practices."
Demonstrating Real-World Impact
The Ops 3.0 platform has already achieved significant outcomes for INOC's clients, including:
• A 30% auto-resolution rate for a leading network OEM, reducing major escalations and streamlining the onboarding of over 800 customers.
• A reduction in NOC support onboarding time from 6 weeks to 1 week for AT&T Business, significantly decreasing site escalations.
• Enhancements in Adtran's NOC service offering, leading to a 26% reduction in time-to-ticket and a 50% reduction in time-to-resolution.
• For Aqua Comms, updated runbooks and a professional services catalog adjustment resulted in a 20% reduction in ticket volume and a 5-minute SLO from alarm detection to ticket creation.
• SHI experienced a 10x decrease in average MTTR, underscoring the platform's ability to alleviate support burdens and improve resolution times.
"Transitioning from our 2.0 to the 3.0 platform marks a pivotal moment for INOC and the broader digital infrastructure sector," INOC's Prasad Ravi said. "Previously, alerting clients about network issues required manual effort. Now, with our 3.0 platform, this, like other processes, is appropriately automated, significantly reducing human effort and improving the performance metrics that matter most. This leap forward is more than technological; it's a testament to our commitment to excellence and innovation in network operations that have spanned over 20 years. We're not just addressing today's challenges; we're also setting new standards for tomorrow, ensuring our clients lead in an era of rapid and accelerating technological advancement."
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How Do Automation Platforms Connect Communication with Business Processes?
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Improving Quality across AI Conversations
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Good platforms will also be capable of testing the AI through scenarios even before deploying it into production. Developers can create different kinds of scenarios ranging from common requests to uncommon ones, questions with ambiguity, and policy-sensitive cases. By testing these scenarios repetitively, companies can find out their gaps in performance before affecting actual users. It is also a good method to compare models and configurations.
Using Analytics for Continuous Improvement
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