Many network teams today juggle between 15 to 30 dashboards, a task that can feel overwhelming. They spend countless hours trying to piece together relevant information from fragmented data, making troubleshooting a single incident a daunting task. Artificial Intelligence (AI) tools, and particularly AI agents, are coming to the rescue by reducing ticket volumes, accelerating incident resolution times, and allowing network engineers to focus on more strategic tasks. However, to harness the full potential of AI, organizations must first address the issue of data hygiene.
John Capobianco, product marketing evangelist at Selector AI, emphasizes the importance of clean data, stating that poor data can compromise user experiences. Selector’s Data Hypervisor is designed to ingest and normalize a variety of operational data—logs, metrics, and events—into a unified format that simplifies analysis. Additionally, it enriches this data with contextual information, transforming raw inputs into actionable insights using machine learning.
Without proper data hygiene, AI agents often rely on inconsistent and fragmented information, undermining their ability to deliver reliable insights. To properly prepare for AI implementation, network teams should undertake a data hygiene initiative, which includes several key steps:
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Conduct an Infrastructure Inventory: Identify all devices and systems to aid in correlation and root-cause analysis.
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Standardize Descriptions: Ensure device and interface descriptions are accurate and consistent to facilitate better AI interpretation.
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Create a Centralized Metadata Repository: Establish a single source of truth to enhance root-cause analysis and correlation engine performance.
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Secure Data Transport: Implement mechanisms that uphold data integrity and comply with data sovereignty requirements during transport.
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Cross-Functional Collaboration: Encourage cooperation among various IT teams (network, security, database) to foster a holistic approach.
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Use Normalization Tools: Deploy technologies that can unify disparate data sources for effective AI analysis.
Beyond data collection, AI in network operations is about changing how teams interact with and utilize data to optimize network performance and reduce downtime. Capobianco likens AI to a digital co-worker that can evaluate network health, which has resulted in significant efficiencies for clients, including reducing daily ticket volumes drastically and enabling quick, accurate troubleshooting.
By integrating intelligent agents into their operations, network teams can move from reactive problem-solving to a more strategic and proactive management approach, ultimately leading to a more resilient and efficient network infrastructure.
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