In a time of great change in networking, the need for network operations productivity improvements has never been more pressing. Tools proliferation, data availability, and network complexity have created an environment of ticket overload, increasing manual work, and less insight. The next generation of network operations tools must automate tasks that humans are less proficient at, reduce the overall ticket load, and provide better insights. Network AIOps is leading the way.
Tools Proliferation and Productivity
Historically, network tool vendors have focused on deep analysis of one specific data type. As a result, Network Operations teams have SNMP trap monitors, metric visualization dashboards, log management systems, flow analysis solutions, and more. This proliferation is exacerbated by vendor-specific tools.
Not only does this create growing manual correlation work across different tools when an incident occurs, but it also limits the insights that comes from correlation across all data. That insight includes reducing redundant / irrelevant data and identifying the incident root. In some Network Operations environments, different tool owners must first have a meeting or conference call to align on where triage should begin. This time can vary, but times of 40-60 minutes have been reported across multiple companies.
Network AIOps brings many data sources together in one normalized, searchable network model so correlation can occur across all data. With a purpose-built network model, and multi-layer topology-based auto-correlation, one Fortune 500 customer saw the time to act reduced from over 40 minutes to 1 minute. Single-data type tools may still play a role in deep-dive analysis for hard to remediate problems, but their use as the first line of anomaly detection and action has been surpassed by Network AIOps.
Data Availability and Productivity
The volume, velocity, and variety of data has increased in all areas of IT, including operations. Network tools must be able to effectively process large volumes of data in a day, they must also be able to process data at small sub-minute periods, sometimes as small as every ten seconds. Designing network tools for both high daily volumes and high velocity frequency was not a significant consideration with historical tools, and certainly not for multiple data types at the same time.
A network tool may be humming along well for a single data type. However, what happens when high-frequency collection is added for millions of switch queues? Can the extra load be supported? A network tool architecture must assume future new collection and analysis requirements.
In addition to being high performance, tools must be highly efficient. Some tools are not even capable of horizontal scaling. Those that are, can perhaps argue that theoretically with enough resources, they can scale to any requirement. However, Network Operations teams do not have access to infinite resources. Rather, they are often resource constrained. Productivity is not just a function of human labor, but also of IT expenses.
Network Complexity and Productivity
Containers and microservices “broke” the traditional application and compute infrastructure approach by IT operations tools. Network Operations has experienced a similar challenge. Underlays, overlays, SD-WAN, hybrid cloud, multi-cloud, and endpoint proliferation require a new approach.
Network AIOps platforms that auto-discover multi-layer relationships, including topology, physical, optical, Ethernet, IP, and TCP, can quickly recognize which alerts / tickets are related and identify the lowest level of incident root information. A capability that does not exist in IT AIOps platforms.
Rapid Incident Root identification dramatically cuts the time to action, as well as overall mitigation and remediation times.
Network Operations teams are faced with growing pressure: hard to find & retain talent, increasing data, increasing network & operations complexity. In addition, the next couple of years are likely to see increasing budget constraints.
Network Operations teams must do more with less. Tools must dramatically increase Network Operations productivity, eliminating redundant / irrelevant tickets, reducing the complexity of identifying incident root, and efficiently scaling. Augtera’s Network AI is attacking these issues and our customers are seeing a 90%+ reduction in trouble tickets, 90% reduction in time to act, 50% reduction in mitigation times, and 40%+ improvement in remediation times. The time between incidents is also increasing by 4x or more.
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