Before 2020, the world was steadily heading towards digital innovation to improve business operations and daily life. But post-2020 saw an explosion of companies wanting to tap into the endless possibilities of digital transformation with a focus on end user digital experience. In fact, businesses in the United States and other global regions are set to spend an estimated $4.4 trillion on digital transformation.
More companies across sectors recognize the value in digital transformation to gain market share, increase revenue, boost profitability, and ensure secure operations. But this shift requires a complete rethinking of an organization’s IT infrastructure, technologies, and processes. And while this rethinking can open up a goldmine of data for organizations to monitor and optimize their digital experiences, it also comes with a cost of increased complexities and strain on networks to process and manage that influx of data.
The data problem stifling networks
From internet-of-things (IoT) sensors to servers, thousands of devices within a company’s IT infrastructure generate data about their operations, performance, and security. These devices rely on a network to transfer this data from edge to edge, edge to cloud, or cloud to edge so their applications can run smoothly. Making sense of the volume of data that travels within the network is the cornerstone of effective digital transformation, but two problems have persisted when trying to do so.
The first problem involves contextualizing the data. Giving context to the generated data becomes more complicated when multiple data sets exist, such as each device’s network performance, server responsiveness, or fiber throughput. Mitigating the issues that emerge from a network, which could hinder its reliability, requires a contextual analysis. But with multiple data sets to account for, this becomes more difficult.
Another issue is the sheer amount of data that each device produces. With a vast number of data from thousands of devices scattered across a network, it’s challenging to monitor - most often manually - the problems that may arise, such as network degradation or failure, and which ones need to be reviewed and evaluated first.
Network intelligence is how companies can gather and analyze data insights and improve a network’s operational ability.
The way forward with network intelligence
Network intelligence, an enabling technology that allows communications service providers (CSPs) to capture subscriber-, service-, and application-level awareness contained in network traffic, is crucial for analyzing data that travels in a network. With network intelligence,
companies can identify performance challenges that exist and determine what’s causing them.
The capabilities of network intelligence power next-generation technologies that are at the forefront of digital transformation, including AI, machine learning, and 5G. These capabilities - which include multi-source, multi-domain data capture across disparate network devices; contextualization and correlation across all network events; problem discovery, alerting and resolution recommendation; and root cause analysis for future improvements — are instrumental in enabling a true end-to-end network solution that’s essential for digital transformation.
Network intelligence that hits the mark should work through a five-step process:
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Gather telemetry from disparate sources
Telemetry uses automation to monitor and analyze data from multiple sources, such as Simple Network Management Protocol (SNMP), webhooks, application programming interfaces (APIs), syslogs, and transaction language 1 (TL1), across a wide array of networks. Network intelligence should leverage telemetry to improve observability intentionally and not monitor with a poll-based methodology — all in an effort to provide insights into how application experience can be improved based on synthetics, real-time and predicted health, how well it’s operating, its quality and security gathered from sources like firewall, edge devices and web gateways. -
Correlate, enrich and transform data
Network intelligence solutions should support the network by pruning down correlations of events established by the automated pattern discovery engine and utilizing various data sources from highly complex and dynamic IT environments. From there, they can focus on enriching, preparing, and composing several alert data payloads with rich operational and topological context from other source systems to compress ticket volumes and drive scalable root cause analysis quickly. Hundreds of alerts could equal one actionable ticket that is paired with automated remediation driving uptime and enhanced reliability. -
Integrate with an IT service management program
Problems are bound to arise with copious amounts of data traveling to the network. When one does, the network intelligence solution should integrate with an IT service management program to give visibility into any change or incident data. To take it a step further, the solution should trigger an alert as necessary and issue automated tickets for incidents. -
Provide a holistic view of all data in a management portal
Having a single pane of glass for administrators to view historical, current, and forecasted data enables them to go beyond simple analysis for network events. With network intelligence, domain silos are avoided and recommendations can be made without manual intervention. -
Automate resolution steps
Finally, the network intelligence solution should provide steps to resolve any network problems that arise, using root cause correlations to guide needed changes, such as remote reboots, policy adjustments, and circuit reroutes which can be automated for ultra-fast repair.
As more companies adopt digital transformation, they’ll require network infrastructure that can support their data demands. The produced data needs to be analyzed and monitored to identify any underlying problems within a network. With network intelligence, companies can understand the value of the data their devices produce and fully embrace digital transformation.
