Network Intelligence: Unlocking New Opportunities for Digital Transformation


As companies undergo digital transformation, they must consider how the added data will impact their networks. Frank Cittadino, SVP of edge services at Zayo, shares how network intelligence can help monitor and manage this influx in data to enable true digital transformation.

As the world becomes increasingly digital, businesses are following suit in an effort to keep pace with unceasing change and evolving end-user expectations. Thriving in today’s digital era means modern enterprises must embrace digital transformation to improve operational efficiency and scale innovation — and an overwhelming majority are doing just that. According to a recent Zayo study of IT leadersOpens a new window , 86% of organizations are currently undertaking a digital transformation. 

While investing in digital transformation can help businesses improve their operations and outpace competitors, businesses must also consider how it’ll impact their networks. After all, organizations integrating digital transformation into existing IT infrastructures must account for the influx of data that will inevitably add more strain to their networks. 

Too Much Data is Overwhelming Networks

At the center of digital transformation is data, and lots of it. Digital transformation technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), require data to be processed and managed in real-time so critical functions aren’t interrupted. As organizations introduce digital transformation to their IT infrastructure, these technologies join thousands of devices, such as servers, firewalls and code switchers, already on the network. Each device creates data on how it is performing. The data is then transferred from edge to edge, edge to cloud or cloud to edge. Making sense of this data is necessary for true digital transformation, but two problems have gotten in the way.

1. Contextual analysis of the data

Data is useless unless it can be understood in its proper context. But getting proper context is more difficult due to the multiple data sets coming from numerous places, like the performance of each device’s network and server responsiveness. Without the context, addressing network issues hindering reliability and performance becomes increasingly difficult.

2. Volume of data

Another challenge is handling the vast amounts of data each device produces. According to a 2022 Optimizing Business Analytics by Transforming Data in the Cloud surveyOpens a new window , data professionals reported data volumes within their organizations are growing by 63% per month. With thousands of devices scattered across a network and data volume rapidly increasing, it can be challenging to quickly identify problems that may arise — like network degradation or failure — and then determine which ones to review and assess first. 

So how can companies collect and evaluate data insights and improve their network’s operational ability? Through network intelligence. 

Network Intelligence Is the Way Forward for Organizations

Analyzing the data within network traffic and gaining insights into performance issues becomes more manageable with network intelligence. With network intelligence, which helps communications service providers (CSPs) capture information from the network traffic, companies have the resilience, agility and security they need for digital transformation. 

There are a range of network intelligence capabilities, including multi-source, multi-domain data capture across a wide array of network devices; correlation and contextualization within network events; problem detection, alerting and resolution suggestion; and root cause analysis for future improvements. These capabilities power a range of next-generation technologies, including AI, machine learning and 5G, and are essential in enabling a true end-to-end network solution that’s key to digital transformation. 

The ideal network intelligence solution should possess two crucial elements. First, it should have network-centric application monitoring, which provides a front-line view of network and application performance and enables rapid alerts should any problems come up. Next, it must offer proactive application performance optimization. This set of responses helps improve application performance. 

Five Steps for Applying Network Intelligence

Here’s the five-step process a network intelligence solution should work through.

1. Use various sources for telemetry gathering

Network observability is enhanced through telemetry — gathering and analyzing data from multiple sources. Telemetry relies on automation to monitor and analyze data from a range of disparate sources proactively. Sources include devices, application programming interfaces (APIs), Syslogs, orchestration, and transaction language 1 (TL1). Using telemetry, a network intelligence solution can show the quality of an application experience and reveal areas for improvement according to synthetics, current and predicted health, and security performance.

2. Provide data correlation, enrichment and transformation  

The automated pattern discovery engine within networks establishes correlations between events. Network intelligence solutions should reduce these correlations by leveraging data sources from dynamic and highly complex IT infrastructures. Then, the solutions can enrich, prepare and compose multiple alert data payloads without distraction by using context — typically topological and operational — from other sources. This decreases the number of tickets and accelerates faster root-cause analysis. That way, only a single actionable ticket and automated resolution are provided instead of numerous alerts, driving uptime and improving reliability. 

See More: Decoding IoT Data: The Case for Network Intelligence

3. Consolidate with an IT service management program for network problems that arise

With a large volume of data transferred to and from the network, some problems will inevitably occur. Once problems arise, the network intelligence solution should clarify any adjustments or incident data by consolidating with an IT service management program. The ideal solution should also prompt alerts as needed and supply automated incident tickets. 

4. Supply a comprehensive view of all data in a single place

A central location for IT personnel or administrators to view historical, current, and predicted data allows them to provide more complex analysis for network events. Network intelligence helps these teams avoid domain silos and provide suggestions without manual intervention.

5. Provide root cause correlations for problem resolution 

Lastly, the optimal network intelligence solution should list directions for IT teams to resolve any network issues. With automated root cause correlations directing the areas that need changes, such as policy adjustments, remote reboots and circuit reroutes, networks can have ultra-fast repair. 

Companies that plan to implement digital transformation need a network infrastructure to support the massive amounts of data their devices generate. They must also monitor and analyze that data to identify network problems. Companies can get a richer picture of this data by turning to network intelligence. With this understanding, there is no limit to what can be achieved. 

How are you applying network intelligence for smarter data management and digital transformation? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .

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