Network Analytics: Work Smarter, Not Harder
If data is the lifeblood of the contemporary workplace, networks are the cardiovascular system, flowing information from central data centers to endpoints and back again. Monitoring the overall health of your network is critical, as its performance can have a large impact on your day-to-day operations, especially as organizations’ scale and their environments become increasingly complex. Providers of network management software like Cisco, Ruckus, and Juniper have recognized administrator’s needs and developed tools that provide deep insight, empowering them with the data necessary to make informed decisions.
By its most basic definition, network analytics is the collection and examination of network data to identify underlying patterns. Using reports generated by analytics software, administrators can make educated choices about how to improve network performance, such as routing traffic away from congested areas. Standard analytics tools require active monitoring to detect performance fluctuations and manual intervention, leading IT professionals to use them in a reactive fashion rather than a proactive one. However, key networking vendors are stepping up their analytics offerings with artificial intelligence (AI) that automates network optimization, enabling self-configuration based on changing conditions. AI can be quickly scaled to improve end users’ experience while keeping operating costs to a minimum.
Benefits of Network Analytics
Whether administrators choose to implement standard Network Analytics software or a platform equipped with artificial intelligence to make decisions for them, the ability of Network Analytics to transform the way IT operates is undeniable. Here are just a few of the benefits:
- Diagnose hidden performance detractors
- Predict service disruptions before they occur
- Optimize resource usage to reduce operational costs
- Receive recommendations for correcting issues, OR Automate remediation
- Remove the burden of network monitoring for increased IT productivity
- Detect malware or cyberattacks almost immediately
- Easily scale with insights that foster capacity planning
How & where is data collected?
If you’re familiar with the Open Systems Interconnection (OSI) model of networking, network analytics primarily concerns incoming and outgoing data packet transmissions from layer 3 devices—network components that are equipped for both switching and routing. This includes DHCP, Active Directory, RADIUS, DNS, and syslog servers, as well as network traffic such as NetFlow, traceroute, and SNMP. The more heavily a network capitalizes on virtualization to automate datacenter processes, the more information analytics software can also draw from layer 2 (node-to-node transfer) and layer 4 (system-to-host transfer) infrastructure.
Data points are gathered through Deep Packet Inspection (DPI), which is already part of background network operations. DPI can be used to analyze traffic flow via Network Based Application Recognition (NBAR) and Software-Defined Application Visibility & Control (SD-AVC) to set quality-of-service standards and monitor endpoint activity. Data stream telemetry enables granular, high-frequency capture of data points, allowing adjustments to be made in real-time.
How is data analyzed?
Network analytics engines aggregate data from a multitude of sources in an environment to create a dynamic portrait of a network and its components, which is continuously compared with a model of optimal performance. When deviations from ideal network parameters or other anomalies are detected, administrators are presented with options for course correction. The number of remediation choices given correlates with the number of data sources and configuration settings available at each point in the system. In advanced analytics equipped with AI, the software runs simulations of recommended adjustments, accounting for different network variables to gauge how changes impact the system as a whole. Using these findings, the engine pursues the remediation pathway of least resistance that won’t cause problems elsewhere in the network.
Cloud vs. Local Analytics
It’s been a critical debate in IT for many years now; Is the cloud the right choice for my needs? When it comes to network analytics, on-premises and cloud-based analytics come with their own unique set of advantages. When making the decision, there are five key factors to consider:
- Cost: The initial cost and time commitment of bringing on-premises network analytics online can be prohibitive. Cloud computing services are offered on a subscription basis and can be set up in significantly less time, however over the life of the network, recurring fees may outweigh the costs of onsite implementation.
- Scalability: The scalability of on-premises network analytics comes down to how much the organization is willing to spend on upgrades; increasing demands on the system mean adding more servers—if space allows. Cloud implementations pose no limit of capacity or performance, but this on-demand flexibility does come at a price.
- Security: Cloud-based analytics often come with 24/7 monitoring, scheduled security assessments, and an emergency response team on-call. Depending on the type of proprietary data managed by the organization, the risk of handing over data to a third party may be too great. On-premises solutions give organizations total control over their information.
- Accessibility: On the opposite side of the spectrum from security is accessibility. While the cloud offers more flexibility in sharing both within the organization and outside of it, network operations housed on site have full ownership of all network analyses performed.
- Performance: Performance is arguably where cloud-based analytics outshines on-premises solutions the most. Local hardware problems can result in substantial downtime and permanent data loss, but these are simply a non-issue for cloud subscribers. With the cloud, data is housed in a single place in compatible formats, making the user experience consistent
Placing an analytics engine in the cloud guarantees more processing power, as well as access to the most up-to-date algorithms out there. However, on-premises implementation offers the peace of mind that comes with knowing where data is at all times. Administrators may find that a hybrid solution is the best fit, providing a balance of information security and flexibility while reducing overhead expenses.
It is possible that the infrastructure you already have in place is capable of providing this insight. The first step is a little research on what your hardware is capable of, then the next most common missing part is the appropriate licensing. Often these AI services require specific authorization to leverage the service. In some cases, this licensing may have been sold as part of a bundle. If in doubt, reach out to a trusted partner that can guide you through the process; each vendor is different, and depending on what was purchased, the next steps will be unique to you.
If you find you have everything you need then it’s time to jump, get it all configured and start leveraging the power of AI to gain real insight into your network and where you have challenges.