The need for capacity planning and predictive analytics

Today’s IT landscape is far more advanced than it was ten years ago. Even though many organizations have adopted capacity planning as part of their management strategy, it’s still not enough to cope with today’s changing environment. Take for example, the cost of network downtime. Not only do companies suffer the financial repercussions, but they also experience increases in customer churn. One cause of network downtime is the inability to detect network issues before they occur. This can be avoided with the right predictive analytics tool. In fact, organizations that use predictive analytics as a central tool in combination with capacity planning, will be more efficient and profitable in the long run.

What is capacity planning and predictive analytics?

In its simplest form, capacity planning describes a company’s ability to meet present and future demands for its products and services. The process involves many facets such as roles, responsibilities, processes and functions. Ultimately, all of these depend on seamless execution between one another with an end goal to be more efficient and deliver quality products and services to customers. Predictive Analytics on the other hand, can forecast future trends with an acceptable level of reliability, provide answers to what-if scenarios and enable risk assessments. The key is to combine both capacity planning and predictive analytics. For example, if you can predict network capacity problems accurately with the right data, you can act pre-emptively to rebalance the load on your network and provision it with more capacity.

Why do we need predictive analytics?

In a perfect world, networks are running efficiently without downtime, outages, or other critical issues. But, in today’s interconnected IT world, we are faced with network complexity and increased data volume. Network monitoring systems often struggle to optimize data flow which results in inefficiencies and degraded network performance. In some cases, by the time network issues have been detected and resolved, the damage has already been done. We all know that an organization’s downtime is a recipe for disaster: increased costs, lost profit, and reduced customer loyalty and retention.

Imagine having the ability to proactively avert potential issues before they impact network health? It is possible, but one needs the right monitoring tool that will provide accurate data to make the right decisions.

Why companies choose cPacket

Identifying and pinpointing potential network performance issues can often lead to an endless cycle of guessing games without any resolution. cPacket’s Predictive Analytics and Baselining feature (PAB) eliminates the guessing game by bringing greater accuracy to network issues before they can affect network operations.

cPacket’s PAB is a feature available in the cVu/Cx traffic monitoring devices from the 17.3.1 release. The advantage of the cVu/Cx devices is the ability to support packet brokering and NPM functionality in a single device, and at data rates from 1G to 100G. These devices are attached to cStors which perform forensic analyses, along with packet captures. The advantage of cPacket’s PAB feature is the proprietary algorithm which intelligently uses data from previous predictions in order to accurately predict future trends. By monitoring and gathering relevant data over a specific time frame, network operators are able to optimize capacity planning and prepare accordingly as seen in Figure 1 below. This significantly reduces costs by eliminating the need to purchase multiple management tools.

In addition to the predictive analytics and baselining feature, network operators can use cPacket’s cBurst to identify any spikes that may occur, especially when these spikes deviate from the baseline value. Furthermore, because cBurst can be used as a predictive behavior tool, network operators are armed with meaningful and actionable data which enables them to make smarter decisions.

Figure 1: Simplified dashboard showing current data and predictive baselining for accurate KPIs

Several key benefits of using cPacket’s predictive analytics and baselining feature are the following:

  • Provide accurate data for network operators to evaluate and prepare accordingly
  • Accelerate network issue resolution
  • Reduce network downtime
  • Reduce overall management costs

cPacket helps companies worldwide generate competitive advantages by offering solutions that keep internal stakeholders and network management teams informed and empowered, and customers happy.