The Network Impact of Big Data

Big data is a big deal. You read about it and its promises of insight. But you will need a network to collect and distribute big data connected to processing locations. Many big data applications require real-time communications. Plan for big data on your network now; don't wait until issues arrive. Catch-up costs money and results in delayed implementations.

The only sure predictions around big data's impact are that the network will be busier, need more capacity, and probably cost more. How much capacity will be needed is only an estimate. It could wind up being far more than estimated if the big data applications are very successful. Educated predictions on traffic may look good now, but conditions can change and render them inaccurate.

Real-time processing of big data will require real-time data delivery. Without real-time delivery, data will already be old and historical. One of the advantages of big data, especially in regard to the Internet of Things (IoT), is its enabling of a rapid response to changing business functions and conditions such as security alerts, building automation, location tracking, etc. Big data collected quickly fosters just-in-time decisions.

The United Nations Economic Commission for Europe predicts that data growth will be 350% higher in 2019 than it is in 2015. Such volume of data means a corresponding 350% growth in network traffic, which may be carried over private LANs (wired and wireless) and WANs, the Internet, and cellular networks.

The problem with predictions is that a number of business conditions are assumed to be known. The predicted 350% data growth is across the board, not for an individual organization.

One of the IoT drivers is the operations department of an organization. Operations may be planning its own rollout of technology without the knowledge of IT, which may impact the network or bypass it entirely via a cellular connection. If it is bypassed then IT does not need to support that IoT traffic. Further, the success of IoT may be underestimated or the frequency of data transmissions may be increased without the knowledge of IT. These sort of scenarios will impact the accuracy of traffic predictions. Therefore, monitoring network traffic conditions in real time with rapid traffic analysis becomes mandatory.

Big data transmission will occur on the premises network and may affect the enterprise WAN. If the cloud is used, Internet access will be taxed for its capacity. The network's capability to absorb and transfer big data traffic is made up of six elements:

1. Bandwidth -- You will always need more. As big data is analyzed, users may want to collect even more data as they learn how to better analyze it. Don't forget that data about the networks adds to the traffic load, and therefore more bandwidth may be required. Bandwidth should be scalable in response to traffic that can increase rapidly. You need to relate bandwidth utilization to the application used.
2. Network Delay/Latency -- Real-time delivery with real-time responses based on analysis means that network delay can cause the data and responses to be created and delivered too late. Predictable consistent latency needs to be delivered.
3. Security -- This is important for both access to and transmission of the data. It is very likely that the data is sensitive for both the organizations and its customers.
4. Delivery Accuracy -- Data can sometimes be lost or delivered with errors. No network is perfect, but knowing that there has been data corruption can help minimize the impacts.
5. Availability -- The loss of networks can be highly disruptive. An availability of 99.99+% is a good goal. Make sure you know what events or conditions are not included in the availability calculation, as you may actually be experiencing only 99% availability.
6. Resiliency -- Failures will occur; they always do. How fast those failures can be resolved leads to either confidence in the network and its management or skepticism of the value of data collection and analysis.

Network Monitoring is Mandatory

Network monitoring has been part of operations for a long time. Most monitoring systems deal with major changes, failures, configuration data, and traffic reporting. The monitoring function itself is a producer of big data. Therefore, the network data needs to be analyzed with big data applications. Traffic trends, where apps are located, what caused the traffic, and what network resources are available to effectively carry the traffic are all part of the network big data information.

Many big data applications require real-time communications so that data can be delivered and analyzed properly. But real-time communications requires real-time monitoring, and this must be incorporated when predicting traffic levels and the impact on the network.