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Ten years ago, if a person had suggested a predictive model to prevent a network failure occurring due to a planned breach, people would have not believed him. Today, that has become a reality thanks to predictive analytic tools and different technologies including big data and statistical modeling.

In simple words, a predictive system looks for irregularities or patterns in data and identifies issues in a network or a server before they transform into bigger problems. This piece of information can then be used to troubleshoot it. An example would be a network outage due to failure in the power supply that can be predicted by looking at the irregularities in the flow of power supply in the days before the outage occurred. The possibilities are immense and that is why it looks so promising.

To make this clearer, let’s understand the basics first.

Analysis of Network Behavior

The basic premise of using predictive analytics to troubleshoot network issues is to let it analyze the behavior of the network. For example, analyzing the flow of data in a communications line can help it understand if any loopholes in it could create a possible entrance for a data breach.

This information can then be used as a preventive measure; a defensive mechanism can be laid out even before the breach is attempted, thereby safeguarding the data available in the line. This not only helps in the security but also in network management and policy setting. To know what is happening inside a server network and monitoring it is real convenience for network managers. It halves their daily maintenance work.

Additionally, analytics can also give out trends and insights to organizations. If a certain type of communication mechanism is known for overloading, companies can avoid creating similar structures and instead opt for better versions or entirely different infrastructures. This information can then be utilized during infrastructure development, especially when it comes to the development of server rooms for IT organizations where data breach and upper thresholds need to be monitored by the second.

Predictive Analytics in Action

Experts suggest that such technologies should be put to use in those sectors where issues can cause discomfort to a whole crowd of people. They are referring to healthcare and other emerging sectors like power distribution and aviation traffic management. Network management in these sectors will help increase safety and security and minimize issues/accidents.

Healthcare systems actually need this technology because it can help hospitals better care for their patients who require 24×7 technical support and are continuously connected to the hospital’s server.

Other than looking at the historical data provided by the network, other parameters like weather conditions are also taken into account. There can be a possibility that a thunderstorm could switch off a hospital’s network because of a power supply failure. If the effect of weather on the network can be predicted, then alternatives can be put in place just in time. Although such alternatives are already in place for emergencies, what such models will help in is better implementation and preparation.

A popular example is the use of predictive analytics in emergency services is how General Electric Power uses AI to manage its power grids in the US. According to this example, the predictive model helps the company get rid of the scope of manual errors in its system. It says that simple errors made at the service provider level can sometimes cause outages in the whole sector. This can be avoided if the data entry is taken online and passes through a filter that is connected to such a model.

Any mismatch in the data as compared to what is expected of it will trigger an alert and the response teams can quickly get in action. This is already being executed by GE Power even as it finds ways to make the entire grid system automated. This does not necessarily mean the absence of service engineers, but just the absence of potential errors that they are sometimes bound to make.

All of this is possible only because of the presence and availability of historical data. Without it, the predictive analytics models cannot compare the new tasks. This is one of the challenges that new sectors face as they do not have sufficient data to work on.

Some Challenges in this Field

Predictive analytics don’t fare well for environments that are rapidly changing. This means that environments where the relationship between two actions is quick, the model finds it difficult to grasp it and thereby ignores it and moves to the next action. This can pose a problem because it can lead to incorrect prediction, or worse, even dangerous predictions.

Availability of data, as noted above, is another hurdle but not something to be worried about. For sectors like healthcare, power, and retail manufacturing, there is abundant data. The challenge then is to source and save it properly which can be used to create the models.

Experts also point to the lack of implementation on the part of engineers. Scientists are continuously toiling to create predictive models that help in error detection but on-field engineers and workers are not supporting the system by providing or utilizing data. This can be a field engineer working on a local transformer for GE Power or a systems engineer at the grids network office not willing to listen to the model’s alerts. This shows that there is also a need for awareness among workers and engineers. This is definitely a radical change in how things work but embracing them is the only way to make it serve us better.

Predictive analytics, despite its active use in detecting and troubleshooting network issues, is still at a nascent stage in the global scale. While some countries and corporations are innovating in the field with ample help from scientific organizations, the practice will only strengthen when it comes to the mainstream. And that might take some more time.

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