Today, information needs to move so quickly that even the slightest delay is unacceptable. So, network monitoring is essential for businesses of all sizes. That’s especially true if your workforce is not under the same roof and relies almost exclusively on a network.

Yet some owners put off this decision. They believe that monitoring means hiring a consultant to set up a system or adding another task to an overburdened IT department. But new software companies are committed to network monitoring made easy. So, you can take advantage of these benefits.

Avoid Losses

Many people spend considerable resources making their networks resistant to outside attacks. That’s a very smart thing to do. Hackers are incredibly advanced, and not all of them simply want money. Yet, most businesses do not experience serious cyber-attacks.

On the other hand, unintentional network interruptions happen almost every day. All of them lead to costly delays. Some common occurrences include:

• Broken printers,
• Website crashes,
• Empty toner cartridges, and
• Tripped-over power cords.

If these events happen offsite, your entire network may go down. You need to know about these events ASAP. First, you need to be able to address them. Second, you need to reassure partners and/or employees that the problem is well-controlled.

Easy to Use

Setting up your network is that hard part; monitoring it is the easy part. If you could do the first with little help, you can do the second with no help. Most network tomography software has a basic, Windows-based interface. Most of the technical decisions have already been made. There’s very little configuration required unless you want to customize the experience or add features.

Once in place, the software gives you a steady stream of information but does not overload you. Filtered email and text alerts are usually available as well. These messages give both all-clear signs and warn of potential interruptions.

End Manual Checkups

Most daily to-do lists include mundane chores, such as verifying that certain components are in order and then sending out updates. Network monitoring replaces these activities. You and your employees can effortlessly strike an item off the to-do list. Then, you can move onto something else with complete confidence.

Measure Trends

Almost all businesses are data-driven, to one extent or another. Network monitoring provides vital information about usage patterns. You can easily see which devices are bearing the brunt of the work. By extension, you can also see which employees are strained almost to the breaking point with work and those who have some additional bandwidth.

Peace of Mind

Installing additional sensors is about the only customization required in the aforementioned setup process. The slight time investment produces significant returns. There’s nothing like the peace of mind that comes from knowing all is well. This mindset reduces stress and enables you to focus your energy on other, more vital areas.

The benefits of network monitoring are significant. But they are only available to those who partner with a top company that provides solutions to everyday problems.

If you have been following the technology news lately, you may be aware of the pace at which companies like IBM are quickly announcing innovations in Machine Learning. For example, their new services on the Watson platform that incorporates predictive analytics, data mining, and pattern recognition are by themselves new paradigms in the business world. Machine Learning, unlike artificial intelligence, is more about the ability of machines to learn when exposed to more data.

Examples of machine learning in use

In very simple terms, what machine learning does is detect patterns and predict outcomes by using different mechanisms. These mechanisms are based on mathematical models and statistics. For example, the way search technology works. It extracts information, gathers them together, and classifies before displaying it to the end user. Behind all machine learning, there are algorithms that help the machine process and fetch information. In doing this, they could look for patterns, for example, the way Amazon predicts user preferences. Machines are trained to recognize patterns and cluster information. That is why it is also referred to as cognitive automation, it is trained to recognize traits in data.

Supervised and unsupervised learning mechanisms

In one stream of machine programming, the machines are given instructions on what actions to take and the sequence of those actions. They may also be told about expected outcomes and the machine is expected to derive the function that would achieve the result. In such instances, where the input, as well as the output, is provided to the machine, the programming world refers to it as supervised learning. The machines are actively supervised to train them on inputs and then validate the output before being put to execution. Since it is very difficult for users to predict or reasonably guess all outcomes for complex systems, machine learning emerged as an alternative that would let IT systems respond rapidly to changes. All of this, of course, is largely dependent on the machine being provided an initial set of sample data that is best suited for the purpose, determining the appropriate learning algorithms and then verifying the output. Only when the output is verified, can the machine be said to have learned the process and allowed to analyze data sets. This sort of machine learning is commonly used on structured data to solve classification and regression problems.

On the other hand, machine learning is said to be unsupervised when though there is a set of input data, there is no outcome provided to the machine. In such instances, the machine is expected to learn by looking at the data. What it does is looks at data sets and studies their structure and distribution and then identify inherent groupings. For example, it can, by studying data sets, look for common phrases. This sort of analysis usually happens on unstructured data.

Cognitive automation

Though mostly used in the context of IBM’s Watson system, cognitive automation systems are increasingly being deployed across businesses in multiple forms. Such systems essentially use the machine learning algorithms to be able to acquire knowledge from data sets they are provided with. As they keep looking for patterns in the information and process the data, machines are increasingly able to anticipate probable new problems and derive appropriate models based on what they have learned. They are then deployed in many applications that include artificial intelligence (AI) applications, neural networks, robotics, and virtual reality systems.

Cognitive computing is increasingly being recognized as the next stage in the evolution of computing capabilities. It is all about making computing systems more user-friendly and that can interact with the end user based on understanding from signals, what a user wants to do and contextualize the output to deliver personalized outcomes. Be it your car, or your refrigerator at home, applications like Siri, that understand context is becoming the norm of the day.