How to make Predictive Monitoring Valuable for your Organization

Doesn’t it sound magical to predict issues? Detecting a network outage, long before it happens.

Yes! It does sound exciting. Now there are numerous network monitoring softwares out there offering this capability.

To accomplish this particular goal, businesses around the world have been investing in  AI powered network monitoring softwares.

Such tools assist them in tracking, monitoring, and analyzing a range of components of the IT environment in a way that allows an IT team to mediate beforehand any probable concern that can turn into an interruption in current services.

What is predictive analytics?

As the term rightly indicates it is a branch of data analytics using which users can make predictions about their state of IT infrastructure.

It involves data mining, machine learning (ML), and artificial intelligence (AI) to analyze historical data which will be useful to make predictions about the future.

This can help your organization forecast possible outages, failures or breakdowns.

What could be the possible challenges?

As per a report by Zion Market Research, Predictive analytics has captured the global market projected at approximately $10.9B by the next two years, growing at a CAGR of roughly 21%.

Although predictive analytics has been widely adopted by organizations around the world, still many IT admins are skeptical in implementing it.

The main challenge is anticipating and figuring out what to measure and then how to execute it.

To make predictive monitoring actually work for your organization requires having an exceptional understanding of all the components of the IT environment.

If you don’t have that then you will be having troubles in understanding the flow or impact of a problem with any given element in your  IT environment.

Making Predictive Monitoring Useful for your Organization

The capability to put off failures before they happen surely sounds exciting!

It saves the usage of added resources, time, and money, further creating a more flexible IT infrastructure.

As you put predictive analytics to work in your IT department, here are some of the ways to get started.

Recognize root causes for comprehensive application performance 

By discovering root causes for application performance, IT teams can concentrate on the precise set of vicinities in which to take quick action.

In most of the instances, professionals view the overall application, errors, and in-hand performance logs when there is a significant problem with the current IT infrastructure.

One approach that can offer a better insight into an application performance involves collecting all of your log data, along with connected configuration information, and creating numerous clusters with it.

Then you can explore the characteristics of a mixture of attributes within every group.

Those clusters or groups can offer IT teams actionable insights into what measures they can take to achieve perfect performance & avoid performance bottlenecks.

By discovering and exploring the clusters, you can determine which mix of parameters will chip into the best application behavior for a set of settings, and which ones are inclined to lead to errors.

Monitor and track application health on a real-time basis

Enabling real-time monitoring of application health through machine-learning (ML) techniques facilitates IT teams to grasp and act in response to the degradation of application health in a well-timed manner.

Most applications depend on different services to confine the actual health of the application.

It would be best if you explored performance metrics from all sorts of services and their sources, which is a combination of the prediction problem.

Here the key to monitoring application health is to first determine the usual behavior.

To do this you need to start off by compiling all of the accessible data generated by the application.

This data might comprise of the configuration information, application logs, error logs, network logs, performance logs, and much more.

Once you have the information compiled, analyze the precedent data during a time in which the application was in a good state.

With anomaly detection you will be able to pinpoint the deviation from its usual state.

Predict application downtimes prior to their happenings

Predicting application downtime or outages earlier than they happen to lend a hand to the IT teams to kick off backup servers and perform required maintenance on that application without any disruption.

This scenario can save organization resources, time, and investments.

It will also help in saving IT leaders from annoyance. Application outage is a considerable drain on a company’s financial health and is the main pain point for technology leaders.

Ahead of application downtime, the IT infrastructure leaves indirect clues.

The solution to predicting application outages is to build a strong predictive model that is based on historical data and past failures.

You can utilize these data points to find significant patterns before you experience failures.

With this a predictive model in place, IT personnel can take anticipatory action at the correct time.

It is also essential to capture which action you took for diverse conditions and what the outcomes were.