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How Predictive Analytics Boosts Your Engineering Software Management System

It is common knowledge that engineering and other technical software can be very expensive, which is why companies and organizations find that employing the best software usage metering tools is crucial in maximizing their investments and optimizing license availability.

However, advanced usage metering tools can do more than just monitor the current status or report on past trends.

It can also be used to forecast future trends and detect possible anomalies in real time through Predictive Analytics, which could potentially open many doors for your engineering software management system and decision-making process.

Predictive Analytics is a branch of advanced data analytics that uses historical data, analysis, statistical models, and machine learning to establish patterns and predict future outcomes and trends.

Historical data is analyzed using statistical methods that consider key trends and patterns in the data to create a predictive model.

The model is then applied to current data to provide probable future trends. It does not tell you what will happen, but it provides a forecast of what may happen in the future with an acceptable level of reliability.

Simply put, Predictive Analytics is technology that learns from experience (historical data) to predict what may happen in the future.

Having a glimpse of the future could certainly lead to better planning and decision-making, but even more importantly, it could give you precious time to respond quickly to possible risks within your system.

The following are some simple ways that Predictive Analytics can help in managing your engineering software environment.

Detecting Possible Anomalies

Time is a valuable resource, especially when dealing with possible risks in your engineering software environment.

Since Predictive Analytics establishes future usage trends with an acceptable level of reliability, it would be prudent to investigate if the actual usage falls far from the predicted outcome.

We call these events “anomalies”.

There are several reasons that anomalies occur. And these reasons could range from relatively harmless, such as a few employees reporting sick or failing to come to work thus skewing the usage data, to the catastrophic, such as a complete system breakdown.

Did a license server unexpectedly shut down?

Was there a malfunctioning system somewhere?

Did an entire department lose their network connection?

Was there a breach in security?

Did someone or something inadvertently overload the system?

In any case, each anomaly should be properly investigated.

A good Predictive Analytics tool should alert you immediately when a possible anomaly is detected. Early detection is key in making sure that something easily fixable would not turn into a larger problem that could take so much time and resources to resolve.

And it would be wise to have an established set of procedures for the proper and thorough investigation of every anomaly.


The most obvious benefit of applying Predictive Analytics to software usage data is learning possible future usage trends.

Knowing when and where users will need to use applications is crucial in optimizing license usage, resources allocation, and future license purchases.

If you can predict that July is when Team A would have low usage, maybe it would be the perfect time to deploy a new software.

Or maybe you could reallocate the licenses to Team C, which your forecast may indicate would have high demand during that time.

With knowledge of future usage trends, the possibilities are endless!

Pick the best time for adoption of new technology. Know when the optimal time to start a new project is. Schedule the most suitable time for training.

Allocate resources with the benefit of foresight. Mitigate risks of production delays due to possible denials.

The only setback with forecasting is that it will need to accumulate a lot of historical data before it could make useful predictions.

However, the beauty of Predictive Analytics is that the more data it collects and analyzes, the more accurate its forecasts will be.

As you accumulate more data, predictions will become more precise. And more precise forecasts mean better planning and decision-making on your part.


Predictive Analytics can turn your already effective engineering software management system into something even better.

Anomaly detection allows you to be on top of possible risks within your engineering software environment.

And forecasts provide you with the necessary knowledge to make better plans and decisions regarding your engineering software assets.

With these new tools at your disposal, your engineering software management system will go beyond monitoring.

Find out how engineering software management tools helped companies like Murphy Oil gain millions of dollars in savings from optimizing their engineering software assets.

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