Bridging the Gap between Business Processes and Metrics Using Machine Learning-Based Anomaly Detection

Enterprises find themselves in a state of uncertainty due to the world of possibilities that big data has opened up. In the present day, there is more data available than ever before. While this should be beneficial to businesses, it also poses challenges, one of which has to do with implementing the right processes to turn data into actionable insights.

As organizations grapple with the need to make decisions faster, massive and seemingly unmanageable datasets are turning from asset to liability. Machine learning is touted as the solution to this conundrum. One of the most outstanding benefits of machine learning to business is its potential for anomaly detection.

What is Anomaly Detection?

In the most basic terms, anomaly detection is the identification of unusual patterns or behavior that does not conform to the norm. Sporting such abnormal, irregular or unusual behavior in a dataset may not always be within human ability to notice, especially when dealing with big data. And this necessitates the use of machine-based anomaly detection algorithms.

How Does Machine Learning-Based Anomaly Detection Work?

Essentially, machine learning-based anomaly detection facilitates the automated analysis of existing datasets to determine normal behavior. After determining the parameters of normalcy, the algorithms would then be in a position to identify pattern breaches that signify anomalies.

Such solutions have widespread applicability in the business world and beyond. Not only are they able to monitor large datasets and flag inconsistencies in real time but they are also eliminating the challenges of manual monitoring. Moreover, their ability to become more intelligent over time enhances performance as they build experience.

Business Processes and Metrics: Using Machine Learning to Bridge the Gap

In order to answer the above question, let us begin with an explanation of business processes and metrics. Business processes refer to a series of structured activities that are aimed at achieving a specific organizational goal. Business metrics, on the other hand, are quantifiable methods that a business applies to track and monitor the success (or lack thereof) of business processes. Business process metrics examples include tracking sales revenues, losses, network performance and overall business productivity among others.

There are a number of areas in which machine learning can bridge the gap between the two. Let us consider some:

Cybersecurity Strategy Implementation

Given the prevalence of cybersecurity threats today, a majority of businesses have strategies in place to manage the risk. But the problem with most of these strategies is that they employ a reactive rather than proactive approach.

Malicious actors in many cases are way ahead of organizations’ IT departments. Consequently, the measures that most organizations implement are unable to capture threats before they infiltrate the system.

But with machine learning-based anomaly detection, the security system does not need to keep up with malicious actors. Rather, all the algorithm needs to do is to learn the parameters of normal behavior. As a result, it is able to detect deviations from the norm and flag them as potential security breaches.

With such information, the IT team can act immediately to stave off the threat as they investigate. This proactive approach is more effective than working to manage the ill effects of a security breach.

Fraud Detection

The same principle applies when it comes to fraud detection. One of the top challenges that hamper fraud detection is the fact that fraudsters are always learning from their mistakes and evolving in their approaches. Creating a rule-based system to counter their attacks is therefore almost always an exercise in futility.

In order to keep up with them, companies have been investing billions in fraud detection initiatives. Machine learning constitutes one of the biggest allies in this, providing a unique solution that is a cut above the rest.

Machine learning models are trained based on available data to recognize fraudulent and non-fraudulent transactions. In addition to this, they get an opportunity for self-learning so as to be able to identify new trends that could be missing from existing data.

At the end of it all, using anomaly detection, the model can identify both existing and previously unseen suspicious behavior that could point to possible fraud.

Network Performance Monitoring

If the performance and success of your business bears some relationship with the ability to stay online, you must appreciate the value of network performance monitoring. As you may already have realized, keeping a network online requires a delicate balance. One has to juggle the concerns of managing hardware (physical and virtual) against degradation and cyber threats.

One of the challenges that arise is that cyber threats are getting more and more advanced by the day. Additionally, consumers have more options than ever before and the slightest network failure can have a massive impact on sales and public perception.

In efforts to keep up with the changing times, many businesses are opting to incorporate machine learning algorithms into their network monitoring models. These tools have what it takes to catch performance issues before they begin to take a toll on the network. More importantly, they are fast enough to respond in real time.

How do they do this? Machine learning algorithms do this by learning normal usage patterns and identifying anomalies. For instance, they can easily take note of a malicious attack from such pattern analysis. Their responsive approach minimizes downtime and optimizes performance.

Seizing Opportunities

Real-time anomaly detection can also help a business to seize fleeting business opportunities to optimize gains. For instance, if you run an e-commerce business, you might notice a sudden spike in demand and purchases of a specific item.

Without the right tools to identify the reason behind the uptick, there is little you can do. But with a machine learning-based anomaly detection model, you can achieve so much.

To begin with, you can attempt to track down the item’s social media metrics. From this, you might learn that the item recently got a celebrity endorsement. Or it could be that demand is high due to an upcoming event. In that case, you can confidently order more stocks to ride the wave of opportunity while it lasts.

Furthermore, you can think of adjusting the price or bundling the item with a product that is experiencing low demand so as to boost revenue.

­Real-Time Anomaly Detection Critical for Business Success

Discovering and acting on anomalies in real time can make the difference between success and failure. Machine learning allows entrepreneurs to take a proactive approach and bridge the gap between various business process and metrics.

Admittedly, few things do as much harm to brand reputation as business incidents. These often result from seemingly minor things like network outage, poor stocking of items in high demand and consumer data breaches among other things. On the other hand, positive reports can boost performance to unprecedented levels.

The application of machine learning approaches to anomaly detection makes a world of difference. It allows business operators to identify problems and address them in-house, in real-time before they cause widespread damage. Moreover, it spares entrepreneurs the heartache of finding out about issues from disgruntled customers after the damage is already done.

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Janica San Juan

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