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EXCLUSIVE: The growing use case for anomaly detection

By , Business Analyst at TechSoft International
Africa , 25 Aug 2022
Mandisa Kopotja, Business Analyst at TechSoft International.
Mandisa Kopotja, Business Analyst at TechSoft International.

Downtime is one of the most significant causes of non-productivity across South African businesses regardless of the industry sector. If equipment fails at a manufacturer or mine, workers cannot do their jobs resulting in significant financial losses. For other companies, getting ahead of volatile supply chains, labour shortages, and environmental issues might be imperative to avoid systems going offline. The need to implement anomaly detection is fundamental to building this resilience across operations.

Anomaly detection alerts the organisation when something different is happening from what is expected. While this is not necessarily a bad or good thing- it is vital for a business to know when there is a break in pattern to assess whether it must act.

One of the reasons why this is becoming so important is due to the sheer amount of data organisations generate daily. So much of this data lies unused or is even forgotten about. 

This is why anomaly detection is gaining prominence as business leaders turn to more innovative ways of optimising operations and streamlining processes when having sight of a more predictable environment.

Cost control

With the right anomaly detection capabilities in place, a company can benefit from significant operational cost reductions while improving the quality of its products. If a manufacturer is alerted of anomalies in the production process, it can reduce defects. Furthermore, businesses can proactively identify where preventative maintenance must occur to lower the risk of equipment going down.

According to some estimates, large plants globally lose 323 production hours on average per year, which translates to almost US$532 000 per hour when it comes to an average cost of lost revenue, idle staff time, and the impact of restarting lines. By using anomaly detection to understand data better, manufacturers will be able to stop a minor issue from becoming a widespread, time-consuming problem that affects operations.

This is where artificial intelligence and machine learning solutions can help track trends, identify opportunities and threats, and deliver a compelling anomaly detection value add to mitigate against the risk of these issues curtailing operations. These advanced technologies bring real-time analytics that can reveal the root cause of the problem and provide guidance on measures that must be implemented to prevent a similar thing from happening in the future.

Making it work

Detecting anomalies in real-time or even predicting where they are likely to occur can be accomplished by incorporating several technology capabilities into the organisation. 

Firstly, visual discovery, where analysts can build data visualisations to make the information more accessible and understandable. In this way, organisations can identify unexpected behaviours far quicker.

Secondly, supervised learning occurs when a machine learning model is built using the experience of human specialists. The technology effectively learns by monitoring experts in their field and can apply that to implement automated anomaly detection at a scale that overcomes the practical limitations of the workforce.

On the other side of this is unsupervised learning. This is where unlabelled data is used to develop machine learning models that predict new data. Because this model is focused on ‘normal’ data, any data points that are uncommon to this will stand out.

Practical examples

Anomaly detection can be used to identify suspicious banking transactions in real-time. By identifying abnormal transactions, clients, suppliers, and financial organisations can eliminate false positive investigations and reduce losses associated with the fraud.

The growth of the Internet of Things means virtually all devices in the manufacturing environment are connected to the Web. Manufacturers can track all their equipment, vehicles, and machines in real-time using embedded sensors. Anomaly detection can help prevent costly breakdowns and disruptions while identifying data patterns that could indicate impending problems.

Another sector that can benefit from anomaly detection is healthcare. Insurance fraud has become common, with providers suffering significant financial losses due to making pay-outs to fraudsters. This has seen many organisations investing in big data analytics to build models capable of detecting insurance fraud. By spotting anomalies in the claims process, the chances of successful fraud are significantly reduced.

Getting prepared

Anomaly detection and its resultant insights can help organisations save time and money while reducing the effort it takes to identify potential problem areas. As machine learning and artificial intelligence become commonplace, anomaly detection will benefit organisations in virtually every industry segment.

As the use cases for this technology grow, so too will anomaly detection be seen as a business imperative that no organisation can afford to be without. At a time when data is generated at scale, extracting insights to facilitate business growth while minimising disruption will be essential for future success.

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