Machine Learning

Spotting the odd one out: How Machine Learning can help in Anomaly Detection

90% of the data in the world today were created in the past 2 years! Right now you, me and the whole world put together churns out 2.5 quintillion bytes of data created every day, says the tech behemoth IBM.

Even a single standalone enterprise is churning data from thousands of data points. Right from employee attendance to inventory that is moving out from factory floor to customer doorstep, tons of data is created and curated on a second-to-second basis.

In fact, advanced ecosystems like the Internet of Things create even more diverse datasets like weather, pressure, speed, motion, touch, etc. which multiplies the volume of data that is produced. But, all these data will remain as mere binary digits if they are not utilized properly.

As the adage goes, information is wealth, but using it wisely is the key to success. If all this information is combined, classified and analyzed it can throw open deep insights into how the business and customer experience can be improved substantially.

However, making use of data is easier said than done. As the volume of data climbs in volume, analyzing it and arriving at conclusions quickly is difficult. And when there is an anomaly in this large chunk of data, enterprises are merely handicapped to identify what caused the anomaly or how to fix it so that it doesn’t repeat.

It is here that anomaly detection comes into play. Anomaly detection relates to data mining and can be described as something which deviates from the standard or expected result.

Why is Anomaly Detection is Critical for Data-driven Enterprises?

In today’s age of interconnected operations, companies rely on real-time data to see the big picture of their business. While their intentions are right, the mad rush of coordinated activity taking place at scale makes it challenging to spot anomalies.

For instance, a social media marketing campaign would have received a massive response on the Internet, but a slight glitch on the checkout page of the store would result in a reduced conversion ratio.

In other cases, seemingly unrelated causes could result in a serious defect. Like a leaking boiler resulting in the entire production line to shut down. Anomaly detection would help spot that the boiler’s inefficiency and alert the technicians to take proactive action.

Similarly, in the virtual world too, anomaly detection can help point the finger at situations that are abnormal. For instance, too many failed login attempts signaling a possible brute force attack. Unusual financial transactions that do not represent the normal nature of the business and so on.

While the possibilities of anomaly detection are limitless, a single person cannot weave through massive amounts of data to spot anomalies. There is a need for a scalable system that can automate the whole process.

Enter, Machine Learning for Anomaly Detection

Anomaly detection is usually done using a software code that weaves through data. Machine learning, on the other hand, is a self-learning system that does not need to be trained explicitly. The system learns from data patterns and delivers predictions or answers as it is programmed.

Machine learning for anomaly detection will help improve the accuracy with which predictions are made. The self-learning systems build data models based on regular data patterns and continue to accumulate patterns based on preset conditions. When either of the conditions is breached, an anomaly is spotted, and an alert is raised.

Here is how anomaly detection using Machine Learning happens from beginning to end:

  1. The machine learning system is provided with datasets
  2. The ML system develops data models from the datasets provided
  3. A potential anomaly is raised each time a transaction that deviates from the model
  4. A domain expert approves the anomaly as correct or wrong or acts upon the prediction
  5. The system learns from the action and builds upon the data model for future predictions

As is evident from the above, the whole process is refined and improved each time an anomaly is spotted. This makes machine learning a reliable ally for businesses that produce massive amounts of data on a regular basis.

Perfomatix Dataramp AI Platform – A next-gen AI & ML platform for anomaly detection

Perfomatix DataramAI platform is envisioned as a ready-to-market and fully-scalable AI and ML platform that enterprises can use to build a system for anomaly detection.

Dataramp AI platform is built on a sturdy tech stack that boasts of cutting-edge data analytic capabilities, real-time processing and omnichannel data gathering for a single reliable and accurate prediction.

Dataramp’s AI platform will help facilitate faster identification of anomalies before they grow into bigger problems that could affect the business as a whole.