16 billion was stolen from 15.4 million U.S. consumers in 2017, reports Javelin Strategy & Research.
Customers and businesses alike are being exposed to serious frauds as cybersecurity criminals become adept at sophisticated hacking methods. The risk of account takeover, identity fraud, online shopping scams and similar frauds has a 200% higher probability than the previous year’s rates.
There is always some kind of internal threat or external factor-induced threat conditions that businesses must set up defenses against. Given the volume of data that organizations have access to, real-time anomaly detection can be a reliable technology to spot risky situations and take remedial actions before things go south.
But, why real-time?
Would you cross the road based on a traffic signal that is five minutes old? The chances of being hit by a vehicle are less if you travel based on real-time signals.
The same applies to data and analytics. Historical data helps narrow down on the causes that led to something. It does help in planning better for the future. But, for digital businesses, real-time anomaly detection can help predict possible problematic scenarios beforehand to take preventive action.
It also saves the organizations from a series of disasters that a potential anomalous incident can lead to. Imagine the fraud incident in a bank can do to its reputation. There would be a volley of negative press coverage, social media shares, statutory investigations and many other upsetting effects that can cost the business significantly.
It is here that real-time anomaly detection can position itself as a proactive problem-identifier.
How Real-time anomaly detection helps in Business Intelligence?
Real-time anomaly detection can help enterprises combine indicators and anomalies from diverse datasets to create a pattern of how an ideal data set would perform.
It contributes to business success in a number of ways, such as:
Helps connect millions of data points
Social, Mobility, IoT, wearables, AI personal assistants and Cloud are generating Big Data which are inherently complex. Analysing these diverse and often interconnected datasets in the size of Petabytes to Exabytes is difficult.
Traditional Business Intelligence systems are not capable of handling such volume and variety of data. Even if they can, they will be slower and would require significant manual intervention for proper functioning.
Real-time anomaly detection, powered by Machine Learning and Artificial intelligence can help connect the dots between the heterogeneous datasets. They can see through the data and spot anomalies by comparing the real-time data to ideal thresholds.
Proactive BI for loss prevention
Real-time anomaly detection helps enterprises adopt an approach that analyzes data as and when it is happening. It detects the possibility of a threat even before it happens by connecting the past incidents and comparing them with preset thresholds.
Virtual private assistants like Apple Siri, Google Now and Windows Cortana can predict their user’s needs with impressive accuracy. Advanced forms of these systems can be used to collect business intelligence and synthesize them for anomaly detection. The findings will help in preventing anomalies that might possibly cause losses.
Helps make faster & accurate decisions
Machine Learning based anomaly detection scores high where manual anomaly detection fails. Manual anomaly detection cannot be scaled at the pace at which data is created. Manual resources are also not quick enough in scanning large volumes of data. The process followed in data analytics is also prone to errors.
Real-time anomaly detection gives results instantly which leads to faster decisions. Since the predictions are data-driven, they are more accurate than manual processes giving businesses all the edge that they need.
Real-time anomaly detection for your business
A digital transformed business that has continuous data churn from machines, systems, sensors, and applications must leverage real-time anomaly detection. It gives end-to-end visibility of the business as well as helps narrow down on data instances that exhibit abnormal characteristics.
The real-time detection of these outliers helps in taking proactive corrective actions that will save the business from serious consequences, equipment downtime, financial mishaps, material losses to name a few.
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