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How does predictive alerting work?


How does predictive alerting work?


Predictive alerting is a technology that predicts events based on historical data and accordingly provides alerts. For example, the technology can predict the sales of certain products based on sales and other data from the past, and it can send alerts to appropriate people when stock is expected to be low. Predictive alerts, though still in the evolutionary stage, are potentially a useful tool across many industries such as banking and finance, defense, IT security, e-commerce, online learning and even medical sciences.

Predictive alerts can be said to be a branch of machine learning. Machine learning is the field of machines learning from new, varied datasets and applying the learning to other situations. The act of learning by machines can be likened to learning by human beings who learn and are enriched from different experiences and apply the lessons to different situations to solve problems. Software applications based on predictive alerts process large, varied datasets and learn from the datasets.

Based on the learning, the applications create data models and apply the models to other problems. For example, in the medical sciences domain, patient and weather data for the past few years can be processed and analyzed to discover crucial information about the outbreak of certain diseases. The machines can analyze and correlate the data to link the onset of certain seasons with certain diseases, such as the fall season with asthma and allergies. Based on that, it can send alerts to doctors and hospitals with predictions on which diseases are likely to strike when. Hospitals and clinics can accordingly plan.

The main challenge for predictive alerts is accuracy. While it can be armed with any number of sophisticated algorithms, it needs to process the data and use the data to accurately predict events. This is the reason it is still treated as an evolving technology. The predictive models are constantly updated as new data comes in so that the models are able to predict accurately. A few prominent companies, especially those in online retailing, have been using alerts for different purposes, such as displaying accurate product recommendations for visitors based on their product search history and preferences. Such recommendation engines are able to fairly accurately predict the choices of potential customers.

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Kaushik Pal

Kaushik is a technical architect with 15 years of experience in enterprise applications and product development. He has expertise in Web technologies, architecture/design, Java/J2EE, open source and Hadoop/big data technologies.

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