It takes more than a machine to define normal (via Passle)

Machine learning and the holy grail of anomaly detection are getting a lot of attention from investors and businesses at the moment. A subset of artificial intelligence, machine learning explores the study and construction of algorithms that can learn from, and make predictions on, data. To detect an anomaly, you first have to determine what is normal and this is easier said than done for the majority of businesses. Anomaly detection requires an organisation to define roles and responsibilities and to put in place robust identity and access controls – all of which pose the question: if this was defined and in place, would I need anomaly detection anyway? There is huge scope for machine learning to become effective, simply because of the way we can now collate, store and analyse data with new business dynamic SMAC (Social, Media, Analytics and Cloud). Machine learning is now being used for assigning hospital beds to root cause analysis for quality improvements and for advanced marketing activities to personalise the consumer shopping experience.