Advancements in big data and machine learning has increased the efficiencies of analytics driven organisations. Companies that have adopted the culture of data driven decision making have clearly outperformed their competitors. The next step in this AI & ML evolution is to use automated anomaly detection to identify instances that are unusual, appreciate the true reason behind that anomaly and leverage the insights gained to grow the business. When an anomaly is identified in big data, it usually signifies an interesting business event that could hold the key to new revenue opportunities.
- Automated signal classification: One of the key complications in detecting anomaly in a time series data is the large number of signal types present in that data. It is imperative to build a generic framework that can automatically identify the signal type and classify it correctly. Wrong classification can result in the application of suboptimal models and increase false alarms. Signal classification is extremely important to stay alert on top of your anomaly data. While it is debatable that one neural network model would fit all the signal types identifying the signal type and tailoring the model to that specific signal type enables considerable reduction of false positives. This also enables using the right model for the right signal type and deliver a robust anomaly detection system.
- Autocorrelation: There are some key KPIs that are tracked business wide so that business leaders can effectively steer the company in the right direction. And all these KPIs have leading indicators. Leading indicators are the levers that managers can use to improve business performance. Leading indicators are easier to influence through managerial actions but harder to measure. Because of the advancements in automated data collection and increasing ability to use artificial intelligence in an automated fashion, today, one can measure these leading indicators and help managers make the right decision. Automatically correlating these indicators and delivering actionable business insights is important to stay alert on top of your anomaly data.
- Deliver alerts in near real time: When artificially intelligent systems are looking at your data round the clock, it is important that anomalies detected are presented to the decision makers in real time. Easy integration of this artificial intelligence into the messaging platforms that business managers already use is important to remain alert. Smart alerts when delivered by an AI that automatically understands the business context would help in outperforming the competition.
Advancements in artificial intelligence has enabled businesses stay alert on the top of big data. An AI assistant with the ability to go granular, automatically relate it to the big picture and deliver insights in real time is essential for staying alert. Businesses that use this power effectively will outperform the competition in future.
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Kumar is a principal consultant at CrunchMetrics. He is an alumnus of IIT- Madras and IIM- Calcutta. As an entrepreneur, he has co-founded an analytics company and then an omni channel retail company. He has worked in advisory roles for Fortune 500 companies such as Deloitte and Tesco in various multinational locations. He has also worked in technology roles for MNCs such as Cognizant and Virtusa. He is a Good Reads author with the pen name Khun S. Kumar and has published seven novellas in Amazon.