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Predictive Analytics: Machine Learning, Artificial Intelligence

We all know that knowledge is power. But in this data driven economy, knowledge about the past is probably not good enough. It is merely a rear view mirror that may reveal historical information that makes you feel like hindsight is 20/20. As the amount of available data explodes and technologies advance, businesses and organizations that can take advantage of the data to gain predictive insights will be ahead of the curve and have a competitive edge.


This is where predictive analytics come in. Predictive analytics is an advanced data analytics technique that utilizes machine learning, artificial intelligence, statistical and data mining algorithms to process and analyze vast amount of historical data to find patterns and build models that can forecast the likelihood of future outcomes.

Applications of predictive analytics and machine learning in general are virtually endless and they practically touch our life almost every day. When we deposit a check at an ATM machine or with our smart phone, certain machine learning algorithm is trying to decipher and convert handwriting on the check into text via OCR. From facial recognition, voice to text, spam email detection to  credit card fraud detection, online shopping product search, Netflix movie recommendations, loan application approval/rejection, and so on just to name a few; predictive analytics is in action. Success stories are abundant in business intelligence applications, scientific discovery, medicine, healthcare, sports, cyber security, machine maintenance, weather forecasting, earthquake prediction, mining, oil and gas exploration, law enforcement…. and on and on. It is fair to say that what we can do with predictive analytics is only limited by our imagination.

Businesses have strong incentives to embrace predictive analytics, especially in the areas of Inventory management, demand forecasting, Risk assessment, targeted marketing, customer churn detection/prevention, customer segmentation, product recommendation, financial modeling, human resource management, etc. The benefits are obvious. They can make better decisions to reduce waste, improve efficiency, lower costs, retain customers and increase profits.

In the past, due to the prohibitive cost and lack of data science resources, access to machine learning technologies by small to medium sized businesses could be a pipe dream at best. However, the proliferation of matured cloud based tools in recent years has basically democratized the adoption of predictive analytics.