How Machine Learning is Improving Business Intelligence

Simply put, machine learning (ML) is a process a software application uses to actively learn from imported data, using it in a way humans would use past experiences as a part of their learning process. Business intelligence (BI), on the other hand, is a complex field representing a process that depends on technology to acquire, store, and analyze business-related data. The goal of BI is to reach optimal courses of action in as short time as possible, so the process includes several different aspects, such as analytics, predictive modeling, performance management, data mining, etc.

MIT Sloan reports that, according to their survey questioning executives from 168 large companies, two out of five companies have already included ML in their sales and marketing efforts. This information comes as no surprise, as the processes behind machine learning have close ties to those of data mining and predictive modeling. When it comes to processing large amounts of data, there simply is no comparison between what a human and a machine can do, so ML naturally appears on stage as a potent tool BI can greatly benefit from.

There is a lot of information a business can harvest from (potential) customers’ purchasing behavior online. ML brings a significant improvement in understanding a target audience and its needs, providing businesses with valuable information that can be used in marketing in order to skyrocket sales. Data collected from personal profiles (realized purchases, browsing searches, personal details) are irreplaceable, powerful information a company can use to predict, for example, how a new product will be accepted on the market, or which qualities should be included when a new product is made, according to what consumers want and look for.

ML brings significant improvements to the field of employee safety, providing optimized protection for operators working in high-risk environments. Superior monitoring combined with predictive analysis can prevent malfunctions or system failures that could endanger human lives, avoiding accidents before they can even happen. ML can also use the data in order to comprehend and “remember” the causes that have led to malfunctions and in the past.