How Machine Learning Unlocks the Power of BI
Machine Learning is the buzzword of the moment. In recent years, news stories raving about its possibilities have soared, Google searches for the term have quadrupled, and companies across the globe have been scrambling to figure out how to capitalize on the excitement by bringing it into their product mix.
While that can be a great thing, claims made by some businesses about what Machine Learning can do are wildly exaggerated. That makes it crucial to cut through the noise and get to grips with its potential, limitations, and what you can realistically achieve with your resources so that any investment makes solid business sense — so say Philip Lima, CEO of Mashey, and Boaz Farkash, Head of Product Management at Sisense. The pair joined forces to deliver an in-depth webinar on Machine Learning and business intelligence, which you can view in full here.
The definition of Machine Learning is actually very simple, says Philip. It’s a system that trains itself to come up with the correct output based on the inputs it’s been given.
When you apply for a credit card, you give details like your name, address, and so on, which the Machine Learning application merges with other data such as your credit score. Based on these inputs, the algorithm assigns you a profile, assessing your likelihood of repaying this credit, and approves or denies your application accordingly.
Simple uses of Machine Learning permeate our day-to-day lives. Consider spam filters, which essentially guess whether a message is junk based on how closely it resembles emails that previously earned this tag.
More recently, though, these basic applications have evolved into “Deep Learning,” allowing software to perform increasingly sophisticated tasks with considerable implications for the way we do business.
Today, as Philip points out, you can deposit a check with your phone simply by taking a picture of the front and back. The algorithm identifies all the important bits, figuring out the amount, name and account number, verifies that it’s real and unused, and then proceeds with making the deposit.
Or take the phenomenon of your iPhone warning you that it’s time to leave for your appointment, based on how long it thinks this will take under current conditions. For that to work, an elaborate process has to happen, taking in information from your calendar, figuring out your location and likely routes, calculating how much traffic there is, and then, combining all these inputs, output advice on when you should head off.
These are both examples of Machine Learning right under our noses. According to Philip, in 10 years’ time, you’ll be hard pressed to find any tech that doesn’t incorporate Machine Learning – and one of the most intriguing areas where this is the case is — you got it — business intelligence.
