There’s a lot of confusion surrounding machine learning and for good reason. It’s very new as a mainstream management tool (as opposed to something only data scientists would use) and is indeed a fairly complex technology. The thought that computers could take in a huge amount of data and not only make sense of it, but learn along the way, is unfathomable to many.
Similar to the confusion and learning curve that accompanied Internet technology, a solid understanding of machine learning and its true applications takes time and a willingness to dive deeper. The following are four myths that often accompany conversations around machine learning—and evidence of the contrary.
Myth 1: Machine learning technology is a black box.
A black box. A big unknown. A technology that sales and marketing teams are calling out in name only—giving you little or no insight into what’s going on “behind the curtain” so to speak. Machine learning is not in fact a black box. A technology with early foundations in classical statistics but not restrained by its preset assumptions, machine learning can yield actionable intelligence that human analysts cannot see on their own. It is both explanatory and predictive. And, machine learning has the ability to impact future events. For example, machine learning can help companies predict churn and act on churn signals, based on consistent customer data analysis.
Myth 2: Machine learning needs lots of data
Some have said that the only way to make machine learning effective is to be sure it has massive amounts of data to digest from the very beginning. The truth is, having high volumes of data is not a prerequisite for machine learning at all. What is a prerequisite, however, is that a company has good, clean and well-curated data. In fact, having gigabytes of clean data is much more preferable than petabytes of messy data. In fact, data scientists recently cited messy, disorganized data as a key hurdle to overcome in regards to achieving accurate results with predictive analysis and customer data mining for behavioral patterns and future trends.
Myth 3: Machine learning requires in-house data science teams.
Though machine learning is a highly sophisticated technology, the “app-ification” of machine learning has made it a very business-friendly, easy-to-use tool. Integrating machine learning technology into your existing business processes does not require hiring specialized data scientists to ensure it “runs right.” More software is moving to the cloud, and machine learning applications are able to take advantage of the secure, open APIs and integration points in order to add an intelligent layer to those CRM, marketing, and support systems where the data and interactions reside."
Myth 4: Machine learning is designed to replace people
Perhaps the most troubling myth of all is that human jobs are being lost to the technology. In fact, machine learning is being used by companies as an assistive tool that allows employees to perform their jobs better. Machine learning applications serve to take on the mindless, repetitive and time-consuming tasks so employees can spend their time doing the creative, empathetic work that only humans can do.
In its infancy, machine learning was a mysterious technology. It was available only to data scientists and statisticians and was referred to by Hollywood as a kind of artificial intelligence solution poised to take over the world. Although these outlandish presumptions can make for good movie plots, they are indeed just myths rooted in lack of knowledge and fear of the unknown. Today, machine learning technologies are readily available to the masses, able to provide companies actionable, predictive insight, and can help companies reallocate their employees to higher value, more engaging roles.
If you’d like to learn how Wise.io can accelerate your understanding of machine learning and direct you to the machine learning capabilities and applications that will serve your business best, download our ebook: The Essential Guide to Automating Customer Service.