How Machine Learning Drives the Competitive Edge for Business Success
How Machine Learning Drives the Competitive Edge for Business Success
Machine learning (ML) has already arrived in the public consumer marketplace, though many don’t realize it. In fact, that lack of awareness is exactly an indicator of ML’s success. The success of machine learning happens behind the scenes, and is intended to provide intuitive solutions to many of the problems consumers share. Here, we’re taking a deeper dive into ML technology and how businesses are using it today.
What Is Machine Learning?
Tech companies have been exploring the concept of artificial intelligence (AI) since the invention of the computer. Over time, the definition of AI has developed into a concept: the point in which a computer performs in such a way that it is indistinguishable from human thought. Machine learning is a subset of AI.
Machine learning involves designing computer programs, a.k.a. the “machine” part of the name, to solve real-world problems on their own. They then are expected to use that solution to solve new, unrelated problems–the “learning.” The programs take data, apply logical rules to analyze the data, produce conclusions, and use what they learn from that process to create new solutions. This can be applied to the realm of customer service, in which AI chatbots field customer requests and respond dynamically. Additionally, it can pertain to ecommerce platforms that use consumer data combined with machine learning to recommend the best products to users.
Don’t let the way the entertainment industry has demonized the rise of the thinking machine cloud your perceptions of this technology. The reality of ML is much more innocuous than taking over the world, and instead has exciting potential for businesses in every field.
A Perfect Storm of Opportunity
Today’s digital world has grown with more complexity than ever. Computer processors have become smaller, faster, and cheaper; data storage is practically unlimited; sophisticated software applications are now in the hands of the average user. For businesses, data collection about consumer trends and needs has become routine and essential.
The challenge for businesses is what to do with all this data. Many firms employ data scientists, or people whose responsibility is to sift data for valuable insights. Machine learning takes that idea and shifts it into high gear.
The significant aspect of ML is that, given a set of criteria and the data to apply it to, it can sift through applications to find patterns and conclusions that can escape a human reviewer. The level of granularity also becomes remarkable, with machine learning able to synthesize enormous amounts of data for more precise categorizations and connections.
Machine Learning Has Potential Across Industries
Regardless of industry, every time a business collects a data point, that information can inform it about its customers, products, and services. The entire concept of search engines like Google work by learning what are the best results to produce for user searches. By analyzing search words, clicked links, frequency of citations, and more, they build their business toward delivering the optimal answers for users.
Netflix, another business in the work of providing the best service to subscribers, aggregates viewer information to create profiles of what its customers will enjoy and make recommendations. The data collected about its services provides insights on where to spend its budget, how to market for new customers, and refine its customer satisfaction.
The applications of ML can go beyond customer services, and present unprecedented potential in terms of practical effects. For example, tools designed to recognize particular items in photographs are being refined beyond that task. This would pertain to when a user is searching Google for images with old telephones, and the most relevant ones appear.
ML is also being used as a diagnostic tool in medicine by using that same concept to search for anomalies in X-rays and MRIs. With the level of granularity a machine can provide, ML is even finding potential cancers that would not be visible to humans until much later in its development.
Driverless cars, security fraud analysis, online security are all diverse industries with a common tool they’re leveraging for competitiveness: machine learning. In today’s global marketplace, machine learning is becoming imperative for cutting-edge creativity and boosting customer satisfaction in every field.
