6 everyday business practices of machine learning
Machine learning may be one of the most important yet misunderstood technological advances in recent years. It’s on the lips of those in and peripheral to the tech industry almost as much as “disruption” once was. To the uninitiated public, machine learning may sound like the stuff of science fiction – ushering in the singularity or necessitating the search for a better killswitch for rebellious robots. In reality, while still an exciting branch of computer science, machine learning is much more mundane and used in a variety of applications that the average person uses on a daily basis.
The most basic explanation of machine learning is that it takes data points and uses statistics to predict what that data set would look like if extrapolated further. Computers are able to parse larger data sets than the human mind could ever be capable of, which allows us to see correlations that are invisible to the naked eye.
To bring all of that back down to Earth, those mathematical predictions are actionable in a number of ways. The technology they have facilitated is already all around us. As a business, it’s important to recognize that and take advantage of it. The number of already extant uses of machine learning for businesses is impressive and growing every day.
Whether you’re concerned with search engine optimization for your marketing strategy or with an internal search engine for your company intranet, you’re dealing with machine learning. These mechanisms gather data on what is searched for and by whom in order to promote the most helpful content. If you don’t already have an intelligent search engine as a piece of your company’s intranet, this can make a world of difference in terms of knowledge management. For SEO, it’s important to understand how Google’s search algorithms work to elevate your page ranking. Either way, machine learning is working behind the scenes.
Closely tied to machine learning is the field of artificial intelligence, and a shining example of AI for businesses is x.ai. X.ai makes a virtual assistant that answers emails and schedules meetings on your behalf. Almost indistinguishable from a human assistant, this creates hope for a day when the average worker will no longer have to bother with emailsand will be able to concentrate exclusively on higher level tasks. The implications of this kind of technology, able to not only replicate natural language but learn our schedules and habits, is very exciting.
Predicting employee performance
Management in its modern incarnation is an art form, with book after book written on the subject. Unfortunately, it’s also an art form based on more guess work than is necessary. In the future, apps could easily gather data on employee performance and point out areas for potential improvement. These could then recommend appropriate trainings for employees, allowing managers to focus on overall team strategy and helping employees to take action based on quantifiably sound performance reviews.
Sentiment analysis is a process of using algorithms to determine whether people feel positively or negatively about a topic based on the phrasing they use to write about it. This can be used to improve employee engagement by detecting when employees feel particularly disengaged and determining what makes them feel that way. Sentiment analysis also has big implications for marketing teams, especially at a time when large amounts of data may be available through social media on mentions of the brand.
Employees often worry about taking vacation or sick days, always feeling too busy to be away from the office. Machine learning could potentially analyze what times of year are busiest, as well as when the most people are likely to take a vacation, and help to suggest vacation days to employees that will cause the least disruption of day to day business. It can also help predict sickness, so that HR can warn workers about good health practices and managers can plan ahead for unpredicted absences.
Arria, a British company, uses machine learning and what they call “Natural Language Generation” to help generate financial reports based on companies’ proprietary data. This type of technology is big news because a human being could not perform data analysis on the scale required by a company in this field, yet to date they still had to use humans to translate that data into reports. With reporting automated, workers are free to make higher level strategic decisions.
The power of machine learning is in prediction. Computers can take huge sets of data and give us insights into our company’s activities that were never before visible. A common thread in all the uses of machine learning mentioned here is automation. Yet this automation isn’t the type of automation that workers may be scared of, replacing them with machines and leaving them jobless. Computers can’t replicate a person’s fine judgement or many of the higher level processes we bring to our jobs. Rather, they take care of the pieces we don’t want to bother with or give us insights that enable us to perform the more interesting parts of work. Machine learning will continue to expand its role in business processes and digital tools used across companies. Knowing its potential and its range of applications will help to build more agile companies and happier employees.