If a robot can solve Rubik’s cube in 0.38 seconds, imagine what machine learning could do for your business.
Machine learning (ML) is transforming the way we interact with the digital world, and businesses can’t ignore it if they want to compete in the future. From driving cars to helping doctors diagnose diseases, ML is advancing everyday life at warp speed.
However, getting started with ML can be daunting. Before diving in, it’s important to first understand what ML is, how it can improve your processes and what steps you must take to properly implement ML into your organization.
The Importance of Machine Learning: Better Business Outcomes
Machine learning, a subset of artificial intelligence (AI), uses algorithms and statistical models to enable software applications to more accurately predict outcomes without being explicitly programmed to perform specific tasks. By identifying patterns of past data, ML excels at tasks such as speech recognition, pattern recognition and translation. And ML systems use that information to make decisions and execute processes more efficiently. Many businesses use ML systems as inroads to AI because ML is tied to data they already have or can easily access.
Organizations that find the most success with ML start by identifying where it can be most useful — an area where available data could help predict the future and improve outcomes. Examples include a company’s marketing team using sales and account data to identify the ideal customer profile, or a hospital leveraging its extensive database of patient and operational information to improve medical outcomes.
Think, too, how big companies such as Netflix, Facebook, Amazon and Spotify use ML to interact with consumers daily. It isn’t magic that comes up with recommendations of shows you might like to watch on Netflix — it’s a machine.
Although ML does the heavy lifting of sorting through complex data and processes, it still requires human oversight. ML needs someone to determine which data sets to evaluate and ensure that data is clean and free from human bias, which could get replicated and skew results.
Take, for instance, what happened when human bias was purposely used to attack Microsoft’s AI chatter bot Tay in 2016. For 16 hours, Tay went rogue and tweeted politically incorrect messages after users exploited a vulnerability — in this case, targeted human bias — in the system. After more than 96,000 tweets, humans finally intervened and shut Tay down.
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Getting Started With ML: Put the Right Planning and People in Place
The most important first step is to identify an area or process in your business that has the greatest potential value to gain from optimization. Clearly define what you want to accomplish with your ML solution, and then identify the people who will need to be involved to ensure that the plan succeeds. Most likely, you’ll want to choose a process that is complex and inefficient rather than a process that already runs smoothly. That’s the best way to maximize your return on investment.
Make sure, too, that it’s an area where significant amounts of data are available or easily accessible. For instance, think of the constantly changing data you’ve amassed on your customers. Now imagine how much more detailed the insights you derive from that data could be if you leveraged ML’s processing power. Using ML, you could go beyond basic demographic statistics and get more nuanced views of your customers by analyzing every click, download or purchase they make.
As mentioned, many companies get the greatest value from ML by enlisting the help of experts with technical training in machine learning and AI. These specialists could be in-house data scientists or employees of third-party firms; their job would include ensuring that your data is clean, standardized and properly stored. Experts like this can help harness big data and incorporate it into your ML system.
You might even consider applications that seamlessly incorporate the power of ML into business tasks or a low-code platform that integrates with ML tools. A low-code platform is often the most versatile and economical way to tie ML into a process and maximize its value.
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The Danger of Ignoring ML: Losing Ground to Competitors
ML is becoming increasingly ubiquitous as companies gather, sift, sort and analyze ever-increasing amounts of data in an effort to make better business decisions. So, regardless of how you implement machine learning, don’t ignore it. Just start.
Research firm MarketsandMarkets predicts that the machine learning market will grow from $1.41 billion in 2017 to $8.81 billion by 2022. Companies that think machine learning has nothing to do with them (or hope that it goes away) won’t be able to keep up with competitors that successfully leverage the technology.
The prospect of incorporating ML into your business might seem daunting, but it doesn’t need to be if you begin with a well-thought-out plan and make sure you have someone — whether a dedicated team or a third-party partner — oversee it.
Computers aren’t taking over; they’re enabling us to better analyze and leverage data while transforming the way we do business. Thankfully, ML can help us use the massive amounts of data we have to make more-informed predictions and smarter business decisions.