Machine learning is the process of pairing algorithms with data to create insights that help businesses make decisions and improve how applications function. It uses mathematics and training to mimic how humans learn, which makes machine learning a useful way to discover hidden patterns, make predictions and recommendations, and increase business intelligence.
How does machine learning help businesses function?
There is data all around us. We create data every day in so many of the things we do. Businesses gather that data to help them better understand customers’ wants and needs. Internal data can also show organizations areas where they can improve internally in workflows, budgets, and even the employee experience.
Data is only as useful as how quickly and effectively it can be processed to gather insights. Machine learning tackles the data sets that are too large or too complicated for humans to process and review manually.
Put simply, machine learning helps organizations solve problems and work in a faster, smarter, and better way.
Examples of machine learning.
Machine learning is a large branch of data science. If you aren’t a data expert, understanding how machine learning works and what it does can seem overwhelming. But, in reality, machine learning is all around us, and we interact with it every day. This isn’t a science fiction case of machines taking over the world. Instead, it’s a case of using science and technology to improve wellbeing.
Here are a few examples of machine learning in action:
As financial transactions have become increasingly digital, machine learning algorithms can track behavior for better fraud detection and protection. Models can identify transactions that pose risks and trigger early warning systems. If you’ve deposited a check directly from your mobile device into your account, you also have machine learning to thank. Lenders use machine learning to analyze credit scores and assess the risks of granting a loan.
Machine learning can also preserve payment integrity by optimizing resources to recover and prevent overpayments. And, it can be used for smarter cash management by helping teams ensure proper amounts of cash are allocated across areas of business and inform financial planning for future budgets.
Machine learning models can help businesses increase retention–for customers and for employees. On the customer side, machine learning can be used to identify behavior patterns that signal the risk for attrition. Then, teams can cohort and segment across a variety of specific dimensions to create targeted strategies for bringing customers back and helping them stay.
The same technique applies to employees, too. With machine learning, business leaders don’t have to blindly guess which employees will stay and which will go. Instead, they can proactively identify the indicators of employees who may be at risk for leaving. They can use those insights to model strategies for improving the employee experience and decreasing turnover.
Every time someone uses texting features like predictive text or voice to text, they are taking advantage of machine learning. Machine learning algorithms are capable of learning words and languages, and they can be trained to recognize commonly used phrases so your device can predict what words you will be using next. And, the more you text, the more personalized data you give your device, allowing it to make customized suggestions based on your own language patterns. Machine learning modeling can identify individual and group customer patterns using this natural language processing to accurately predict behavior. That means that your voice assistant can learn when you’ll be adding a “y’all” “woop” or “haha.” All of this improves the customer experience.
Healthcare is arguably the most human of human industries. Yet, machine learning is playing a more significant role in the modern healthcare system. As the majority of medical providers move to using all electronic medical records, clinicians have an opportunity to safely and securely use patient data to gather insights for better patient care and diagnostics. And, by improving manual functions with automation, doctors can offer lower treatment costs and ensure staff are able to spend their time focused on their patients instead of paperwork.
In areas like pathology, radiology, and cardiology, machine learning models and algorithms are helping detect abnormalities faster and sooner as well as detect patterns that could indicate a larger medical issue than those being tested. Machine learning can also give accurate predictions based on medical data for health risks like heart diseases, genetic cancers, diabetes, and more.
Machine learning is improving the way that educators and schools for all ages teach, learn, and research. Machine learning tools can use student data to help teachers identify students who are struggling before they are too far into the semester to improve their performance. Targeted algorithms can also identify patterns that help teachers see which intervention methods are most effective. Grading automation saves teachers valuable time so they can focus on personalizing learning for their students.
In higher education, colleges and universities can use machine learning to make enrollment predictions, support students’ individual learning paths, and make critical research simpler and more financially viable across departments. Machine learning models can even be used to help administrators modernize students’ campus experiences by making buildings and pathways safer and more efficient.
Fueling facial recognition.
Facial recognition is one of the most pervasive use cases for machine learning in our everyday lives. Machine learning makes it possible for your face to unlock your phone and for apps to make suggestions for photo tagging and organization. It can also be used by law enforcement to find criminals and identify victims.
Making product recommendations.
Machine learning makes it possible for your favorite streamer to suggest entertainment you might like based on what you have enjoyed in the past. By grouping individuals based on purchase history and similar demographics, machine learning can also draw connections between people with similar interests and use that information to fuel recommendations.
How will machine learning be used in the future?
The list of machine learning examples is going to grow and grow. Machine learning is being improved and used for new things every day. Here are a few notable predictions:
In healthcare, the rising popularity of wearable devices that track health and activity data will be integrated with doctor’s data to alert practitioners of potential issues, help support positive preventative health measures, and even make it easier for healthcare workers to prepare for and respond to emergencies.
Tools like Domo’s automated machine learning (AutoML) will help make machine learning models accessible for every member of an organization, making benefits like faster data-driven decision making available to every department.
Machine learning will help manufacturers cut costs, better manage supply chains, and improve quality control.
Automotive companies will harness machine learning for use in self-driving cars. The right algorithms will make functions like navigation and obstacle and pedestrian detection possible.
Check out some related resources:
9 Ways Modern BI for All Differs from Traditional BI
A Quick Introduction to Modern BI for All
How Arthrex Improved Planning & Forecasting Using Domo’s Data Science Suite
Try Domo for yourself. Completely free.
Domo transforms the way these companies manage business.