The Age of Decisioning: How to Apply Machine Learning in Your Business
Machine learning has great potential to solve business problems, create efficiencies and transform industries. Learn how machine learning works, how it’s evolving and how it’s empowering and improving every business.
Machine learning is a transformative technology that has the potential to impact business from every angle—by improving efficiency, driving revenue and delivering a better customer experience. When you hear about these technologies, you might think of digital assistants or self-driving cars, but the capabilities go far beyond that. For example, nearly every website you interact with uses machine learning, whether to make a product recommendation, offer a personalized user experience, or enable communication with a customer service or help desk agent. Additionally, smart assistants such as Siri and Alexa, which are now ingrained in our everyday lives, are driven by machine learning.
So, how does machine learning work? At the most basic level, machine learning is just math. Machine learning uses algorithms to parse data, learn from it and make a recommendation or prediction about something in the world. Instead of manually programming a computer to do a specific task, machine learning teaches computers to make decisions by learning from large data sets. The term machine learning is often used interchangeably with artificial intelligence, but they are different. Most artificial intelligence applications today use machine learning to mimic human judgment and perform complex tasks, like deep thinking, problem solving and image or sound recognition.
Artificial intelligence is the simulation of human judgement by machines; it uses data to learn behavior or alter future behavior.
Machine learning enables systems to learn and predict outcomes based on data and information, and improve learning over time.
Neural networks are computer systems that are trained using data sets to model the way the human brain processes information.
In reinforcement learning, a model isn’t trained—it learns ideal behavior based on feedback from the environment.
In supervised learning, a model is trained with a large set of historical data, and algorithms are created to predict future outcome.
In unsupervised learning, a model is trained using unclassified data. These models can perform more complex processing tasks and identify patterns in data.
Machine Learning and Artificial Intelligence: Why Now?
Machine learning and artificial intelligence are not new, but businesses are now seeing tremendous possibility to leverage these technologies to improve efficiency and offer customers a more personalized experience. These techniques are being applied in every field, led by industries such as retail, automotive and financial services. Machine learning is expected to grow exponentially in the near future, potentially generating tens of billions of dollars.
Recently, there has been a significant increase in the use of these technologies because of the availability of tremendous computing power. Advances in graphic processing units (GPUs) now allow computers to perform trillions of calculations a second. With the speed of today’s computers, machine learning algorithms are being used to improve legacy systems, create operational efficiencies and make innovations like autonomous vehicles a reality. And, as fraud prevention becomes more of a focus in business operations, machine learning techniques will help businesses identify anomalies and prevent fraudulent activity before it occurs.
While all of this may sound expensive and difficult to implement, the barriers to using these technologies have lowered significantly. Public cloud providers like Google, Microsoft and Amazon are readily available and allow anyone to adopt these capabilities as a service. Machine learning is built into their products, and they are increasingly delivered in a self-service fashion.
Applying Machine Learning to Business Problems
Machine learning has significant capabilities that can help solve business problems. Its potential is huge and it’s only growing as more data becomes available and more sophisticated open-source models are developed. Some of the most visible use cases include:
Machine learning algorithms and natural language processing make it possible for customers to interact with chatbots, help desk agents and digital assistants to get personalized and on-demand customer assistance.
Customers increasingly expect tailored recommendations. Machine learning can make inferences to help customers find products faster, see smarter search results and get content and product recommendations based on their interests and purchase history.
Fraudsters are constantly changing and improving their tactics. Machine learning models can adapt and learn over time to detect fraud patterns in real time. It can also identify with great accuracy anomalies in a customer’s payment patterns and, more importantly, distinguish between false positives and true fraud.
Machine learning can be used to improve processes—whether it’s an approval process or a workflow. It can be used anywhere you see friction in your organization by revealing what workflows can be introduced or improved to create efficiencies or reduce friction points.
In today’s world of mass customization, serving customers how they want to be served is essential. Predictive modeling allows businesses to tailor a customer’s experience with extreme certainty. While some customers may want a more personal experience where they can connect one-on-one with a service professional, others may prefer a self-service channel that’s available 24/7. Machine learning lets you apply data to learn your customers’ habits and preferences.
Best Practices: Training and Calibrating Models
In order to successfully apply machine learning to your business, you have to start with rich, quality data. Make sure you understand your data—both how to store it and how to use it appropriately. Historical data might not be useful in making correct predictions if you are in a fast-changing or growing industry. In this case, it’s important to have quality data that you can apply to draw accurate inferences. When testing a new algorithm, it’s important to train it on the widest set of data, and you will also want to continually recalibrate your models with new, real-time data. It takes time to develop useful models—so make sure to focus your efforts on assessing your data quality and building on it to glean predictive value from it.
Companies that are successful are focused on ensuring they offer a great user experience and knowing what their customers want even before they want it. Machine learning is integral in delivering on that objective. Many companies, including JPMorgan Chase, often talk about customer obsession and taking friction out of the customer experience. The best way to accomplish this is by understanding your customer as much as possible—and leveraging data to make decisions about how to better serve them. Machine learning helps make all of this possible.
What the Future Holds
In a historical sense, we have seen transformative technologies impact how we live, work and consume information. The Industrial Age brought new manufacturing processes, and the Information Age introduced us to information technology on a large scale. Today, we’re seeing the impact of the Decisioning Age, where many choices are being made for us in our everyday lives—and there are simply fewer decisions to be made.
The accessibility of machine learning technologies is going to permeate every part of our lives, and we will see it in how products and services are delivered to us every day. In the past, we were using capabilities like business intelligence to explain what happened. Eventually, we started using statistical modeling to answer why something happened. Now, we’re applying machine learning to predict what will happen. Machine learning has great potential to create efficiencies, improve workflows and automate certain functions so people can focus on higher-order tasks. Business has always evolved, and these innovations will continue to help us solve problems and create value from data in ways that weren’t possible before.
- Identify potential use cases or applications for machine learning in your business. Ask yourself, can the business problem you’re trying to solve be addressed with machine learning? Not all problems will be a good use case for machine learning, but the technology can be used to help you discover critical business information and apply it to your business.
- Determine whether to apply machine learning through one of your technology partners or to build it yourself. Getting started doesn’t have to be a huge investment—you can make use of the many open-source models available to drive company value.
- Make sure you have new and clean data to work with. If the data available to you is outdated or unstructured, you may not be able to glean useful information from it. In industries that are changing quickly, new data is essential.
- Know that you will have to continuously iterate, test and retrain your models to achieve a useful result. It will take work to develop functional models and put the right processes in place, but the advanced predictive capabilities that can be applied to your business—and, in turn, the ROI—will make it worthwhile.