Predictive analytics can sound like a complicated topic. It's not. It's simply a way to predict the future by looking at historical information and other relevant data to find patterns.
Of course, deriving actionable insights from data using traditional data modeling methods can take a long time – days, weeks – even months. That’s where machine learning comes in. Today, predictive analytics generally refers to using historical data, machine learning, and artificial intelligence to predict future events.
Machine learning makes predictive analytics possible; more accurately, it makes sophisticated predictive analytics possible. Think of machine learning as a modern iteration of predictive analytics. Machine learning applications automatically learn and improve from experience. They can evaluate data in real time and change their behavior accordingly.
Gartner calls machine learning one of the hottest concepts in technology. “It will soon become impossible for conventional engineering solutions to handle the increasing amounts of available data. Machine learning offers the ability to extract certain knowledge and patterns from a series of observations.”
Machine-learning business applications abound, across industries.
It's All Around
We see machine learning applications every day: Think about recommendations from Amazon, Pandora, Spotify, Netflix or...you get the idea. And of course, there are those choices Facebook makes for your newsfeed.
Spam filtering offers a good example of how machine learning changes things. In the past, a spam filter simply looked for keywords, domains, email addresses, patterns, etc. As new spammers came online, programmers would make the necessary adjustments.
A machine learning spam filter starts at the same place and learns over time based on what gets flagged as spam – or for that matter, items that end up in the spam folder that get tagged as “not spam.” It also can adapt to the preferences and habits of individual users.
Here are some other machine learning business applications already in place – or on the horizon.
Digital Twin: IoT meets Machine Learning
Machine learning applications in industry abound; it’s used in manufacturing, shipping, and dozens of other areas.
One of the most interesting uses ties into the Internet of Things: The digital twin.
As IBM describes the digital twin, it is, “a virtual doppelganger of...a complex ecosystem of connected things, such as an autonomous car in the middle of rush-hour traffic in Los Angeles. It’s not just a 3D model – it’s a living model in 3D that sees the car as part of a complex technology ecosystem of electronics, navigation, communication, entertainment, collision avoidance, climate control, and so on.”
Put more simply, a digital twin refers to the digital model of something – machine or maybe one day, a human.
By 2021, half of large industrial companies will use digital twins, Gartner predicts. GE, for instance, has created digital twins for physical assets, from car engines to power turbines – digital representations of industrial equipment and manufacturing processes.
Keeping with the auto theme, let's look at Tesla. Data Science Center calls the company “the poster child for using real-time IoT data directly from customers’ cars and their driving experiences to enhance the performance of not only its existing fleet but also future models. Reportedly this can be as discrete as resolving a customer’s rattling door by updating onboard software to adjust hydraulic pressure in that specific door.”
Healthcare and Drug Discovery
Healthcare, from the lab bench to the bedside, stands to benefit from machine learning and predictive analytics.
Back in 2016, the Royal Society noted that “in early-stage drug discovery machine learning can sift through vast amounts of data to detect patterns that elucidate the complex biological systems at work. For example, machine learning could be used in the initial screening of drug compounds to predict success in compound activity and interaction.”
Overall, machine learning could save pharma and medicine around $100 billion annually, according to the consultants over at McKinsey.
Could is the operative word. Keep in mind that healthcare is often the last one on the digital train, so this may be best described as one of the future applications of machine learning.
McKinsey notes that “many pharmaceutical companies are wary about investing significantly in improving big-data analytical capabilities, partly because there are few examples of peers creating a lot of value from it. However, we believe investment and value creation will grow. The road ahead is indeed challenging, but the big-data opportunity in pharmaceutical R&D is real, and the rewards will be great for companies that succeed.”
Still, we’d be remiss if we didn’t point out that on the clinical side, predictive analysis enables doctors and clinicians to focus on providing better service and patient care by helping them address patient needs before they are sick.
Overall, the global healthcare predictive analytics market is expected to grow about 10 percent in 2018-2023, according to Orbis Research.
Moreover, 85 percent of healthcare payers and providers are currently using predictive analytics or plan to do so in the next five years, according to a survey by the Society of Actuaries. “Most executives are very interested in using predictive analytics tools to increase cost-saving, improve patient satisfaction, and satisfy their staffing and workforce needs,” Lillian Dittrick, fellow of the Society of Actuaries, told HealthITAnalytics.com.
Improving Data Quality
Machine learning and predictive analytics require good-quality data, and data quality is a growing concern – especially in the era of the IoT. Expect to see more organizations turn to machine-learning algorithms to improve their data, reports CIO.
“As more data is generated through technologies like IoT, it becomes increasingly difficult to manage and leverage,” Emily Washington, senior vice president of product management at data and analytics software provider Infogix, tells CIO. “Integrated self-service tools deliver an all-inclusive view of a business’s data landscape to draw meaningful, timely conclusions.”
The real-world applications of machine learning in retail are too numerous to list. Healthcare may lag, but retail is blazing trails.
Retailers are equipped to capture, analyze, and leverage data to customize the shopping experience in real time. Machine learning algorithms use similarities and differences in customer data to improve segmentation and targeting. Think about Amazon, with its dynamic pricing, personalized product recommendations, and real-time incentives.
“Retailers are looking to incorporate non-traditional demand signals to get a better picture of demand – seeing if there are connections to be made about consumer behavior related to products that can be exploited in the future, using new kinds of data. For example, predicting that a restaurant will sell 25% more salads if the lunch-time temperature is above 80 degrees F. Or, conversely, that lettuce contamination in the headlines creates a 10% decline in salad sales.”
Banks and financial institutions use machine learning to gather real-time insights that help drive investment and other strategies. But perhaps the most obvious – and important – use is to prevent fraud. That’s why you may get that phone call asking about those skis you supposedly bought in Aspen – although you live in Miami.
American Express relies heavily on machine learning algorithms to help detect fraud, and it does so in (almost) real time, according to consultant Bernard Marr. At the same time – like retailers – it uses the same technology to generate insights that can connect its customers (cardholders) with products or services and special offers.
PayPal uses machine learning to fight money laundering, reports Compliance Alert. It’s able to compare billions of transactions and differentiate between legitimate and fraudulent transactions. This not only lets it spot suspicious activity – it has dramatically cut down on false alarms.
As in financial services, one could find many uses of machine learning and predictive analytics in insurance. One interesting one is workers’ comp.
The best performing workers’ comp organizations “use predictive modeling eight times more frequently than firms with less success in closing claims,” reports Business Insurance. And the practice is growing.
“I think we are at a tipping point, at least in terms of using predictive analytics to improve claims management and claims practices,” David Huth, COO for Rising Medical Solutions, told Business Insurance. “I think it’s the future of the industry for a variety of reasons…we are in a data-rich industry, but we haven’t leveraged that data historically to make better and smarter decisions.”
What’s in Your Future?
The future is all about the data. Having it and putting it to work. That means companies that successfully use predictive analytics and machine learning to inform business decisions have a competitive edge. If you want that edge, we can help. Get in touch.