Machine learning is helping, with software engineers continually creating and improving algorithms that automatically analyze data to identify patterns and predict outcomes.
Machine learning involves studying computer algorithms that provide computer programs with the ability to learn, discover, predict and improve automatically using large amounts of data without explicit programming, said Dave Schubmehl, research director for Cognitive Systems and Content Analytics at IDC.
“Machine learning starts with data—the more you have, the better your results are likely to be,” said David Chappell, in a white paper on Microsoft’s Azure Machine Learning solution. “Because we live in the big data era, machine learning has become much more popular in the last few years. Having lots of data to work with in many different areas lets the techniques of machine learning be applied to a broader set of problems. Since the process starts with data, choosing the right data to work with is critically important.”
Herain Oberoi, director of product management at Microsoft, described an existing use case where an organization is studying the problem of customer churn by doing an in-depth analysis based on Web logs and click streams.
“They store their Web log information in their big data system, do some processing on it, run a model against it and then predict from a customer's browsing and clickstream history whether they're likely to churn or not,” he said. “That's an example of an existing use case that machine learning makes better.”
What's happening, Oberoi said, is "a convergence between the cloud, big data analytics and machine learning to enable a new set of use cases and [to] simplify an existing set of use cases.”
Machine Learning Is Not New
Although machine learning libraries have been around for decades and have been offered as part of many statistical packages, including IBM’s SPSS, SAS and many others, the use of machine learning by enterprises has not been widespread until recently. That’s because the analytical algorithms require a lot of data and a lot of compute power, Schubmehl said.
However, many leading technology firms, such as Google, Facebook, Amazon, Baidu, Yahoo, Walmart Labs and others have been using machine learning tools over the last few years to improve analytics applications in areas, such as image recognition, programmatic advertising, as well as product and content recommendations.
“Enterprises haven’t been as quick to adopt machine learning and are now doing so as part of their big data efforts,” Schubmehl said. “Probably the biggest use of machine learning to date is for data categorization, discovery and cleansing.”
Machine learning in general is one—albeit highly important and necessary—component in a new generation of "smart" applications that have cognitive capabilities—applications that are able to recognize patterns in data, documents or even images.
Other technologies, in addition to various types of machine learning, include content acquisition/aggregation, text analytics, speech analytics, knowledge graphs and question and answer systems.
Companies like IBM, Cognitive Scale and TCS are including machine learning capabilities in their cognitive systems platforms. They allow developers and enterprises to build applications capable of learning and improving their performance over time.
According to Mike Gualtieri, a principal analyst at Forrester Research, slightly fewer than half of enterprises report using predictive analytics. However, verticals such as retail, travel, financial services, law enforcement and others have been quick to try their hands at machine learning and predictive analytics to solve problems, reach bigger audiences and catch criminals.