Make Smarter, Faster Decisions from Ever-Growing Sets of Data
Many business problems can be maddeningly complex, involving the analysis of extremely large and diverse data sets. Organizations often face questions like "Can IT build a spam-hunting application to stop junk postings from choking our company’s community web forums?"
Answering and acting on such complicated, data-intensive questions might exceed the ability of traditional business intelligence (BI) and rule-based analytics systems. These approaches may not be forward-looking or flexible enough in dynamic business environments deluged by Big Data from the cloud, social media, smart mobile devices and the Internet of Things (IoT). To better understand and solve fast-changing challenges involving enormous data troves, companies in every industry – from healthcare to banking, transportation to manufacturing, education to retail and more – are upgrading their analytics capabilities with machine learning.
A subset of artificial intelligence (AI), machine learning uses specialized software algorithms that iteratively “learn” and adapt as programs sift through massive data sets. These examples allow the organization to discover and act on patterns, insights and trends. And that produces better results over time without human intervention. These benefits are making machine learning more mainstream every day. Computers that learn drive a wide array of real-world applications: IoT data analysis, computer server monitoring, targeted advertising, image recognition, route scheduling, genetic sequencing, gaming, autonomous vehicles, energy exploration, facial recognition and many more.
Machine learning produces data-driven insights and complex, actionable decisions from extremely large data sets much more quickly and reliably than human analysis, traditional BI or other AI approaches. Machine learning drives greater efficiencies in business operations, improves security and sparks data-powered innovation with new products and services tailored to customer behaviors.