Data is the foundation of success, from fueling scientific research and creating new medical treatments, to delivering a personalized shopping experience and optimizing business operations. Today’s organizations are utilizing cutting-edge technologies to harness the full power of their data. However, legacy IT lacks the management and analytics capabilities required to handle growing datasets.
High performance computing (HPC) is key to extracting real-time insights, and enabling IT departments to achieve new levels of performance. Among these advancements, machine learning is a powerful tool that allows organizations to ingest continuous streams of information and glean actionable intelligence. Machine learning is a method of data analysis in which HPC applications derive predictive insights—or learn—from data. Advanced algorithms are trained to recognize patterns and identify pertinent bytes of information until they can begin to independently learn, identify, and predict. This era of digital innovation is delivering machines that can not only augment human processes, but command them.
As computers become increasingly capable of learning freely, reasoning, and determining the best course of action in real time, businesses stand to gain valuable competitive advantage. Machine learning, a proliferating component of artificial intelligence (AI), has advanced rapidly in recent years and become even more accessible for businesses to adopt.
Powered by significant new innovations in infrastructure and applications, these technologies are driving massive improvements to business intelligence and productivity.
Data from customer profiles, such as browsing activity, recent purchases, or personal details, can be used to predict the uptake of a new product or service, and forecast which products a specific customer is most likely to buy. According to a 2016 survey, refining sales and marketing efforts is one of the most widely used applications of machine learning, with at least 40% of companies indicating that they are already using machine learning to improve sales and marketing performance.
Machine learning algorithms can also help enhance the customer experience. For example, contact centers leverage machine learning techniques to route incoming calls to the right representative more quickly, helping to reduce call durations and increase the incidence of first-call resolutions. Companies like Netflix and Amazon are experts at using predictive algorithms to deliver content or recommend products that are personalized for their customers, which drives customer satisfaction and loyalty.
In high-risk environments, such as power plants or offshore drilling rigs, using every tool to ensure employee safety is paramount. IT personnel utilize predictive analytics to monitor equipment health in real time and foreshadow malfunctions or failures that might put employees at risk. Machine learning algorithms can also be trained to analyze historical data in order to pinpoint factors that may have led to dangerous events or identify employee segments that are at the highest risk.
Perhaps the greatest benefit of machine learning is the ability to optimize business performance. Machine learning models incorporate data from all aspects of the business, providing IT leaders a comprehensive view of processes and workflows, and the opportunity to automate. Sometimes referred to as intelligent automation, machine learning systems work to synthesize both historical datasets and streaming data in order to enhance operations across the board, from quality assurance to compliance.
As machine learning applications become more accessible at the enterprise level, organizations across all industries will be positioned to achieve new levels of productivity and efficiency. We’ve only just begun to understand the true potential of machine learning in the enterprise. Be sure to follow me on Twitter @VineethRam to stay up-to-date on all the latest innovations.