Business Uses for Machine Learning
Business use of machine learning is growing due to the increasing pervasiveness of the technology and the rising discovery of business benefits that can be derived from its use. The data-rich nature that underpins a digital business, along with other big data sources and trends, has also been a major driver. Information is being collected and generated from more sources than ever before, including sensors at the edge of IoT systems, social media, mobile devices, the web and traditional business data stores.
Many organizations just don't have the resources to derive all the business value they could from this mountain of information. Because machine learning can analyze data and derive predictions and inferences on its own, without the need for significant programming, it is opening up new opportunities to exploit the latent value in business data and gain a competitive edge.
“Machine learning is particularly well-suited to gaining a competitive edge in digital business because it offers the benefits of speed, power, efficiency and intelligence through learning without having to explicitly program these characteristics into an application,” said Mr. Sapp. “In other words, machine learning enables us to teach a program how we make decisions instead of programming those decisions.”
Machine Learning Opportunities Abound
This offers many opportunities for developers and data science teams to enhance product offerings, customer relationships, marketing and advertising, process improvement, and much more. For example, an energy company can use machine learning to optimize the management and productivity of its work fleets, enabling the company to predict where its workers are likely to be most needed.
A hedge fund can reap significant benefits by using machine learning to price financial portfolios in overly aggressive markets. By discovering latent features from the data in its portfolios, the firm can adjust pricing to maximize profit and boost revenue.
“Machine learning is the next generation of analytics for digital business architects. These architects should be building for machine learning by understanding the business opportunity and understanding how to acquire data, process, model and deploy machine learning capabilities,” said Mr. Sapp. “IT organizations that are proactive about planning and preparing the IT environment for machine learning now will be better positioned to deliver on its benefits in the future.”