What if an insurance company had a tool that improved profitability by predicting insurance rates and premiums with greater precision and efficiency? Or if a natural gas utility company improved safety, efficiency, and customer satisfaction with a tool to predict and better respond to leaks or unaccounted-for gas? These are both examples of machine learning techniques that are gaining momentum due to the pervasiveness of data (from the Internet of Things [IoT], social media and mobile devices) and the seemingly infinite scalability of cloud-based compute power.
At the same time, the understanding and sophistication of algorithms have expanded, and the ability to apply complex mathematical calculations to data, and rapidly process them, is also driving growing interest in exploiting machine learning to gain competitive advantages in business.
Machine learning is a type of data analysis technology that extracts knowledge without being explicitly programmed to do so. Data from a wide variety of potential sources (such as applications, sensors, networks, devices and appliances) is fed to a machine learning system, which uses that data and applies algorithms to build its own logic to solve a problem, derive insight or make a prediction.
“The capability to transform data into actionable insight is the key to a competitive advantage for any organization,” said Carlton Sapp. research director at Gartner. “However, the ability to autonomously learn and evolve as new data is introduced — without explicitly programming to do so — is the holy grail of business intelligence.”
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.”
Gartner clients can learn more in “Preparing and Architecting for Machine Learning.”
Gartner Catalyst Conference 2017
Additional information will be presented at Gartner Catalyst Conference 2017 taking place August 21-24 in San Diego, CA and September 18-19 in London. Follow news and updates from the event on Twitter using #GartnerCAT.