Business intelligence (BI) has come a long way in a short amount of time. Just a few years ago, executives at many companies relied on static reports to inform them what happened in different areas of the business. From there, BI dashboards made information more interactive for business leaders where they could drill down on data and use data visualization tools to uncover hidden patterns in data that could lead to better decision making.
The latest iteration of BI is focused on machine learning. Machine learning is a subset of artificial intelligence that uses algorithms to iteratively learn from and adapt to data. Much like data visualization tools, machine learning enables computers to find hidden insights from data without being told where to look.
Meanwhile, one of the strengths of machine learning is that its accuracy and performance improves with experience. Machine learning computations learn from previous results to deliver improved performance in applications ranging from fraud detection to facial recognition.
A machine learning algorithm can parse through millions of pieces of data and find patterns in data that might otherwise be difficult if not impossible for humans to discover on their own. For instance, a machine learning algorithm used by an automobile insurer can predict losses from insurance claims using geospatial data from weather events.
One of the applications for machine learning that offers tremendous promise for enterprises is the use of predictive machine learning algorithms. In customer-facing applications, predictive machine learning algorithms can be used to take the next best action with customers based on everything that’s known about them.
For instance, if a high-value banking customer calls into a bank’s contact center to check the current status of his or her investments, a customer agent could be alerted to offer the customer a high-yield investment opportunity that’s expected to appeal to the customer based on his or her current holdings, along with the responses to the offer from customers with similar characteristics.
These and other machine learning capabilities offer CIOs fresh opportunities to enable the enterprise to take advantage of new business applications. As a trusted advisor, the CIO can educate the C-suite on what machine learning is and how it can be applied to the company’s business.
For instance, a healthcare CIO can point to how the collection and analysis of patient data gathered from remote medical devices can enable a healthcare provider to provide new services for monitoring patients’ vital signs remotely and use machine learning algorithms to allow physicians to identify and respond to anomalies in near real-time. Such services can provide solid ROI. Clinical trials for the medical Internet of Things have demonstrated sizable reductions in patient readmission rates and cost savings in patient care.
CIOs can also point to how the use of prescriptive machine learning algorithms can help the enterprise make predictions about future potential challenges such as macro-economic shifts or anticipated changes in customer behavior. Such insights can enable business leaders to take steps to prevent potential issues.
One of the key analytic problems that banks and companies in other industries are trying to tackle is where customers are going, including the digital and physical channels they will be using to transact and interact.
“It’s really critical that we understand where they’re (customers are) going to prefer to do business because that’s how we staff up, ramp up, and invest in the business,” said Anil Cheriyan, Corporate EVP & CIO at SunTrust Banks, Inc. in an HMG Strategy Transformational CIO video. “And that problem is really difficult to solve because you have to look at the different segments that individuals are in, the demographics, the locations, their preferences, their life experiences, their moments in their life spans and where they will want to work with us. That takes a lot of information and that’s a huge problem we’re trying to solve through the use of analytics.”
- One of the strengths of machine learning is that its accuracy and performance improves with experience.
- As a trusted advisor, the CIO can educate the C-suite on what machine learning is and how it can be applied to the company’s business.
- CIOs can also point to how the use of prescriptive machine learning algorithms can help the enterprise make predictions about future potential challenges such as macro-economic shifts or anticipated changes in customer behavior.