For years, companies have been striving to develop analytics capabilities, not just to understand past performance, but to anticipate trends and future events to improve agility. Increasingly, companies are deploying predictive analytics to make their services more efficient, develop products, find potential threats, optimize maintenance, and even save lives.
In March, research firm Facts & Factors said the global predictive analytics market was estimated at $5.7 billion in 2019 and will reach $22.1 billion by 2026, a compound annual growth rate (CAGR) of 24.5%.
Here are four examples of how organizations are using predictive analytics today.
Rolls-Royce optimizes maintenance schedules
Rolls-Royce, one of the world’s largest makers of aircraft engines, is deploying predictive analytics to help dramatically reduce the amount of carbon its engines produce while also optimizing maintenance to help customers keep their planes in the air longer.
The company’s Intelligent Engine platform monitors how each of its engines fly, the conditions in which they’re flying, and how pilots use them. Rolls-Royce applies machine learning to that data to customize maintenance regimes for individual engines.
“We’re tailoring our maintenance regimes to make sure that we’re optimizing for the life an engine has, not the life the manual says it should have,” says Stuart Hughes, chief information and digital officer at Rolls-Royce. “It’s truly variable service looking at each engine as an individual engine.”
Hughes’ advice: Focus on helping your customer. Analytics are helping Rolls-Royce optimize the maintenance services it offers, but the ultimate benefit is that customers are seeing less service interruption because the company can better predict when maintenance will be required and help them schedule it.
“Rolls-Royce has been monitoring engines and charging per hour for at least 20 years,” Hughes says. “That part of the business isn’t new. But as we’ve evolved, we’ve begun to treat the engine as a singular engine. It’s much more about the personalization of that engine.”
DC Water proactively hunts sewer main breaks
“It uses an advanced, deep learning neural network model to do image analysis of small diameter sewer pipes, classify them, and then create a condition assessment report,” says Thomas Kuczynski, CIO and vice president of IT at DC Water.
Before deploying Pipe Sleuth, operators had to review the CCTV footage manually and tag defects they saw. The tagged footage was then provided to certified engineers for classification. The process was time-consuming and inefficient.
Kuczynski’s advice: Focus on revenue and efficiency. Pipe Sleuth is just one piece of a broader effort to leverage predictive analytics and real-time analytics at DC Water. They are all part of an effort to drive down water loss by about 2% to 5%. Every 1% of “found water” that was previously unmetered is worth about $4 million to DC Water.
“You want to look at those problems that are persistent challenges for your organization and ideally have a revenue component or efficiency component associated with them,” Kuczynski says. “It’s always easier to sell something that saves you something, whether that’s real dollars or something that improves a process significantly.”
Ellie Mae hunts for ransomware threats
Mortgage technology company Ellie Mae has taken a proactive stance on ransomware by developing Autonomous Threat Hunting. Autonomous Threat Hunting combines threat intelligence, predictive analytics, AI, and previously identified indicators of compromise (IOC) to identify new compromise indicators and new evasion techniques before they can be used.
“The nature of threat hunting is very proactive,” says Selim Aissi, senior vice president and chief security officer at Ellie Mae. “You don’t wait until an attack has happened. You explore, prioritize, and investigate threats before an attack happens or even before a malware is known.”
Aissi says the project has increased security operational efficiency by roughly 35% and has led to about 10x improvement in early identification of threats. It has also increased the speed of resolution of new threats by about 60%.
Aissi’s advice: Make change management part of your planning process from the beginning.
“From a change management perspective, a lot of the impact was really on my security operations and engineering teams,” Aissi says. “A lot of these capabilities have traditionally been manual, and security analysts had to go collect the threat information and manually input that information into tools. We had to adjust to this and train the security analysts and engineers to this new, autonomous way of doing things.”
Kaiser Permanente reduces patient mortality
Managed care consortium Kaiser Permanente has created a hospital workflow tool that leverages predictive analytics to identify non-intensive care unit (ICU) patients that are at risk of rapid deterioration.
Non-ICU patients that require unexpected transfers to the ICU make up only 2% to 4% of the total hospital population, but account for 20% of all hospital deaths, according to Dr. Gabriel Escobar, research scientist, Division of Research, and regional director, Hospital Operations Research, Kaiser Permanente Northern California.
Kaiser Permanente developed the Advanced Alert Monitor (AAM) system, which leverages three predictive analytic models to analyze more than 70 factors in a given patient’s electronic health record to generate a composite risk score.
“The AAM system synthesizes and analyzes vital statistics, lab results, and other variables to generate hourly deterioration risk scores for adult hospital patients in the medical-surgical and transitional care units,” says Dick Daniels, executive vice president and CIO of Kaiser Permanente. “Remote hospital teams evaluate the risk scores every hour and notify rapid response teams in the hospital when potential deterioration is detected. The rapid response team conducts bedside evaluation of the patient and calibrates the course treatment with the hospitalist.”
Daniels’ advice: Focus on process. Predictive analytics tools are only as good as the processes that ensure the information will be used. Beyond the time spent developing the tool, the AAM team spent a significant amount of time developing and implementing workflows that would allow health care teams to respond to alerts as efficiently as possible.
“It took us about five years to perform the initial mapping of the electronic medical record back end and develop the predictive models,” Daniels says. “It then took us another two to three years to transition these models into a live web services application that could be use operationally.”