Using predictive analytics enables enterprises to see into the future. By leveraging the information contained within massive data sets and advanced machine learning algorithms, businesses are able to gain deep insights and predict the outcomes for future customers, patients, and key initiatives.
Nearly ubiquitous across industries, due in part to its demonstrable return on investment (ROI), the use of analytics and machine learning has paved the way for a variety of transformative changes in a wide range of industries. Here are just a few examples.
In the healthcare industry, predictive technology has proven to be a huge success across a staggering range of applications. From a patient perspective, the ability to look at medical records and leverage the power of real-time patient monitoring has led to a much finer-grain understanding of the potential indicators and patterns that drive more precise, personalized, patient-centered outcomes. Connected devices—from blood-sugar monitors to heart monitors—create a massive increase in both the volume of data and the granularity at which it is collected. Patient data has evolved from self-reported questionnaires to rich troves of sub-second monitoring data.
According to Forbes, “Researchers are beginning to compile this information into incredibly useful databases that could be game changers in understanding the intersection of lifestyle and disease.” Predictive and preventative analytics are the cornerstone capabilities that turns large volumes of data into actionable, potentially life-saving insights in the healthcare space.
For health insurers, this same source of data can be applied to data mining and machine learning capabilities that can recognize the needle in the haystack of healthcare fraud. By combining big data storage solutions with cutting-edge algorithms, artificial intelligence systems are helping bend the cost of healthcare while improving the quality.
Analytics is transforming both the process of manufacturing goods and the continued maintenance of products in the field. Connected manufacturing processes are now capable of ingesting massive amounts of real-time data and executing sub-second decisions to determine whether a defect is present in one of often hundreds of components within an assembly.
From temperature fluctuations to subtle mechanical vibrations and other seemingly minute details, this information is collated in big data warehouses that are capable of monitoring and understanding end-to-end manufacturing processes. These systems, powered by predictive analytics, not only prevent manufacturing errors and quality issues, but improve the entire downstream customer experience by minimizing the need for customer service requests, product returns and repairs, warranty claims, and high-publicity recalls.
In addition, once off the manufacturing line, modern connected products can provide a continuous stream of rich data from the field. “Smart Service” enabled devices provide the basis for both revenue- and margin-enhancing services for manufacturers, and the ability to provide far more personalized services to consumers.
Financial services firms have historically been data-driven institutions, and big data, combined with predictive tools, has enabled them to move beyond Excel to more advanced predictive modeling capabilities that can scale to billions of data records. From supply chain optimization to updating financial forecasts in real time, financial analysts can use predictive modeling to generate far more accurate forecasts than previously possible.
By combining the power of cutting-edge machine learning and artificial algorithms with massive data sets, a variety of industries are making significant advances across a wide range of domains. Is your company able to read the future?
Learn more about how predictive analytics can help your organization shift from reactive to proactive decision-making.