Predictive analytics is the process of using data to make predictions. In business analytics environments, these predictions can help business leaders and other personnel make better decisions.
Predictive analytics (i.e., “what will happen”) is considered an advanced form of analytics, with greater sophistication than traditional descriptive (i.e., “what happened”) and diagnostic (i.e., “why it happened”) analytics. Capabilities are housed in advanced analytics or business intelligence (BI) environments, enabling models to be created, tested, and deployed for humans to review.
Predictive analytics uses data mining, statistics, and machine learning (ML) to identify data patterns and predict future events. Data can come from various sources, including internal company data, public data sets, or customer surveys. Once the data is gathered, it is analyzed to find patterns and relationships. These patterns are then used to make predictions.
Before predictive analytics, and even today, data scientists rely on historical data to map potential outcomes. For example, they reference past customer behavior to forecast the possible success of new products and initiatives. Meanwhile, predictive analytics models leverage predictive modeling and algorithms to find relationships between factors that can’t be seen through descriptive or diagnostic analysis.
It has applications across industries. For example, healthcare analysts used it to study the COVID-19 virus. As the U.S. Chamber of Commerce Foundation described in 2020, “predictive models have become more sophisticated as vast troves of data secured from varied sources—including social and news media, medical research, and hospital records—expose new indicators to track the virus spread and recovery.”
Organizational leaders can apply predictive analytics within various initiatives depending on the types of data available. Broad examples of these benefits include:
With these capabilities, it can help in specific line-of-business use cases. For example:
It also can help organizations uncover otherwise hidden relationships in large data sets, enabling personnel across departments to make more informed decisions.
Increasingly, low-code and no-code analytics environments are accessible on a self-service basis by non-technical personnel. This makes predictive analytics capabilities available to a wider variety of decision-makers within organizations. These capabilities are made possible by decision intelligence (DI)—the next evolutionary stage of BI—characterized by comprehensive DI platforms designed for these purposes.
Notably, it also contributes to prescriptive analytics capabilities, which anticipate outcomes and make recommendations to decision-makers based on those predictions. In DI environments, predictive insights and prescriptive recommendations can come via natural language generation (NLG), among other methods, allowing virtually anyone to access these capabilities.
Pyramid Analytics is the world’s leader in decision intelligence. We provide a DI platform trusted by enterprise brands across the globe. The platform can deliver data-driven insights and other capabilities to various personnel in a governed, self-service way. The platform provides predictive and prescriptive analytics through individual access featuring advanced visualizations, data-sharing features, and collaboration tools—all within a single environment, eliminating the need for disparate analytics technologies.
Contact us today to learn more about how we can help your organization with predictive analytics and your broader analytics initiatives.