Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.
Predictive analytics is often associated with big data and data science. Companies today are swimming in data that resides across transactional databases, equipment log files, images, video, sensors or other data sources. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. These include linear and nonlinear regression, neural networks, support vector machines and decision trees. Learnings obtained through predictive analytics can then be used further within prescriptive analytics to drive actions based on predictive insights.
IBM offers a set of software tools to help you more easily and quickly build scalable predictive models. These tools can also be run on IBM Cloud Pak® for Data, a containerized data and AI platform that enables you to build and run models anywhere — on any cloud and on premises.
Read: A Business Guide to Modern Predictive Analytics (2.5 MB)
Automate data science and data engineering tasks. Train, test and deploy models seamlessly across multiple enterprise applications. Extend common data science capabilities across hybrid, multicloud environments.
Harness pre-built applications and pre-trained models. Help data science and business teams collaborate and streamline model building with state-of-the-art IBM and open source software.
Use a central platform to manage the entire data science lifecycle. Standardize development and deployment processes. Create a single framework for data governance and security across the organization.
IBM Watson® Studio helps operationalize AI by providing the tools to prepare data and build models anywhere using open source code or visual modelling.
IBM® SPSS® Statistics is designed to solve business and research problems using ad hoc analysis, hypothesis testing, geospatial analysis and predictive analytics.
The IBM SPSS Modeler solution can help you tap into data assets and modern applications, with complete algorithms and models that are ready for immediate use.
IBM Decision Optimization optimizes outcomes by offering prescriptive analytics capabilities to augment predictive insights from machine learning models.
Financial services use machine learning and quantitative tools to predict credit risk and detect fraud.
Predictive analytics in health care is used to detect and manage the care of chronically ill patients.
HR teams use predictive analytics to identify and hire employees, determine labor markets and predict an employee’s performance level.
Predictive analytics can be used for marketing campaigns throughout the customer lifecycle and in cross-sell strategies.
Retailers use predictive analytics to identify product recommendations, forecast sales, analyze markets and manage seasonal inventory.
Businesses use predictive analytics to make inventory management more efficient, helping to meet demand while minimizing stock.
