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    Predictive analytics is a proactive approach to harnessing enterprise data, detecting patterns, and helping businesses prepare for events that are possible or likely. Enterprises use dedicated software, including business intelligence and advanced analytics platforms, to visualize predictions.

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    What is predictive analytics?

    Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell the user what will happen in the future. Instead, it forecasts what might happen in the future based on all factors that the analytics solution has taken into consideration. It includes what-if scenarios and risk assessment. 

    A predictive analytics platform uses different algorithms to sort data, both structured and unstructured, into groups or categories that help answer questions or provide details. Predictive analytics software often uses automation and machine learning technologies to study petabytes of data from multiple storage environments and locate patterns that otherwise wouldn’t be apparent. 

    Predictive models and analysis applied to business

    Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. Predictive models use a number of techniques, including data mining, statistical modeling and machine learning, to help analysts make future business forecasts.

    Charts and graphs help businesses clearly visualize where growth or problems are occurring. 

    SAS Visual analytics — interface.

    Image credit: SAS

    Predictive analytics solutions improve upon manual, or entirely human-based, decision-making models. There are multiple methods of making enterprise decisions, according to Atlassian, and though some are based on rational patterns, evaluation, and answering multiple questions, others are based more on intuition or instinct. Predictive analytics adds data and concrete detail to any decision, so that enterprises can make choices backed specifically by information that their systems have discovered. 

    Predictive analytics models

    Predictive analytics tools use modeling to classify and organize data. Models are useful depending on their purpose and application. They’re specifically designed to sort and analyze data in a particular way.

    Classification models determine the answers to straightforward prompts and questions, grouping data depending on those answers. They’re popular and flexible because they can determine a simple answer and move on.

    Outlier models look for strange things rather than standard trends and patterns. Their purpose is to track anomalies. Outlier models are helpful for threat detection.

    Time series models sort data recorded at different times over a certain period. They are helpful for observing trends that occur in a chosen time frame.

    Segmentation models place customers together based on their preferences, activities, demographics, or any other predetermined variable.

    Decision tree models separate data into categories using different branches. They’re based on a certain variable and try to gain more information by applying a filter or searching for a criterion. As its name suggests, using this model to separate data makes decisions easier.

    How is predictive analytics used? 

    Predictive analytics is used to optimize products, processes, and technology through insights taken directly from enterprise data. Better business predictions and decisions can mean saving money, utilizing resources wisely, and planning more successful campaigns. For example, if a company uses advanced data analytics to observe that a very large demographic has been more likely to purchase a product around Christmastime for the past ten years, then the company can:

    1. Make more specific marketing decisions by targeting that demographic with the product
    2. Use their employees’ time more efficiently because they know a specific area of focus and exactly how they plan to market the product
    3. Know where to place their efforts instead of floundering through holiday sales, not quite knowing how to advertise

    This is an extremely simplistic example; predictive analytics are often used to forecast long-term situations and can be a way of detecting anomalies and threats within a system, such as potential fraud. Again, no predictive analytics platform should be used to assume that a situation like the one above is guaranteed success. But this type of data analysis makes businesses more likely to notice trends that they can use to improve their products, time and resource management, and customer experience.

    Full-cycle predictive analytics solutions allow businesses to examine results after the insights from analytics have already been applied to see whether they made improvements or not. Then the company can reassess. To continue the example above: 

    1. Analytics software reveals that customers were purchasing high numbers of one product during the month of December.
    2. The company develops an advertising campaign centered around that one product and implements it during the next holiday season. 
    3. Afterwards, the company uses the predictive analytics tool to assess how successful that campaign was by comparing revenue earned with the past year’s revenue and monitoring customer behavior.  
    4. Depending on which factors were successful and which weren’t, the company can tailor its next campaign more specifically, or can drop the campaign altogether if other market factors render it no longer profitable. 

    Other examples of predictive analytics use cases include:

    • Meeting demand more quickly. If a company that sells rack servers notices that its predictive analytics software has tracked an unusually high number of rack server purchases, it can then place an order for more hardware to build the servers before the parts are out of stock. 
    • Avoiding procurement fraud. By leveraging predictive analytics to monitor financial data like invoices, businesses can more quickly pick up discrepancies that suggest collusion between an employee and a potential supplier. 
    • Garnering insights from sensor data. For example, gathering information from IoT sensors on factory machinery allows businesses to predict when an assembly line machine will need to be fixed or replaced. 

    Predictive analytics and big data

    Predictive analytics is often applied to big data. Businesses are inundated with data — customer data, sales data, manufacturing data, computer system and server data, log file data — and managing such large data sets is a tall order. Predictive analytics helps companies manage their business data because its algorithms can organize and make sense of that information and show them ways to capitalize on it.

    Data mining

    Data mining is the advanced analysis of large sets of data, often unstructured, that is set to pick out specific trends for better future estimates. In the earlier example, data mining techniques might search for sales that happen the week before Christmas. (Again, that’s simplified; data mining is often highly advanced.) The purpose of data mining is to reveal specific patterns so that businesses can predict reasonable, probable future events and plan accordingly. Data mining focuses on actionable details.

    Machine learning

    Machine learning also plays an important role in modern predictive analytics. Intelligent technology not only observes patterns in data but also eventually makes decisions based on trends in the data. It automates data analysis that people would have to wade through otherwise. At times, machine learning can make suggestions or notice trends that human administrators or data experts wouldn’t recognize. Many popular analytics tools implement machine learning; it’s becoming a staple for big data.


    Predictive analytics, when used effectively, can improve a company’s customer relationship management. Analytics platforms detect trends in customer behavior and purchases, using that data to predict what a customer wants or needs. For large businesses, big data analytics is a large part of CRM. Predictions, a subset of big data analytics, allow businesses to more strategically and successfully interact with customers based on discoveries in data that could be overwhelming without an analytics platform.

    Also read: Best Big Data Tools & Software for Analysis  

    Predictive analytics software vendors for the enterprise

    Predictive analytics and data mining solutions for the enterprise are currently available from a number of companies: 

    • SAS Predictive Analytics is a suite that covers all stages of the predictive modeling process, like data preparation, visualization, and model deployment.
    • IBM SPSS Statistics users can integrate the platform with open source technology to create their own extensions. IBM offers multiple licensing options.
    • Sisense Fusion is an analytics platform that uses artificial intelligence and allows no-code, low-code, and code-first approaches to data visualization for users with varying amounts of programming experience. 
    • Oracle Crystal Ball is spreadsheet-based software for modeling, forecasting, and simulating factors that affect enterprise risks.
    • Minitab is an education-focused suite of six statistics, analytics, and workplace applications that schools and universities can use to govern and automate business processes.  
    • TIBCO Analytics offers enterprises an analytics solution for hybrid cloud deployments, covering both on-premises data and multiple cloud environments. 
    • DataRobot AI Cloud is designed for multiple teams, including executives, IT, and DevOps, and uses artificial intelligence to improve data operations. 

    Vendors may offer proprietary solutions or solutions based on open source technologies. Predictive analytics software can be deployed on-premises for enterprise users or in the cloud for small businesses or for project or team-based initiatives.

    Planning to buy a predictive analytics platform? Read Best Predictive Analytics Software next.

    This article was updated February 2022 by Jenna Phipps.