Predictive Analytics Definition & Meaning

Predictive analytics is the process of studying large amounts of data and observing patterns and trends in the data that can inform realistic predictions for the future. 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. Although a predictive analytics platform cannot guarantee perfect forecasting, it’s a good resource to make more accurate predictions.

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.

Predictive analytics models for businesses

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.

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 some of 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. It’s also possible that, 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 can 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.

Predictive analytics tools

Many software vendors offer predictive analytics, and some predictive analysis falls under a larger analytics category. We’ve listed four popular tools in the predictive analytics field:

Oracle Data Mining works within the Oracle Database to mine data and design prediction models. Oracle is one of the biggest names in database software, and its data mining tool offers additional insights about the information in databases. Users can visualize the data analysis process using the Data Miner GUI.

IBM Predictive Analytics offers an SPSS platform for predictive models. Artificial intelligence and sensors give the platform greater accuracy and observation skills.

SAS Advanced Analytics offers options for modeling in an extremely visual platform. SAS is one of the most renowned and respected names in analytics, and they have good resources for predictive analytics.

SAP Predictive Analytics integrates with business intelligence and planning in SAP’s Analytics Cloud platform. SAP offers large-scale data resources for BI and analytics needs.

Some predictive analytics tools integrate with other software, which can help businesses consolidate and use their data more effectively. Some are designed for one specific platform or main purpose. Oracle, for example, is made to work in the Oracle Database and works with SQL. Some predictive analytics tools are designed to function in the cloud, but not all are.

Webopedia predictive analytics resources

Big Data Analytics Expert Predictions

What is Predictive Model Markup Language (PMML)

Predictive Analysis Defined

What is Big Data Analytics

Revolution Analytics – Big Data Analytics Software




Jenna Phipps
Jenna Phipps
Jenna Phipps is a contributor for websites such as and Enterprise Storage Forum. She writes about information technology security, networking, and data storage. Jenna lives in Nashville, TN.

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