What is predictive analytics?

Predictive analytics is the study of historical and current data to make future predictions. It uses a mixture of advanced mathematical, statistical, and machine learning techniques to analyze data to determine and extrapolate hidden trends. 

Many businesses and organizations use predictive analytics to guide future decisions. For example, marketing analysts use predictive analytics to determine future sales for their products, weather stations use it to forecast weather, and stockbrokers use it to maximize trading returns.

Predictive analytics and the analytics hierarchy

Analysts use four types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. The analytics hierarchy is as follows. 

  • Descriptive analytics identifies what has happened in the past through historical analysis
  • Diagnostic analytics uses historical data to explain why something happened in the past
  • Predictive analytics predicts future trends based on patterns found in historical and current data
  • Prescriptive analytics prescribes future actions and decisions, allowing businesses to optimize decision-making

Why is predictive analytics important?

The ability to predict aspects of the future is critical. Engineers, scientists, businesses, and economists have long used predictive analytics to guide their activities. These include techniques still in use today, such as regression analysis and decision trees. 

Developments in machine learning technology have allowed data science to expand predictive modeling into areas that were previously too difficult or complex to handle. Scalable computing, data mining and deep learning techniques allow businesses to dig deep into their data lakes and extract information and trends. Predictive analytics has become embedded in business processes, giving organizations at the forefront a significant competitive advantage.

How does predictive analysis work?

Predictive analytics today is largely based on advanced machine learning techniques. Data scientists use deep learning and complex algorithms to analyze multiple variables to create predictive models able to forecast likely behavior from big data. 

As with many machine learning applications, predictive analytics is a dynamic activity that’s constantly using new data to update predictions. This means the technique uses the classic machine tool pipeline of data cleansing, model training, deployment, feedback, retraining, and redeployment together with an ability to ingest data in close to real time. Techniques include decision trees, regression analysis, time series analysis, and deep learning neural networks. 

Predictive analytic models that guide future business decisions tend to be complex and involve taking numerous factors into account. These generally take time to develop and validate and need continual returning to adapt to changes in the business and economic environment. 

What are use cases for predictive analytics?

Many organizations actively use predictive analytics to guide real-time and future decision-making.


The financial sector uses predictive analytics to guide decision-making. Examples include predictive market trading, credit risk assessment, and loan approvals. Insurance companies use predictive analytics to predict extreme weather events to mitigate insurance claims and detect fraudulent insurance claims.


Retail companies use predictive analytics to forecast regional and local customer demand and pre-deliver stock to regional and local distribution stations to reduce delivery times. Other companies use lead scoring models to improve lead conversion rates as well as predictive recommendations to increase up- and cross-selling opportunities based on customer profiles. Companies also use predictive analytics to forecast future demand and sales.


Manufacturers use predictive analytics to monitor production line equipment to optimize throughput, detect irregularities, and highlight equipment defects. Manufacturing companies use predictive analytics to monitor machinery, identify machine conditions, and predict maintenance requirements.


Healthcare companies use predictive analytics on patient monitoring equipment to detect real-time changes in patients' conditions while largely eliminating spurious alarms that render patient monitoring equipment ineffective. Other use cases include medical diagnosis and predicting the course of a disease based on past data and the patient's health.

What are benefits of predictive analytics?

There are four key benefits of predictive analytics:

  1. Predictive analytics helps customers to make informed decisions.
  2. Predictive analytics can provide real-time answers. Trained predictive analytics models can ingest data in real time and provide immediate answers.
  3. Predictive analytics can help customers understand complex problems. It can help reveal patterns in data faster and more accurately.
  4. Predictive analytics can help companies gain a competitive advantage. Companies using predictive analytics have a competitive advantage over those who don't because of an ability to more accurately predict future events.

How can AWS help with predictive analytics?

To get started with predictive analytics, you can explore Amazon SageMaker Canvas, which expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface. This allows analysts to generate accurate ML predictions on their own—without requiring any machine learning experience or having to write a single line of code. You can use SageMaker Canvas for a range of use cases, including predicting customer churn, predicting on-time deliveries, and inventory planning.

You can also explore Amazon SageMaker to build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows.

To get started, explore the SageMaker Canvas tutorial.

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