What is Predictive Analytics?

Predictive analytics is the study of current and historical data to forecast future outcomes. Organizations want to understand how current decisions impact future growth and finances. Predictive analytics helps them guess future trends based on patterns and relationships in existing data. It aims to minimize risks, increase productivity, and guide strategic decision-making. \n

Analysts use mathematical modeling, machine learning, and other advanced data science techniques to answer what-if questions for business. For example, marketing analysts use predictive analytics to determine future product sales, weather stations use it to forecast weather, and stockbrokers use it to maximize trading returns.","id":"ams#c1","heading":"What is predictive analytics?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

Analysts use four types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. \n

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

  Predictive analytics vs. descriptive analytics \n

  Descriptive analytics is the data science that allows data engineers to learn what happened in past events. It explores past data and presents it in easily understandable statistical models, such as tables and charts. For example, companies have used descriptive analytics to study seasonal sales trends for several years.  \n

  Descriptive analytics is based on factual events and patterns uncovered through data mining techniques. However, it does not predict future events, as predictive analytics does. \n

  Predictive analytics vs. prescriptive analytics \n

  Predictive analytics tells you what might happen based on past events, whereas prescriptive analytics further recommends decisions that influence the outcome. For example, the predictive model suggests that the delivery team cannot cope with the coming festive seasons. Factory managers then use prescriptive analytics to find the best delivery schedules, courier services, and personnel shift arrangements. ","id":"ams#c2","heading":"What is the difference between predictive analytics and other types of analytics?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

  The ability to predict aspects of the future is critical. Engineers, scientists, businesses, and economists have long used predictive analytics to guide their activities. Developments in machine learning technology have allowed data science to expand predictive modeling into previously too difficult or complex areas. Scalable computing, data mining, and deep learning techniques enable 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. Benefits include \n

  Reduced decision risks \n

  Management and employees make many decisions daily that impact the company's performance. Predictive analytics tools help stakeholders to support their choices with data-driven indicators. For example, data analysts forecast future demands to support a product launch in a new market segment.  \n

  Personalized customer experiences \n

  Predictive analytics applications allow companies to engage with customers more effectively by analyzing market trends and customer data. For example, marketing teams create a more targeted campaign by recommending products based on past buying behaviors, which leads to more sales. \n

  Improved productivity  \n

  Predictive analytics is essential in helping companies optimize and scale their operations. Business managers use predictive data analysis to identify workflow bottlenecks if variables like workforce, sales, and material cost fluctuate. They simulate different scenarios to anticipate potential issues.","id":"ams#c3","heading":"Why is predictive analytics important?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

  Many organizations actively use predictive analytics to guide real-time and future outcomes. Here are some predictive analytics examples. \n

  Finance \n

  Banking and fund managers make high-stakes decisions that might affect the financial institution's profitability. Predictive analytics allows them to make determinations confidently by providing business intelligence based on past transactional data. For example, \n

  \n
  • Loan managers use advanced analytics software to predict credit risk before approving loans to applicants.  \n
  • Banking security teams use predictive analytics software to identify abnormal transactional data suggesting fraudulent activities. \n
  • Insurance companies can use predictive modeling to identify false insurance claims.  \n \n

    Retail \n

    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 and predictive recommendations to increase up-and-cross-selling opportunities based on customer profiles. Here, predictive analytics determines more effective marketing strategies. Companies also use predictive analytics to forecast future demand and sales. \n

    Manufacturing \n

    Manufacturers use predictive analytics to improve productivity, cost efficiency, and quality across the supply chain. For example, procurement managers use predictive analytics to forecast material prices and secure them at the lowest possible rate. Meanwhile, the logistics department runs predictive analyses to chart optimum delivery routes and reduce shipping expenses. \n

    Manufacturing also uses predictive machine learning to identify potential equipment failure. Technicians can carry out scheduled repairs with minimum impact on the production schedule. Manufacturers use predictive data analytics to monitor production line equipment to optimize throughput, detect irregularities, and highlight equipment defects. Manufacturing companies use predictive analytics to monitor machinery, identify conditions, and predict maintenance requirements. \n

    Healthcare \n

    The healthcare industry benefits from predictive analytics on both macro and micro levels. For example, medical experts use predictive modeling to chart the path of global diseases based on changing variables such as vaccine development and availability. Doctors also use healthcare predictive analytics to monitor patients' symptoms and anticipate complications that might arise in the future. Healthcare companies use predictive analytics on patient monitoring equipment to detect real-time changes in patients' conditions while eliminating spurious alarms that render patient monitoring equipment ineffective.","id":"ams#c4","heading":"What are use cases for predictive analytics?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

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

    Predictive analytics models \n

    Predictive analytics models consist of techniques, formulas, and mathematical principles that enable computers to calculate the probability of an event happening based on certain assumptions. These models attempt to answer probabilistic questions, such as: \n

    \n
    • What are the chances of a particular customer defaulting on a loan? \n
    • How will specific marketing and financial decisions impact future share prices? \n
    • How long will a machine run before it needs repair? \n \n

      Predictive analytic models that guide future business decisions tend to be complex and consider numerous factors. They generally take time to develop and validate, and need continual refining to adapt to changes in the business and economic environment.  \n

      Predictive analytics models can include classification models \n

      Building the model \n

      Organizations use predictive modeling to analyze possible outcomes for historical and transactional data. The predictive model is built by following these steps: \n

      Define objectives \n

      The team discusses the question they’d like to predict to understand the business objectives. By correctly scoping the business objectives of the predictive analytics case, you can start to identify the model’s inputs, outputs, and relevant datasets. \n

      Collect required data \n

      The next step is to consolidate data from different sources into a data warehouse. Data is collected from sources such as emails, ERP systems, spreadsheets, and other enterprise applications. Predictive modeling typically becomes more accurate when you provide larger datasets to the statistical model, rather than a few data points.  \n

      Train and deploy the model \n

      It is now possible to analyze the sample data using statistical techniques and predictive technology. You can integrate the model with enterprise applications once the predictive modeling techniques produce consistent and accurate results. This provides access to every business department so they can make accurate forecasts.","id":"ams#c5","heading":"How does predictive analysis work?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

      As with many machine learning applications, predictive analytics is a dynamic activity that constantly uses new data to update predictions. This means the technique uses the pipeline of data cleansing, model training, deployment, feedback, retraining, redeployment, and the ability to ingest data in close to real-time. Data scientists use the following predictive analytics techniques. \n

      Decision trees \n

      A decision tree is a machine-learning model that allows the software to make predictions by answering a series of yes-or-no questions. Like its name, this technique emulates a tree shape with nodes and branches. Each node contains a feature specific to the problem that must be answered before proceeding to the next node. Each node branches out to two leaves, which lead to the subsequent nodes. \n

      A decision tree can predict both qualitative and quantitative data. For example, you can use a decision-tree predictive model to predict property prices or a patient's health conditions based on noticeable symptoms. Decision trees are easy to understand but less flexible when analyzing diverse new data.   \n

      Regression analysis \n

      Regression is a statistical approach data scientists use to make predictions by classifying or correlating new data with known data sets. Linear regression models the relationship between an independent variable and a dependent value on a two-dimensional chart. For example, HR managers use linear regression to predict a candidate's salary based on years of experience.  \n

      Meanwhile, logistic regression classifies the variables into two or more categories based on probabilities. For example, IT teams use logistic regression to detect and predict whether an email is spam. The model classifies the email as suspicious if it finds too many undesired characteristics beyond a set threshold.  \n

      Time series analysis \n

      Time series analysis is a predictive analytics technique used to analyze data points collected or recorded over time, recognizing the importance of temporal order. This makes it particularly useful in forecasting applications such as stock price movements, energy consumption, or demand planning in supply chain management. \n

      A key method within time series analysis is the autoregressive integrated moving average (ARIMA), which models time-dependent relationships by factoring in past values and errors to predict future trends. More advanced approaches, such as Long Short-Term Memory (LSTM), maintain memory over extended timeframes, leveraging deep learning to capture long-term dependencies in time series data. \n

      Deep learning neural networks \n

      Deep learning has revolutionized predictive analytics by allowing models to process complex, high-dimensional data and uncover intricate relationships that traditional techniques might miss. Neural networks are particularly effective when making predictions with complex data such as images, videos, and voice recordings. Deep learning models enhance predictive capabilities across complex industries such as healthcare and cybersecurity.","id":"ams#c6","heading":"What are common predictive analytics techniques?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"bodyContent":"

      Analytics on AWS offers a comprehensive set of capabilities for every analytics workload.  \n

      Building your own predictive analytics models and workflows on AWS starts with Amazon SageMaker. Amazon SageMaker offers an integrated experience for analytics, artificial intelligence, and machine learning with unified access to all your data.  \n

      Collaborate and build faster from a unified studio using familiar AWS tools for model development in SageMaker AI, generative AI, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. Access all your data, whether it’s stored in data lakes, data warehouses, or third-party or federated data sources, with governance built in to meet enterprise security needs. \n

      You can also use Amazon SageMaker Canvas, a no-code service with dozens of built-in predictive models and capabilities to support the entire predictive analytics workflow, from data preparation to model building and training, generating predictions, and deploying the models to production. It provides business analysts with a visual point-and-click interface to generate accurate predictions independently—without requiring machine learning experience or writing a single line of code. \n

      Get started with predictive analytics on AWS by creating a free AWS account today.","id":"ams#c7","heading":"How can AWS help with predictive analytics?","mediaShouldReset":"false"},"metadata":{"tags":[]}},{"fields":{"id":"ams#rt-grouped-questions1830621454#pattern-data","patternDark":"false"},"metadata":{"tags":[{"name":"pattern-data","description":"Default pattern data","id":"ams#rt-grouped-questions1830621454#pattern-data","namespaceId":"rt-grouped-questions"}]}}]},"metadata":{"auth":{},"testAttributes":{}},"context":{"page":{"pageUrl":"https://aws.amazon.com/what-is/predictive-analytics/"},"contentType":"page","environment":{"stage":"prod","region":"us-west-2"},"sdkVersion":"2.0.26"},"refMap":{"manifest.js":"269d4ac6b1","rt-grouped-questions.rtl.css":"6041e37f19","rt-grouped-questions.js":"76731b0c78","rt-grouped-questions.css.js":"1d375453ec","rt-grouped-questions.css":"ec816aa39b","rt-grouped-questions.rtl.css.js":"590781e0f0"},"settings":{"templateMappings":{"patternDark":"patternDark","id":"id","heading":"heading","answerSummary":"answerSummary","title":"title","bodyContent":"bodyContent","hyperlinkText":"hyperlinkText","hyperlinkUrl":"hyperlinkUrl","mediaUrl":"mediaUrl","mediaAltText":"mediaAltText","mediaShouldReset":"mediaShouldReset","videoOverlayTitle":"videoOverlayTitle","videoThumbnailUrl":"videoThumbnailUrl"}}}

      Page topics

      • What is predictive analytics?
      • What is the difference between predictive analytics and other types of analytics?
      • Why is predictive analytics important?
      • What are use cases for predictive analytics?
      • How does predictive analysis work?
      • What are common predictive analytics techniques?
      • How can AWS help with predictive analytics?

      What is predictive analytics?

      Predictive analytics is the study of current and historical data to forecast future outcomes. Organizations want to understand how current decisions impact future growth and finances. Predictive analytics helps them guess future trends based on patterns and relationships in existing data. It aims to minimize risks, increase productivity, and guide strategic decision-making.

      Analysts use mathematical modeling, machine learning, and other advanced data science techniques to answer what-if questions for business. For example, marketing analysts use predictive analytics to determine future product sales, weather stations use it to forecast weather, and stockbrokers use it to maximize trading returns.

      What is the difference between predictive analytics and other types of analytics?

      Analysts use four types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics.

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

      Predictive analytics vs. descriptive analytics

      Descriptive analytics is the data science that allows data engineers to learn what happened in past events. It explores past data and presents it in easily understandable statistical models, such as tables and charts. For example, companies have used descriptive analytics to study seasonal sales trends for several years. 

      Descriptive analytics is based on factual events and patterns uncovered through data mining techniques. However, it does not predict future events, as predictive analytics does.

      Predictive analytics vs. prescriptive analytics

      Predictive analytics tells you what might happen based on past events, whereas prescriptive analytics further recommends decisions that influence the outcome. For example, the predictive model suggests that the delivery team cannot cope with the coming festive seasons. Factory managers then use prescriptive analytics to find the best delivery schedules, courier services, and personnel shift arrangements. 

      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. Developments in machine learning technology have allowed data science to expand predictive modeling into previously too difficult or complex areas. Scalable computing, data mining, and deep learning techniques enable 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. Benefits include

      Reduced decision risks

      Management and employees make many decisions daily that impact the company's performance. Predictive analytics tools help stakeholders to support their choices with data-driven indicators. For example, data analysts forecast future demands to support a product launch in a new market segment. 

      Personalized customer experiences

      Predictive analytics applications allow companies to engage with customers more effectively by analyzing market trends and customer data. For example, marketing teams create a more targeted campaign by recommending products based on past buying behaviors, which leads to more sales.

      Improved productivity 

      Predictive analytics is essential in helping companies optimize and scale their operations. Business managers use predictive data analysis to identify workflow bottlenecks if variables like workforce, sales, and material cost fluctuate. They simulate different scenarios to anticipate potential issues.

      What are use cases for predictive analytics?

      Many organizations actively use predictive analytics to guide real-time and future outcomes. Here are some predictive analytics examples.

      Finance

      Banking and fund managers make high-stakes decisions that might affect the financial institution's profitability. Predictive analytics allows them to make determinations confidently by providing business intelligence based on past transactional data. For example,

      • Loan managers use advanced analytics software to predict credit risk before approving loans to applicants. 
      • Banking security teams use predictive analytics software to identify abnormal transactional data suggesting fraudulent activities.
      • Insurance companies can use predictive modeling to identify false insurance claims. 

      Retail

      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 and predictive recommendations to increase up-and-cross-selling opportunities based on customer profiles. Here, predictive analytics determines more effective marketing strategies. Companies also use predictive analytics to forecast future demand and sales.

      Manufacturing

      Manufacturers use predictive analytics to improve productivity, cost efficiency, and quality across the supply chain. For example, procurement managers use predictive analytics to forecast material prices and secure them at the lowest possible rate. Meanwhile, the logistics department runs predictive analyses to chart optimum delivery routes and reduce shipping expenses.

      Manufacturing also uses predictive machine learning to identify potential equipment failure. Technicians can carry out scheduled repairs with minimum impact on the production schedule. Manufacturers use predictive data analytics to monitor production line equipment to optimize throughput, detect irregularities, and highlight equipment defects. Manufacturing companies use predictive analytics to monitor machinery, identify conditions, and predict maintenance requirements.

      Healthcare

      The healthcare industry benefits from predictive analytics on both macro and micro levels. For example, medical experts use predictive modeling to chart the path of global diseases based on changing variables such as vaccine development and availability. Doctors also use healthcare predictive analytics to monitor patients' symptoms and anticipate complications that might arise in the future. Healthcare companies use predictive analytics on patient monitoring equipment to detect real-time changes in patients' conditions while eliminating spurious alarms that render patient monitoring equipment ineffective.

      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 and create predictive models to forecast likely behavior from big data.

      Predictive analytics models

      Predictive analytics models consist of techniques, formulas, and mathematical principles that enable computers to calculate the probability of an event happening based on certain assumptions. These models attempt to answer probabilistic questions, such as:

      • What are the chances of a particular customer defaulting on a loan?
      • How will specific marketing and financial decisions impact future share prices?
      • How long will a machine run before it needs repair?

      Predictive analytic models that guide future business decisions tend to be complex and consider numerous factors. They generally take time to develop and validate, and need continual refining to adapt to changes in the business and economic environment. 

      Predictive analytics models can include classification models

      Building the model

      Organizations use predictive modeling to analyze possible outcomes for historical and transactional data. The predictive model is built by following these steps:

      Define objectives

      The team discusses the question they’d like to predict to understand the business objectives. By correctly scoping the business objectives of the predictive analytics case, you can start to identify the model’s inputs, outputs, and relevant datasets.

      Collect required data

      The next step is to consolidate data from different sources into a data warehouse. Data is collected from sources such as emails, ERP systems, spreadsheets, and other enterprise applications. Predictive modeling typically becomes more accurate when you provide larger datasets to the statistical model, rather than a few data points. 

      Train and deploy the model

      It is now possible to analyze the sample data using statistical techniques and predictive technology. You can integrate the model with enterprise applications once the predictive modeling techniques produce consistent and accurate results. This provides access to every business department so they can make accurate forecasts.

      What are common predictive analytics techniques?

      As with many machine learning applications, predictive analytics is a dynamic activity that constantly uses new data to update predictions. This means the technique uses the pipeline of data cleansing, model training, deployment, feedback, retraining, redeployment, and the ability to ingest data in close to real-time. Data scientists use the following predictive analytics techniques.

      Decision trees

      A decision tree is a machine-learning model that allows the software to make predictions by answering a series of yes-or-no questions. Like its name, this technique emulates a tree shape with nodes and branches. Each node contains a feature specific to the problem that must be answered before proceeding to the next node. Each node branches out to two leaves, which lead to the subsequent nodes.

      A decision tree can predict both qualitative and quantitative data. For example, you can use a decision-tree predictive model to predict property prices or a patient's health conditions based on noticeable symptoms. Decision trees are easy to understand but less flexible when analyzing diverse new data.  

      Regression analysis

      Regression is a statistical approach data scientists use to make predictions by classifying or correlating new data with known data sets. Linear regression models the relationship between an independent variable and a dependent value on a two-dimensional chart. For example, HR managers use linear regression to predict a candidate's salary based on years of experience. 

      Meanwhile, logistic regression classifies the variables into two or more categories based on probabilities. For example, IT teams use logistic regression to detect and predict whether an email is spam. The model classifies the email as suspicious if it finds too many undesired characteristics beyond a set threshold. 

      Time series analysis

      Time series analysis is a predictive analytics technique used to analyze data points collected or recorded over time, recognizing the importance of temporal order. This makes it particularly useful in forecasting applications such as stock price movements, energy consumption, or demand planning in supply chain management.

      A key method within time series analysis is the autoregressive integrated moving average (ARIMA), which models time-dependent relationships by factoring in past values and errors to predict future trends. More advanced approaches, such as Long Short-Term Memory (LSTM), maintain memory over extended timeframes, leveraging deep learning to capture long-term dependencies in time series data.

      Deep learning neural networks

      Deep learning has revolutionized predictive analytics by allowing models to process complex, high-dimensional data and uncover intricate relationships that traditional techniques might miss. Neural networks are particularly effective when making predictions with complex data such as images, videos, and voice recordings. Deep learning models enhance predictive capabilities across complex industries such as healthcare and cybersecurity.

      How can AWS help with predictive analytics?

      Analytics on AWS offers a comprehensive set of capabilities for every analytics workload. 

      Building your own predictive analytics models and workflows on AWS starts with Amazon SageMaker. Amazon SageMaker offers an integrated experience for analytics, artificial intelligence, and machine learning with unified access to all your data. 

      Collaborate and build faster from a unified studio using familiar AWS tools for model development in SageMaker AI, generative AI, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. Access all your data, whether it’s stored in data lakes, data warehouses, or third-party or federated data sources, with governance built in to meet enterprise security needs.

      You can also use Amazon SageMaker Canvas, a no-code service with dozens of built-in predictive models and capabilities to support the entire predictive analytics workflow, from data preparation to model building and training, generating predictions, and deploying the models to production. It provides business analysts with a visual point-and-click interface to generate accurate predictions independently—without requiring machine learning experience or writing a single line of code.

      Get started with predictive analytics on AWS by creating a free AWS account today.