5 Predictive Analytics Tools to Help You Unlock Insights from Data

In today’s data-driven world, it is essential to take a proactive rather than reactive approach to potential setbacks. To stay ahead of the curve, businesses must anticipate outcomes, customer behaviors, and market trends—that’s where predictive analytics tools come in. These platforms incorporate mathematical modeling, machine learning, and historical data to predict future events and outcomes, allowing businesses to optimize processes and mitigate potential risks. 

Increasing investments in big data and the incorporation of Internet of Things (IoT) and artificial intelligence (AI) have increased the demand for predictive analytics tools, with the predictive analytics market expected to grow by 22.5 percent between 2025 and 2032 [1]. Whether you’re in finance, health care, marketing, or retail, predictive analytics tools can help you make informed decisions and improve customer experiences. 

Explore some popular predictive modeling tools, how to choose the right tool for your business, and careers that use predictive modeling to forecast outcomes.

What is predictive analytics?

Predictive analytics is the use of historical and current data to predict future trends and outcomes. It involves the use of mathematical and statistical models, machine learning, and data analysis to make highly precise predictions about future events based on patterns detected in data. Predictive analytics can help organizations to:

• Make data-informed decisions

• Obtain real-time answers to business questions 

• Reduce risks like cyberattacks 

• Improve workflow efficiency and profit margins

The predictive analytics process starts with data collection, followed by the formulation, training, and modification of a statistical model to make predictions by analyzing correlations between various elements in a data set. Different types of predictive modeling techniques, including regression, classification, clustering, and time-series modeling, can be applied to obtain insights from data.

What are the four types of data analytics?

Data analytics has four key types: descriptive, which analyzes raw data to identify “what happened” in the past; diagnostic, which examines data related to a past event to answer “why did this happen”; predictive, which forecasts “what might happen” in the future based on trends uncovered from historical and current data; and prescriptive, which provides actionable insights about “what to do next” about the predicted trend. 

What is a predictive analytics tool?

Predictive analytics tools use advanced statistical modeling, data mining, and machine learning to analyze current and historical data and detect patterns and trends to forecast future outcomes. In this way, predictive analytics tools help businesses make better decisions with AI-powered analytics, visualization, and reporting.

5 popular predictive analytics tools

Explore some popular predictive analytics tools currently available, including their notable features, pricing, and limitations.

Alteryx

Alteryx is an end-to-end, AI-powered self-service platform that offers predictive analytics within the entire data analysis workflow, unifying data prep, modeling, and results sharing on one platform. Alteryx also offers a free Predictive Analytics Starter Kit to introduce you to its various predictive analytics tools. The Alteryx AiDIN combines AI, machine learning, and generative AI (genAI) to help you quickly identify new patterns in data that were previously undiscoverable.

Notable features: 

• Allows cloud, on-premises, and hybrid connections

• Offers 300+ drag-and-drop automation tools to streamline the creation of analytic workflows

• Automated machine learning (AutoML) helps automate repetitive tasks and feature engineering 

• Auto Insights feature uses natural language and genAI to summarize and visualize results clearly

• Offers low-code, no-code, spatial, and prescriptive analysis tools to analyze trends with precision

• Real-time results grid allows you to visualize changes in your data while building workflows

Cost: Available upon contacting the sales team; free trial available 

Best for: Data scientists who need to automate data collection from several sources to produce multiple deliverables

Limitations: Extensive visualization features are lacking; cost can be an issue for some users; scaling large data sets can be problematic.

H2O.ai

H2O is an open-source machine learning platform offering various AI tools. H2O supports a number of statistical and machine learning algorithms, such as gradient boosted machines and deep learning. H2O has helped a number of organizations solve their business problems with its AI solutions. For example, H2O helped AT&T, a leading telecommunications company, reduce its expenditure by 70 percent by optimizing call center operations and improving customer experience [2]. 

Notable features:

• Provides support for cloud and on-premises sources 

• H2O Flow serves as a graphical notebook-style user interface for building models without any coding

• AutoML feature automates building AI models, including model development and feature engineering.

• H2O Driverless AI features an AI Wizard that analyzes your data and recommends a suitable model and machine learning technique based on your data and needs

Cost: Available upon request; free trial available

Best for: Data scientists, IT professionals, DevOps professionals, and business analysts

Limitations: Model deployment and management features are lacking; multiple simultaneously running models can burden the system. 

SAP Analytics Cloud

SAP Analytics Cloud is a cloud-based platform that combines business intelligence, enterprise planning, and predictive analytics capabilities into one product. Leveraging machine learning and AI, the platform allows you to simulate business scenarios and automatically generate plans based on smart predictions. SAP Analytics Cloud has helped multiple businesses revolutionize their operations. For example, using SAP Analytics Cloud for financial planning and reporting, GEA, a German manufacturing company, could obtain new insights and reduce its need for technical support by 30 percent [3].

Notable features:

• SAP Datasphere integration combines data preparation, modeling, and analytics into a single tool

• AI-powered Joule copilot helps automate workflows and enhance decision-making.

• Compass feature uses Monte Carlo simulations to predict outcomes of business scenarios.

• Smart Predict feature allows you to investigate relationships in your data set and build a formula for a predictive scenario to forecast future trends.

Cost: For a pay-as-you-go plan, $1,435.20 per unit/month for the “Planning, Professional” option, $812.50 per unit/month for business intelligence users, and $1,218.10 per unit/month for the “Planning, Standard” option (unit price may vary based on the number of units required); SAP Business Technology Platform Enterprise Agreement (BTPEA) and subscription plans also available [4]; free trial available

Best for: Advanced data analysts, business users, and data scientists

Limitations: Steeper learning curve; integration with external collaboration platforms can be less intuitive 

SAS Viya

SAS Viya is an end-to-end AI-driven data analytics platform that allows you to manage and integrate data from potentially any source, identify and optimize the best machine learning models, and deploy models seamlessly across your enterprise. The SAS Viya platform offers advanced scalability and faster model deployment, allowing teams to be an average of 4.6 times more productive than other platforms [5].

Notable features:

• Offers a range of cloud deployment options, including Azure, Google Cloud, and AWS

• Automatically segments data based on time series patterns such as volume and seasonality.

• You can model and simulate complex business scenarios using large-scale observational data with SAS Econometrics.

• Real-time event streaming analytics allow you to assess data from streaming sources such as transactions and IoT devices and use AI to unlock insights and make intelligent decisions.

Cost: Available upon request; free trial available 

Best for: Data engineers, data scientists, MLOps engineers, business analysts

Limitations: Data visualization can be too complex; limited flexibility in browser-based access 

IBM SPSS

The IBM SPSS Predictive Analytics Enterprise platform offers statistics-based descriptive and predictive analytics, combined with automated data preparation, data mining, and decision management. It can understand both structured and unstructured data and integrate predictive analytics with business objectives to optimize operations. 

Notable features:

• Combines different IBM predictive analytics software into one platform, including IBM SPSS Statistics and IBM SPSS Modeler

• Offers a full set of predictive analytics and visualization capabilities, allowing users without programming or statistical knowledge to obtain predictive insights

• Supports multiple data sources and structured and unstructured data formats

• Can be scaled and deployed for multiple business use cases

Cost: For SPSS Statistics, the base plan is $99.00 per user/month, the 12+ month plan is $1,524 per user/year, and the permanent license is $3,830 per user; a free trial is available [6]. For SPSS Modeler, pricing starts at $499 per user/month for 1 year; a free trial is available [7].

Best for: Large organizations with big databases

Limitations: Slower performance with large data sets; prices can be expensive for individuals and small enterprises 

How to choose a predictive analytics tool?

The process of choosing a predictive analytics tool will first and foremost depend on your business needs and data sets. Identify the questions you want the tool to answer or the purpose you want it to serve, such as sales forecasting or fraud detection, and evaluate whether your data sets are complete and relevant for the tool. 

The next step involves determining the right tool for your business. Various tools are currently available that can be generalized to a number of use cases or applied to specific industries. Evaluate your industry and what type of tool you require, as all-purpose predictive analytics tools can sometimes fail to meet compliance standards often required by industries such as health care and finance. 

When looking at predictive analytics tools for your business, consider the following features:

• Accuracy and performance: Look for performance metrics like F1 score, confusion matrix, and confidence score to monitor the tool’s accuracy. Read reviews on reputable websites and download free trials to assess whether the tool can work with your needs.

• Scalability: Ensure that the tool can support the continuous increase in the amount of data and multiple data sources. Look for a tool that offers AI features and AutoML so that it can analyze more complex data and make data preparation and model training easier.

• User-friendly interface: To ensure your tool isn’t too complicated for your team to use, look for software with intuitive features such as drag-and-drop interfaces, AI-powered query builders, and data visualization capabilities.

• Human-in-the-loop feedback: The tool should have the capability to incorporate human oversight to ensure the mitigation of incorrect or biased outcomes. Look for tools that allow you to make adjustments to parameters so that your model remains accurate and reliable.

Careers that use predictive analytics

Jobs in the field of predictive analytics are in high demand across industries due to the increasing reliance on data to shape business decisions. Explore some jobs that use predictive analytics and their salaries below:

• Business intelligence analyst: $115,000 

• Cloud engineer: $148,000

• Cloud architect: $145,796

• Data analyst: $195,000

• Data scientist: $150,000

• Cloud systems engineer: $150,000

• Machine learning specialist: $158,000

• IoT engineer: $149,000

• Predictive modeler: $111,000

• Big data engineer: $141,000 

*All salary data sourced from Glassdoor as of May 2025.

Explore predictive analytics tools on Coursera

Predictive analytics software can simplify the process of data analysis to predict future trends and provide business insights. If you’re looking to learn more about predictive analytics, consider starting out by first grasping the fundamentals of data analysis. For example, you can opt for the Meta Data Analyst Professional Certificate on Coursera, which will demonstrate how to use statistical techniques to extract insights from data. Alternatively, if you want to focus on predictive analytics for business intelligence, you can opt for the IBM Business Intelligence (BI) Analyst Professional Certificate. This course can give you the opportunity to understand how to use data analysis tools to identify trends in data and create accurate visualizations.