Seeking predictive analytics software solutions? Unveil 2024’s best tools to drive your data analysis and business intelligence.
The best predictive analytics software leverages historical data to forecast future trends, behaviors, and outcomes to inform your business’s decision-making and enhance its strategic planning and resource allocation. These tools use advanced algorithms and machine learning techniques to analyze vast datasets and identify patterns and predict results.
We evaluated the top predictive analytics software platforms on the market to see how they compared on features, data connectors, and price—here are our picks for the nine best predictive analytics solutions of 2024:
The comparison table below outlines the core features of our best predictive analytics software, the number of data connectors they offer, the availability of a free version, free trial durations, and pricing information.
| Core Features | Data Connectors | Free Trial Duration | Pricing | |
|---|---|---|---|---|
| SAP Analytics Cloud |
|
N/A* | 60 days | $396/user/year |
| Alteryx AI Platform |
|
80+ | 30 days | $4,950/user/year |
| SAS Viya |
|
40+ | 14 days | N/A* |
| Tableau |
|
90+ | 30 days | $75/user/month |
| KNIME Analytics Platform |
|
300+ | Free version | Free |
| Qlik Cloud Analytics |
|
500+ | 30 days | $20/user/month |
| Altair RapidMiner |
|
150+ | Free version | N/A* |
| Power BI |
|
200+ | Free version | $10/user/month |
| IBM Watson Studio |
|
80+ | Free version | $1.02/capacity unit-hour |
*Vendor does not disclose information
Best Overall
SAP Analytics Cloud integrates business intelligence (BI), planning, and predictive analytics in a comprehensive cloud-based platform. It ranked as our best predictive analytics software overall because of its topnotch predictive analytics capabilities, unified approach to analytics and planning, augmented analytics, and broad integration with third-party solutions.
Its predictive analytics feature uses artificial intelligence (AI) and machine learning (ML) to automatically reveal hidden relationships in data and generate actionable insights. Unlike many other solutions that treat analytics and planning as separate processes, SAP Analytics Cloud brings them together in a single workflow. This not only streamlines the analytics process, but also lets you create and link financial and operational plans, leading to better business outcomes.
SAP Analytics Cloud’s augmented analytics feature democratizes analytics and puts it in the hands of each user, so everyone in your organization goes from insight to action in a fraction of the time. Finally, this predictive analytics software’s integration with various third-party applications ensures that you can use your existing data investments for analytics and planning activities.
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SAP Analytics Cloud boasts a modern interface with a wealth of information and functionalities. While it may seem overwhelming at first, it offers a great deal of potential once you become familiar with its navigation. It includes machine-generated analytics and insights in the Smart Insights panel that let you explore datasets and visualize new insights. The visual browsing of dimensions and hierarchies makes it easy to slice and dice data and gain insights, and the built-in messaging and discussion feature facilitates collaborative decision-making among team members.
SAP Analytics Cloud’s recent update introduces AI-driven natural language queries for simpler data exploration and better predictive planning. This update highlights SAP’s ongoing efforts to stay at the forefront of business intelligence technology.
Selecting SAP Analytics Cloud as one of our best predictive analytics solutions is a no-brainer for us. It is a feature-rich and powerful analytics software that can meet the needs of a wide range of organizations. Aside from providing advanced predictive analytics capabilities, it offers customers several options for customer support, has clear pricing details, and can be a valuable addition to any company.
| Pros | Cons |
|---|---|
| AI-driven natural language queries | Steep learning curve |
| AI-powered augmented analytics | Third-party integration can be complex |
| Clear pricing | Resource-intensive |
Learn more on SAP Analytics Cloud’s pricing page.
Best for AI-Powered Enterprise Analytics
The Alteryx AI Platform is an all-in-one solution that uses AI and ML for better data analysis. Its predictive analytics capabilities are integrated into the entire workflow, streamlining data access, preparation, modeling, and sharing. As an enterprise-grade software, it guarantees data protection with the latest security standards and offers connectivity through open API standards.
This platform is powered by AiDIN, Alteryx’s AI engine, which allows for faster innovation by discovering new patterns in data that were previously undiscoverable. Additionally, its Generative AI creates contextualized data visualizations in several forms, providing data visualization summaries.
Alteryx brings robust data security with industry standards through built-in data encryption, role-based access control (RBAC), and audit trails. It also complies with GDPR, HIPAA, and SOC 2.
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The Alteryx AI Platform has a user-friendly interface that makes it easy for businesses to get started with data analytics. Its drag-and-drop functionality aids in building and modifying workflows effortlessly, without the need for coding. The Favorites sidebar gives quick access to frequently used tools, which can save time and effort.
Since 2019, the company has been adding new functionalities related to topic modeling, text summarization, and sentiment analysis. In January 2024, Alteryx changed the name of the platform to reflect that the future of Alteryx is A, and it continues to invest in AI innovation, including generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities.
Alteryx AI Platform made it to our list because it equips users of all technical levels to tackle complex predictive analytics tasks with minimal or no coding with its comprehensive suite of tools. Also, its pre-built and customizable ML models let you employ proven algorithms without the need for extensive programming.
| Pros | Cons |
|---|---|
| Automated Feature Selection and Engineering feature assists in building high-quality ML models | Limited visualization options |
| Enterprise-grade AI | Steep learning curve |
| Clear pricing | Customer support slow response times |
Learn more on Alteryx’s pricing page.
Best for Large-Scale Data Processing
SAS Viya offers powerful data analysis, visualization, and predictive analytics capabilities in one integrated solution. It’s designed for high performance, using in-memory analytics to handle large-scale data processing efficiently. This tool simplifies predictive analytics through automated model building, transparent model tuning, and model management. Together, these features enable users to identify effective models quickly, fine-tune their performance, and manage them without a hitch.
SAS Viya can effectively process large datasets efficiently by performing in-memory processing and distributing tasks across a cluster to significantly speed up data analysis. This eliminates the need for manual data partitioning and organization, reducing processing time and complexity. Additionally, most sorting routines can be removed from existing SAS advanced analytics programs, taking efficiency to new heights.
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SAS Viya has a modern, user-friendly interface that enhances usability. It organizes data well, minimizing confusion and maximizing user experience. The UI has a few, colorful graphs that are not only aesthetically pleasing to look at, but also easily comprehensible. Lists are nicely spaced, providing an unobtrusive design. It also features different tabs and advanced filtering options, which allows for flexible data access and customization.
In 2024, SAS will release the SAS Viya Workbench, a lightweight development environment that quickly executes code in a cloud-native, efficient, and secure way. It promotes a multi-language architecture, enabling users to build highly performant and production-ready models in their preferred language, Python, R, or SAS. This tool will allow data scientists to use the tools they are most comfortable with to build predictive models.
Although the vendor doesn’t give out clear pricing details, we still picked SAS Viya for its exceptional capabilities. Its multi-threaded processing, in-memory analytics engine, and the ability to handle large datasets make it a valuable asset for businesses dealing with big data analytics. What’s more, its open analytics platform supports programming in multiple languages, making it accessible to data scientists and analysts with varying skill sets. In our opinion, these capabilities supersede the uncertainty in their cost structure.
| Pros | Cons |
|---|---|
| Advanced analytical visualizations | Steep learning curve |
| Open analytics platform | Occasional system slowness when running large datasets |
| Automated model building | Lacks pricing transparency |
Contact the SAS sales team to learn more.
Best for Scenario Analysis
Tableau is primarily a visualization and business intelligence tool with built-in predictive analytics powered by Einstein Discovery. This software aids organizations in transforming raw data into understandable and actionable insights and allows you to build AI-powered predictive and prescriptive analytics models, so you can forecast trends, identify patterns, and make data-driven decisions.
Tableau’s predictive analytics brings unified predictive modeling functions, letting you fill in missing data using built-in calculated fields. It also supports complex analyses, such as scenario analysis. You can test different scenarios, moving from theories and questions to a professional-looking dashboard that lets even non-experts ask questions and test their own scenarios. This is made possible by combining the system’s flexible front-end with input capabilities.
In addition, Tableau’s scenario analysis is complemented by its excellent visualization capabilities. You can present your enterprise data in a visually appealing and understandable manner, making it easier to interpret results.
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Tableau has a user-centric interface that streamlines data exploration and analysis so individuals of varying technical backgrounds can navigate it confidently. The intuitive interface enables you to swiftly capture key insights at a glance. Important details are meticulously presented through a curated selection of graphs, organized in a logical and structured manner.
Salesforce added the Tableau Pulse feature to the Tableau platform, letting users take advantage of generative AI for insightful, actionable analytics. Tableau Pulse simplifies discovering key metrics and understanding their business implications. It automatically detects trends, contributors, and outliers, giving users a full understanding of their key metrics. This new feature is included with all Tableau Cloud editions and Embedded Analytics solutions at no additional cost.
We chose Tableau because it allows users of all skill levels to engage with data and uncover insights. Its predictive analytics capabilities, particularly scenario analysis, can assist you in anticipating future trends, mitigating risks, and seizing opportunities for your organization. Plus, it has many out-of-the box integrations, promoting seamless data analysis and collaboration.
| Pros | Cons |
|---|---|
| No-code predictive modeling | Resource-intensive |
| Automated insights | Steep learning curve |
| Clear pricing | Customer support delayed response times |
Learn more on Tableau’s pricing page.
Best for Cost-Efficient SMBs
The KNIME Analytics Platform delivers an open-source suite of tools for end-to-end data science workflows. It comes with a rich set of functionalities for data preprocessing, data visualization, ML, predictive modeling, and statistical analysis. In terms of predictive analytics, KNIME offers machine learning algorithms, supports different predictive analytics modeling techniques, and brings advanced model evaluation and validation methods.
KNIME is an ideal choice for cost-efficient small and medium-sized businesses (SMBs). Since it is open-source and free, it eliminates the financial barriers often associated with advanced analytics platforms. This lets SMBs make use of sophisticated data science capabilities without straining their budgets. On top of that, KNIME’s data connectors enable you to integrate data from various sources, which is typically a requirement for businesses that use a diverse set of systems and applications.
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KNIME Analytics Platform has a graphical user interface (GUI) that allows non-technical users to perform complex data manipulations. It has a drag-and-drop feature that simplifies navigation for everyone, including beginners. It follows a consistent design throughout the entire software, further simplifying navigation regardless of the context or section you are in. Switching perspectives in the KNIME Analytics Platform will only display the active workflow and its loaded tabs, eliminating confusion from multiple open workflows.
KNIME introduced its K-AI artificial intelligence Assistant, which answers questions and constructs workflows, thereby streamlining the entire process of creating KNIME workflows. By automatically generating analytics workflows based on user instructions, K-AI expedites the learning process and increases the efficiency of any data solution.
We selected KNIME Analytics Platform because it strikes a balance between cost-effectiveness and proficiency. It gives organizations access to a feature-rich and flexible predictive analytics tool without breaking the bank. Its combination of advanced features, ease of use, and affordability make it an excellent choice for SMBs looking to leverage data science to drive their business forward.
| Pros | Cons |
|---|---|
| Free | Resource-intensive |
| User-friendly | Not suitable for large datasets |
| Highly-flexible | No built-in real time prediction |
Best for Data-Driven Decision-Making
Qlik Cloud Analytics brings analytical features for a diverse spectrum of users and scenarios, including self-managed analytics, interactive dashboards, and predictive analytics. It also includes conversational analytics, a metadata catalog with lineage tracking, mobile analytics capabilities, as well as reporting and alerting functions. The predictive analytics component allows users to forecast trends and make data-driven decisions for the future.
Its unique Associative Engine feature allows you to explore and analyze data independently, fostering a deeper understanding of business data. Another distinct feature of the Qlik Cloud Analytics Platform is its integration of Qlik Sense, a tool dedicated to self-service data visualization and exploration. This provides you with a smooth experience as you analyze your data and gain insights.
The Qlik Cloud Analytics Platform also supports real-time data streaming, real-time predictions, and real-time data movement, making sure that you can make timely decisions based on the most current data.
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Qlik Cloud Analytics Platform’s user interface is simple and clear, establishing direct information that is understandable to both beginners and pros. Key graphs and information are neatly grouped into panels, making it easy for users to find the data they need. The interface is also customizable, so you can choose the data you want to see based on your specific role and access information most relevant to your needs.
Qlik added significant enhancements to the Qlik Cloud Analytics platform, with a focus on visualizations, formatting, and functionality, indicating that the vendor wants to elevate user experience and ease of use. This update brings a feature-rich Pivot Table, a flexible Layout Container for chart placement, and diverse chart styling options. There’s also a new Copy and Paste Style feature that simplifies the creation of consistent dashboards.
We picked Qlik Cloud Analytics for several reasons, including its broad range of machine learning algorithms, Associative Engine that lets users explore and analyze data on their own, automated feature selection and engineering, and capability to handle large datasets and demanding predictive analytics tasks. This software also has the most number of data connectors out of all the predictive analytics solutions on our list.
| Pros | Cons |
|---|---|
| AI-assisted creation and data prep | Resource-intensive |
| Associative Engine | Steep learning curve |
| Clear pricing | Limited design customization options |
Learn more on Qlik Cloud Analytics’ pricing page.
Best for Versatility
Altair RapidMiner is a data science platform offering extensive predictive analytics capabilities. It has built-in data preparation and exploration functions as well as model validation tools to support all your data science projects and predictive analytics use cases.
RapidMiner is known for its versatility. It can handle both small and large datasets, making it suitable for businesses of all sizes. The memory-based system analyzes as much data as the RAM in servers allows. It can tag, classify, and analyze unstructured data sources. Also, it integrates with big data platforms such as Hadoop and Spark, streamlining the analysis of large-scale datasets.
The platform’s cloud-based solutions use Amazon S3 for data storage, and therefore uses the encryption features provided by Amazon S3 to protect the data.
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RapidMiner’s UI has a clean, modern, and straightforward design, making it easy to understand. It displays a few but important and recognizable icons on its main menu and sidebar, simplifying navigation. You can customize the dashboard to your liking, and add, resize, and rearrange panels with a few simple point-and-click actions. There are also many visualization options to choose from.
Altair made enhancements to the Altair RapidMiner platform that includes allowing users to create custom large language models, like ChatGPT, with their own data. Additionally, RapidMiner’s AutoML capabilities have been expanded and now has automated clustering, predictive modeling, feature engineering, and time series forecasting.
We recommend Altair RapidMiner for its solid featureset, user-friendly interface, and versatility. This predictive analytics software also has a free version for non-commercial use, which can be a great resource for those learning data science, teaching a course, conducting research, or assessing the software for industrial use.
| Pros | Cons |
|---|---|
| User-friendly | Resource-intensive |
| Extensive feature set | Lacks pricing transparency |
| Free version | Access to customer support services requires separate purchase |
Contact Altair’s sales to learn more.
Best for Complex Data Analysis
Microsoft Power BI is a business analytics tool with features related to predictive analytics, such as integration with Azure Machine Learning, built-in time series analysis, and forecasting. While it focuses largely on interactive visualizations and BI, the software comes with several algorithms and tools for creating predictive models.
This tool stands out for its ability to handle complex data analysis thanks to its capacity to work with large datasets. It compresses extensive data to manage it effectively and uses Power Query for advanced data transformation and cleaning, making data preparation a breeze. The tight integration with R and Python enables users to perform advanced statistical analysis and machine learning tasks. Finally, the solution’s DAX (Data Analysis Expressions) language allows you to carry out advanced calculations on data models, increasing the depth of data analysis.
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Power BI’s interface is generally user-friendly and intuitive, with a clean and focused design that emphasizes key data and reduces clutter. It maintains consistency in design elements like colors and fonts, which contribute to a cohesive and professional look. However, some users may initially find the interface a bit overwhelming when dealing with complex datasets or exploring the sophisticated features that Power BI has to offer, including a wide array of data visualizations.
Power BI introduced a new feature that automatically generates descriptions for semantic model measures, optimizing the documentation process. It’s a valuable addition for users, particularly report builders, as it delivers essential measure documentation that refine clarity and understanding.
Aside from its rich feature set, Power BI became one of the contenders for our best predictive analytics software because of its seamless integration with other Microsoft products like Office 365, Dynamics 365, and Azure. This connection boosts its utility in a Microsoft-centric business environment. Its built-in ML capabilities allow you to build regression models, data classification models, and clustering models also contributed to this software being a frontrunner on our list. Power BI has a free desktop version that lets you enjoy its predictive analytics at no cost.
| Pros | Cons |
|---|---|
| Generous free trial duration and free version | Integrations more focused on Microsoft ecosystem |
| Data compression to extract insights from large datasets | Resource-intensive |
| Clear pricing | Steep learning curve |
Learn more on Power BI’s pricing page.
Best for Data Science and Machine Learning
IBM Watson Studio is a cloud-based data science and machine learning platform with predictive analytics capabilities. It equips data scientists, developers, and analysts with the necessary tools to build, run, and manage AI models. It also allows you to predict future trends and behaviors. Watson Studio supports numerous machine learning algorithms and popular programming languages like Python, R, and Scala.
Watson Studio is particularly well-suited for data science and machine learning for several reasons. First, since it supports many machine learning algorithms, data scientists and machine learning engineers can choose the most appropriate algorithm for their specific use case. Second, the software comes with modern data preparation and cleaning tools, which are needed for accurate and reliable models. Third, it can handle large-scale data projects. Overall, IBM Watson Studio is a comprehensive platform that can meet the needs of data scientists, developers, and analysts alike.
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The interface, with its numerous tabs and options, can be challenging to navigate. Users performing simple tasks may find these extra features unnecessary and confusing. With that said, the platform provides a lot of useful functionalities once mastered. For example, it can present insights precalculated by the system and likely to be of interest to the user. Running analytics is also fairly easy once learned: you just have to click on a specific data set or type a question into the question bar to prompt IBM Watson Studio to search for the relevant information in all the loaded datasets.
IBM’s acquisition of Manta Software Inc., a provider of data lineage solutions, strengthens its data and AI governance capabilities within Watson Studio. This acquisition promotes transparency and trust in data environments by showing a detailed map of data flows, sources, and dependencies. It helps businesses verify the accuracy and integrity of data used in AI models and systems, identify and anonymize sensitive data, and reduce the risk of data exposure and regulatory breaches.
We picked IBM Watson Studio because of its versatility, ability to deal with large-scale data, and free version that can help you become a better data scientist or domain expert in a collaborative environment. In addition, this predictive analytics software has robust data cleaning and preparation tools that save time and effort in the data preprocessing stage, which is often the most time-consuming part of a data science project.
| Pros | Cons |
|---|---|
| Free version | Steep learning curve |
| Extensive open source frameworks | Slow performance |
| Clear pricing | Limited integration capabilities compared to competitors |
Learn more on IBM Watson Studio’s pricing page.
Predictive analytics solutions sift through vast amounts of data to uncover patterns and insights that might otherwise remain hidden. They use this data to forecast future trends, behaviors, and outcomes. Though they offer a wide range of features, all predictive analytics software has five key features that work together to help businesses anticipate potential opportunities or risks, and make more informed decisions.
This feature involves gathering and organizing relevant information from various data sources to ensure in-depth and high-quality data. Effective data collection captures key variables and trends, establishing a foundation for analysis and modeling. Without robust data collection, the software lacks the necessary inputs to generate meaningful insights and predictions.
Data visualization transforms raw data into intuitive visual representations, such as charts, graphs, and dashboards. As a result, you can interpret complex datasets, identify patterns, trends, and outliers effectively. By presenting information clearly and concisely, this feature boosts comprehension and communication, allowing users to make decisions based on a deeper understanding of the underlying data
Machine learning involves using algorithms to analyze large data volumes and extract valuable insights. This feature lets the software learn from historical data and dynamically adapt its predictions or recommendations based on new information. Machine learning algorithms uncover complex patterns and relationships within the data, unearthing insights beyond traditional analysis methods. Moreover, these models continuously improve as they encounter more data, refining their accuracy and predictive capabilities.
Predictive modeling employs statistical techniques to analyze historical data and foresee future trends. As a result, organizations can reduce uncertainties, detect potential risks, and make proactive decisions to maximize performance. Through predictive modeling, your organization can gain valuable insights into possible outcomes and quantify uncertainties, so you can mitigate risks.
This feature lets you generate insights and make decisions instantaneously based on incoming data streams. It enables your organizations to respond quickly to changing market conditions, emerging trends, or unexpected events. Real-time prediction capabilities make use of advanced algorithms and processing techniques to analyze data in real-time, providing timely recommendations and alerts to users.
To create an accurate assessment of the best predictive analytics software, we meticulously analyzed a comprehensive set of criteria. Our evaluation focused on five major aspects: core features, costs, customer support, integrations, and user-friendliness.
We evaluated the effectiveness of each solution against these predefined criteria, resulting in unbiased scores based on their individual performance. By consolidating these scores, we systematically arrived at the final scores for each tool.
For the core features, we took into account the main features of predictive analytics tools and their level of comprehensiveness. This included data collection, data visualization, ML, predictive modeling, statistical techniques, real-lime prediction, data preparation, data mining, and security.
Criteria winner: SAS Viya, Qlik Cloud Analytics, Altair RapidMiner, SAP Analytics Cloud, Power BI, and IBM Watson Studio
We considered factors such as the availability of a free version, the duration of free trials, and transparency for this criteria.
Criteria winner: KNIME Analytics Platform, Power BI, and IBM Watson Studio
We verified if the predictive analytics solutions deliver out-of-the-box integrations with third party tools, including data sources, data cleaning, big data platforms, BI software, cloud platforms, and machine learning libraries. We also factored in the availability of custom integrations.
Criteria winner: Tableau
To fairly score the customer service quality of each vendor, we researched if they offer several customer support options to all users 24 x 7, regardless of their payment tier. These options include live chat, phone, email, vibrant community, and complete documentation. Delayed and ineffective customer support responses also had an impact on the scores.
Criteria winner: Qlik Cloud Analytics and SAP Analytics Cloud
In calculating the scores for this criteria, we took into account the ease of management, installation, and configuration of the predictive analytics software for users of varying levels of technical expertise. We gathered several user feedback from multiple independent sources to maintain integrity and fairness.
Criteria winner: KNIME Analytics Platform and Altair RapidMiner
While using predictive analytics software comes with valuable benefits to organizations, it also brings the following challenges:
To overcome these challenges, organizations can improve data quality with data cleansing tools, conduct training for understanding results, develop technical expertise among staff, and use cloud-based solutions or specialized software for infrastructure management. A clear strategy and process are also imperative for effective use of predictive analytics.
This list of best predictive analytics software solutions aims to introduce you to the most reliable options available today and highlight the essential features to consider when choosing a tool for your business.
Before you finalize your choice, it’s important to consider the variety and volume of data sources in your business, which could include databases, cloud services, and applications you use in your daily business operations. The predictive analytics software you select should be capable of effectively processing and analyzing data from these diverse sources. Free trials and free software versions are a great opportunity to test the tools and determine their suitability based on your unique environment and intended use.
Predictive analytics software solutions work best when used in conjunction with data mining tools to uncover hidden patterns and insights within large datasets. Read our guide to get acquainted with the best data mining tools today and gain valuable insights from your enterprise data.
Liz Ticong is a tech industry expert with hands-on experience in AI, software testing, and product analysis. Specializing in AI news, software reviews, and buyer’s guides, she rigorously tests and experiments with the latest AI and tech tools to provide in-depth, practical insights. As a contributor to eWeek and TechRepublic, she simplifies complex topics, helping readers make well-informed decisions.
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