8 best predictive analytics tools on the market

8 best predictive analytics tools on the market

September 7, 2022

Tatyana Korobeyko

Data Strategist

With the big data, machine learning and AI, near infinite cloud storage and compute resources modern companies have at their disposal, the future of data analytics appears quite certain. Indeed, 59% of the surveyed organizations are already moving forward with the use of advanced and predictive analytics by either investing in in-house expertise or by relying on professional predictive analytics consultancy, the Global State of Enterprise survey by MicroStrategy reveals. In this blog post, we’ll overview predictive analytics benefits as well as share the eight best tools to start the journey and factors you need to consider while choosing between them.

What is predictive analytics software?

Predictive analytics tools are various tools used to extract information from current and historical datasets to build predictive models that help companies define the probability of some future or otherwise unknown event happening. Today, predictive analytics software is commonly referred to as data science and machine learning tools, although sometimes business intelligence and big data software are added to the list.

Predictive analytics and its techniques

Within the data analytics maturity ladder, predictive analytics is between diagnostic analytics, which reveals the causes of certain business outcomes, and prescriptive analytics, which suggests the optimal course of action to take. Predictive analytics helps mine historical and current data for trends and patterns to forecast the probability of certain events or outcomes with the help of statistical modeling and machine learning.

Types of predictive analytics models

To make predictions as accurate as possible, companies employ a whole range of predictive analytics techniques, which they choose based on their specific business needs and requirements. The most common types of predictive analytics models include:

Regression analysis

Used to identify the relationships among variables. For example, it can be used to identify how increased advertising spending influences monthly sales or how excess weight in a person can influence the probability of having a thromboembolism of the pulmonary artery.

Decision trees

Used for predicting the class or value of the target variable based on the decision rules derived from the training data. Such models are used to predict how likely a customer is to pay off their loan, identify at-risk patients for healthcare prioritization, assess expansion opportunities, identify prospective customers, etc.

Neural networks

Used to establish complex data relationships between the inputs and outputs of the explored dataset. This predictive analytics model type is used in image recognition, recommendation engines, stock market prediction, etc.

Predictive analytics benefits

Organizations are adopting predictive analytics to solve complicated business tasks and uncover new opportunities. To display the real value of predictive analytics adoption, we’ve listed its most common use cases across industries:

  • Improve customer experience. Marketing departments employ predictive analytics to identify target customer segments and predict customer behavior. Based on the derived insights, they can allocate the efforts efficiently, for example, towards designing effective customer loyalty programs, developing retention strategies, etc.
  • Optimize marketing efforts. With predictive analytics capabilities on hand, retailers can identify how shoppers navigate brick-and-mortar stores and plan merchandise accordingly to boost sales. Predictive analytics engines also serve to dynamically optimize prices, forecast the demand for particular products and product bundles, predict the effectiveness of promotional activities, and facilitate upselling and cross-selling. 
  • Identify profitable customers and predict sales. Sales representatives rely on predictive modeling for lead scoring to determine which customers to reach out to in the first place. They also run models to calculate customer lifetime value and forecast sales for sales strategy planning and budget allocation.
  • Increase productivity of production processes. Manufacturing companies run predictive analytics models to identify factors of poor quality or production failures and come up with a solution. 
  • Balance supply and demand. Various industries embrace predictive analytics capabilities to optimize warehouse and logistic operations by forecasting demand and planning shipment and order fulfillment accordingly. 
  • Reduce risks. Financial institutes adopt predictive analytics for predicting fraudulent transactions, determining a credit score, forecasting market behavior, and predicting customer attrition.
  • Predict clinical risk. Healthcare organizations turn to predictive analytics for the early detection of deteriorating patients and identification of patients at risk of chronic diseases.

Do you have a predictive analytics project in mind?

Let's discuss

Best predictive analytics software

Tool Best for Deployment Pricing Free trial Website

Alteryx Analytic Process Automation Platform

Self-service predictive analytics across all departments Alteryx cloud and on premises

Per user, per year

Designer - $5,195/user/year

Intelligence Suite - $2,300/user/year
Available See more

Azure Machine Learning

Automating ML workflows for data scientists, ML engineers, and application developers with cost control and visibility

Cloud, on premises, hybrid, multi-cloud

Tied to resource usage

Free tier available
Available See more

AWS SageMaker

Running ML workflows for developers and data scientists in the AWS cloud within a managed infrastructure AWS cloud

Tied to resource usage

Free tier available
Available See more

H2O Driverless AI

Automating various ML workflows for data scientists, ML engineers, and business analysts with rich explainability functionality Cloud and on premises

Pricing for the enterprise version on request

Free version available

Available See more

IBM SPSS

Automating ML workflows for individual users or groups of users with different levels of expertise across the enterprise Cloud, on premises, hybrid

Per user, per month

IBM SPSS Statistics – from $99/user/month

IBM SPSS Modeler – from $499/user/month
Available See more

RapidMiner Data Science Platform

End-to-end augmented data science lifecycle management Cloud and on premises

Pricing for the commercial version on request

Free version available
Available See more

SAS Advanced Analytics

Automating the entire analytics lifecycle at enterprise grade, accelerated ML models operationalization Public cloud, private cloud, on premises On request Available See more

TIBCO Data Science

Production and execution of ML on edge devices for asset-centric organizations Cloud and on premises On request Available See more

Choosing the optimal predictive analytics software is an effort-intensive endeavor, which requires comprehensive analysis and domain expertise. To help you accurately evaluate potential predictive analytics software, we reviewed the top predictive analytics software vendors down below, listing them in alphabetical order.

Alteryx Analytic Process Automation Platform

Alteryx

Alteryx’s main product is the APA platform, a unified solution, which serves to automate analytics, ML and data science processes and offers self-service predictive analytics capabilities across all departments.

Company

Alteryx

Product

Key product: Alteryx Analytic Process Automation (APA) Platform

Supporting products:  Alteryx Designer, Alteryx Intelligence Suite, Alteryx Server, Alteryx Connect, Alteryx Promote

Key features

  • Secure connection to 80+ natively integrated data sources, including Amazon, Oracle, and Salesforce
  • Ingesting, cleansing, blending and reformatting data from on premises, cloud and hybrid data environments
  • Extracting data from semi-structured and unstructured sources (PDFs, text files, and images)
  • Support for diagnostic, predictive, prescriptive, geospatial, etc. analytics.
  • Assisted machine learning models creation
  • Automation of data preparation, blending, reporting, data science, machine learning and AI with hundreds of code-free automation building blocks
  • Embedding Alteryx functionality into interfaces, dashboards, and business processes
  • Advanced analytics with built-in R and Python integration
  • Visual text mining and NLP capabilities
  • Collaboration environment equipped with role-based access and version control
  • Drag-and-drop data prep, blending, and analytics

Deployment

Alteryx cloud and on premises

Pricing

Designer - $5,195/user/year

Intelligence Suite - $2,300/user/year

Pricing for other products and services is available upon direct request.

Free trial available

Azure Machine Learning

Azure Machine Learning

Azure Machine Learning is Microsoft’s cloud service that supports the end-to-end predictive analytics lifecycle and automates workflows for data scientists, ML engineers and application developers.

Company

Microsoft Azure

Product

Key product: Azure Machine Learning

Supporting products: Azure Synapse Analytics, Azure Arc, Azure SQL Database, Azure Storage Blobs, Azure App Service, Power BI, etc.

Key features

  • Machine learning–assisted data labeling
  • Low-code data management and machine learning pipelines creation with a drag-and-drop user interface
  • Automated ML development with built-in feature engineering, algorithm selection, hyperparameter tuning, etc.
  • Collaborative notebooks
  • Support of open-source frameworks and languages for ML creation (MLflow, Kubeflow, PyTorch, TensorFlow, scikit-learn, XGBoost, Python, R, .NET, etc.)
  • ML auditability and governance with built-in tracking and lineage
  • Seamless integration with other Azure services (Microsoft Power BI, Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, Azure Arc, Azure Security Center, and Azure Databricks)
  • Support of various machine learning methods and algorithms (classification, regression, time-series forecasting, natural language processing tasks, computer vision, etc.)
  • Dynamic resource optimization with resource-level quota limits and automatic shutdowns
  • Built-in data encryption, role-based access control, managed identity for compute resources, private endpoints, etc.

Deployment

Cloud, on premises, hybrid, multi-cloud

Pricing

No surcharge for Machine Learning Service, you pay only for computing and storage resources used.

Free trial available

AWS SageMaker

AWS SageMaker

Amazon SageMaker is a cloud machine learning platform for data scientists, developers, and business analysts to accelerate ML model development with a fully managed infrastructure and tools as well as support for major ML frameworks, toolkits, and programming languages.

Company

Amazon Web Services

Product

AWS SageMaker

Key features

  • Automatic ML modes creation with SageMaker Autopilot
  • Visual point-and-click user interface for no-code ML models development
  • ML models bias detection and limitation
  • Seamless data import from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, Snowflake, Databricks Delta Lake, etc.
  • Ingestion of data from different file formats (CSV files, Parquet files, JSON files, database tables, etc.)
  • 300+ built-in data transformations and support for custom transformations in PySpark, SQL, and Pandas
  • Automatic detection of training errors and continuous monitoring of resource utilization
  • Accelerated ML models training with distributed training libraries
  • ML models optimization for efficient  running on target hardware devices
  • A unified repository for feature use across the ML lifecycle 
  • Accelerated data labeling workflows with AWS Ground Truth
  • Pre-trained ML models (TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV) and 150+ built-in algorithms (NLP, object detection, image classification, linear regression, etc.)
  • Continuous automatic model tuning with SageMaker Model Monitor
  • ML deployment across multiple availability zones
  • Automatic health checks
  • Performance monitoring and triggering alarms for any changes occurring in production performance with Amazon CloudWatch
  • Comprehensive security (ability to run SageMaker in the AWS private cloud, ML models encryption in transit and at rest, AWS Identity and Access Management framework, etc.)
  • Automatic suggestion of the optimal AWS compute instance for the best price-performance ratio

Deployment

AWS Cloud

Pricing

Pricing is tied to resource usage.

Free trial available

H2O Driverless AI

H2O Driverless AI

A data science platform to satisfy the needs of various employees, including data scientists, ML engineers, and business analysts.

Company

H2O.ai

Product

Key product: H2o Driverless AI (commercial enterprise version)

Supporting products: H2O 3 (open source), H2O Sparkling Water (for Spark Integration), H2O AutoML for ML, H2O Wave

Key features

  • Integration with Hadoop, Python, Spark, and leading cloud services
  • Support of popular statistical and machine learning algorithms (gradient boosted machines, generalized linear models, deep learning, etc.)
  • ML model workflow automation (hyperparameter autotuning, label assignment, ML model documentation, etc.)
  • Automatic feature engineering with the help of built-in transformer recipes, an open catalog of recipes, and bring-your-own recipe support
  • User interface creation and machine learning integration with a low-code application development framework (Python/R)
  • Complete ML model interpretability
  • Time series forecasting capability
  • Natural language processing with TensorFlow and PyTorch
  • Image processing capability
  • Automatic scoring pipelines

Deployment

On premises and cloud

Pricing

Pricing for the enterprise version is upon direct request to the vendor.

Free version available

Free trial available

IBM SPSS

IBM SPSS

IBM offers a suite of predictive analytics tools to be run both in the cloud and on premises. The suite includes SPSS Statistics, which helps users perform complex statistical analysis and SPSS Modeler used for building predictive models.

Company

IBM

Product

IBM SPSS Statistics

IBM SPSS Modeler

Key features

IBM SPSS Modeler is a visual data science and ML solution to speed up operational tasks for data scientists and conduct analysis regardless of where the data is located, its size, or whether it is structured or unstructured.

  • Accessing data from flat files, spreadsheets, relational databases, data warehouses, Hadoop distributions, etc.
  • Automated data preparation (e.g., data transformation into the optimal format) and modeling
  • Support of various machine learning methods and algorithms (decision trees, neural networks, and regression models, ARMA, ARIMA, support vector machine, temporal causal modeling, time series, generative adversarial networks, etc.)
  • Support of geographic spatial data analysis
  • Saving and deploying models from most popular ML frameworks (Scikit-learn, TensorFlow, etc.)
  • Smart graphics engine with automated recommendations
  • Support of open source technologies (Hadoop, Spark, R, Python)
  • Intuitive graphical interface to meet the needs of both non-technical business users and analytical professionals

 

IBM SPSS Statistics is a comprehensive solution supporting the entire analytical lifecycle from data preparation to analysis and reporting.

  • Supports all common data sources used by enterprise organizations
  • Data preparation (automation, rule-based data validation, anomaly detection, optimal binning capabilities, and more) and bootstrapping (measure bias, variance, confidence intervals, prediction error of sample estimates) capabilities across all license types
  • Descriptive statistics
  • Linear regression
  • Basic hypothesis testing
  • Factor analysis
  • Geospatial analysis
  • Cluster analysis
  • Support for R and Python
  • Three add-ons to extend the capabilities of the plan to meet the needs of users at all skill levels (custom tables and advanced statistics, complex sampling and testing, forecasting and decision trees)

Deployment

Cloud, on premise, hybrid

Pricing

  • IBM SPSS Statistics – from $99/user/month (subscription plan). Pricing information for perpetual and term licenses is available upon request.
  • IBM SPSS Modeler – from $499/user/month (subscription plan). Pricing information for perpetual and term licenses is available upon request.

IBM SPSS Modeler is also available as part of IBM Watson Studio.

Free trials available

RapidMiner Data Science Platform

RapidMiner Data Science Platform

RapidMiner is a data science software platform aimed to meet the needs of data scientists and tech-savvy business users for data preparation, machine learning development, predictive analytics, and text mining.

Company

RapidMiner

Product

Key Product: RapidMiner Data Science Platform

Supporting products: RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud, RapidMiner Model Ops, etc.

Key features

  • Drag-and-drop user interface
  • Library of 1,500 native ML algorithms
  • Support for many third-party machine-learning technologies (R, Python, Scala, Java, MATLAB, Octave, HiveQL, Pig, SQL, and Groovy)
  • Notebooks and integration with custom Python and R
  • Augmented capabilities for data prep with Turbo Prep, model generation with Auto Model and model deployment with Model Ops
  • Feature-sharing catalog for sharing of predictive models across the organization
  • ML explainability and governance capabilities for increased transparency
  • Pre-built templates for common use cases (customer churn, predictive maintenance, fraud detection, etc.) in self-service predictive analytics
  • Proactive recommendations at every step of predictive model creation
  • Point and click connections to databases, enterprise data warehouses, data lakes, cloud storages, business applications, and social media
  • Basic and advanced ML techniques, including regression, clustering, time-series, bayesian modeling, neural networks, support vector machines, model ensembles, deep learning, etc.
  • Support for the Local Interpretable Model-Agnostic Explanations (LIME) framework

Deployment

On premises, cloud

Pricing

The pricing of the commercial version is available upon direct request.

Free version available

Free trial available

SAS Advanced Analytics

SAS Advanced Analytics

SAS offers a set of integrated predictive analytics solutions designed for the needs of all types of users – from business managers and business analysts to data stewards and data scientists. The tools support the whole predictive modeling life cycle from data preparation to ML model operationalization.

Company

SAS

Product

Key product: SAS Visual Data Mining and Machine Learning

Supporting products:

  • SAS Enterprise Miner,
  • SAS Factory Miner,
  • SAS Machine Learning on SAS Analytics Cloud,
  • SAS Model Manager,
  • SAS Predictive Modeling Workbench for SAP HANA,
  • SAS Visual Statistics,
  • SAS Analytics Pro,
  • SAS/ETS,
  • SAS/IML,
  • SAS/STAT,
  • SAS Forecast Server,
  • SAS Visual Forecasting,
  • SAS Visual Text Analytics, etc.

Key features

  • Augmented (with automated suggestions) data preparation and cleaning for integrating and synchronizing data into a unified format
  • Interactive visuals supported at every step of the modeling process
  • Statistical analysis used to perform simple descriptive statistics as well as complex Bayesian analyses (variance analysis, categorical data analysis, predictive modeling, time series analysis, multivariate analysis, survival analysis, psychometric analysis, mixed-models analysis, etc.)
  • A broad set of algorithms (decision trees, linear and logistic regression, neural networks, hierarchical clustering, self-organizing maps, sequence and web path analysis, and more) for open, code-based machine learning model development
  • Machine learning model deployment with the automatically generated score code in SAS, C, Java and PMML in both batch and real-time environments
  • Centralized machine learning model management (registering, validating, publishing, scoring and monitoring models) with a web-based workflow console
  • Visual drag-and-drop interactive interface for the entire analytical life cycle
  • Integrated text analytics support of 33 native languages out of the box
  • Integrates with other SAS tools, Python, R, Java, Teradata, SAP HANA, etc.
  • Focused tools optimized for specific industries (banking, retail, government, and healthcare)

Deployment

Public cloud, private cloud, on premises

Pricing

Pricing is available upon direct request.

Free trials available

TIBCO Data Science

TIBCO Data Science

TIBCO Data Science integrates the capabilities of TIBCO Statistica, TIBCO Spotfire Data Science, TIBCO Spotfire Statistics Services, and TIBCO Enterprise Runtime for R to support the end-to-end machine learning lifecycle and automate the steps involved, from data ingestion and preparation to model deployment, monitoring, and governance. 

Company

TIBCO

Product

TIBCO Data Science

Key features

  • Comprehensive built-in analytics capabilities (machine learning, graph/network, predictive, and text analytics, regression, clustering, time-series, decision trees, neural networks, data mining, multivariate statistics, statistical process control (SPC), and design of experiments (DOE))
  • Creation of data prep, analytic, and scoring pipelines with a drag-and-drop interface
  • Robust security capabilities (role-based security for any asset, built-in version control, audit logs, etc.)
  • Integration with Amazon, Azure, and Google ecosystems, Python, R, Jupyter Notebooks, C#, and Scala. (Amazon SageMaker, Microsoft Azure, Google TensorFlow, Algorithmia, Azure ML, Apervita, and H20.)
  • Code-free data exploration, preparation, transformation, predictive modeling and evaluation with a drag-and-drop interface
  • Collaboration capabilities supported with an intuitive interface, reusable data prep and analytic workflow templates

Deployment

Cloud, on premises

Pricing

Pricing is available upon direct request.

Free trial available

Partner up with Itransition to embrace predictive analytics

Predictive analytics consulting

Predictive analytics consulting

Our consultants will put together a tech stack that will work for you and help you implement it within the set time and budget.

Predictive analytics software selection: best practices

The process of choosing predictive analytics software is pretty much the same as for any other piece of software – too complex for generalization and does not have a one-size-fits-all approach. Here are some aspects you need to clear up when selecting predictive analytics tools:

Use cases

Today, the predictive analytics market offers both generic solutions applicable across all industries and industry- or case-specific tools with a well-crafted set of capabilities. Therefore, to avoid reinventing the wheel, you need to carefully explore if any of the existing tools have the functionality you need. 

Software users

You need to establish whether the solution will be used by well-seasoned data scientists, business users, or both. The first set are usually looking for the functionality to augment data discovery, preparation, and model development, while the latter seek a more automated solution that helps reduce the time and expertise required to tune and build predictive models from scratch.

Solution scalability

You need to make sure the solution is seamlessly scaled to accommodate a user base increase and allow for the advancement of existing analytics capabilities as soon as the need arises.

Deployment flexibility

Every year, more and more cloud solutions with a subscription-based pricing model are being offered, which could be relevant for companies which lack in-house resources or the budget to set up and maintain full-scale systems, create predictive models, scale efficiently, etc.

Integration flexibility

You need to ascertain if the solution would fit well into your existing technology environment, integrated with the relevant data sources as well as business applications and systems.

An afterword

Predictive analytics software adoption can be tricky for many organizations. According to the Thrive in the Digital Era with AI Lifecycle Synergies research conducted by IDC, 50% of ML and AI initiatives fail. The reasons behind these numbers, along with the data quality challenges, lack of skilled personnel, and software costs, is the inability to choose an optimal tech stack. We have shared only a number of factors to start with when making the choice, however, the list is not exhaustive and each particular case requires careful examination from professional consultants.