Predictive analysis software is used in many business industries to improve an aspect of a business. Predictive analysis tools use statistics, data science, machine learning, and other artificial intelligence (AI) techniques to improve a business function or predict the most likely outcome of a future event.

Here’s our list of the top predictive analytics software solutions:

  • Google Cloud BigQuery: Best for intense interactive ad-hoc queries
  • Alteryx: Best for Self-service predictive analytics across an entire organization
  • IBM SPSS: Best for businesses with personnel with multiple skill levels
  • Minitab: Best for organizations with quality engineers, process analysts, and Six Sigma professionals
  • TIBCO Data Science: Best for organizations that use ML algorithms

The Best 6 Predictive Analysis Tools

OpenOS is designed for marketing businesses to remain competitive with its no-code predictive analytics tools and help businesses maintain their marketing success. OpenOS uses a Market Mix Modeling (MMM) that analyzes historical data to help businesses set a specific marketing budget to maximize their Return on Investment (ROI). The MMM helps define Key Performance Indicators (KPIs) focusing on sales, revenue, and brand awareness.

Google Cloud BigQuery: Best for intense interactive ad-hoc queries

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Pros

  • Efficiently manages data across RDBMSes 
  • Automatically optimize to retrieve data quickly
  • Intuitive interface for editor and query builder
  • Comes with a free tier that includes a terabyte of queries and 10GB of data loading allocation each month

Cons

  • External tables have no cache option
  • Difficulty working with enterprise data models  
  • For new users, SQL and data warehousing are challenging to learn and use
  • Data storage cost can be high
  • Automatic software patching—patches automatically applied to a database
  • Automated backups—provide point-in-time data recovery
  • Automatic encryption—automatically encrypts data before it is written to a hard drive
  • Query performance—uses column storage, in-memory caching, and optimization methods for enhanced query performance
  • BigQuery Tools—capable of extracting data JSON, XML files, and multiple RDBMSes

BigQuery costs are divided between queries, storage options, and extra features.

  • Offers free and trial versions
  • BigQuery costs $2,000 per month, and the pricing model is a flat rate

Google Cloud BigQuery is a comprehensive managed service that relieves businesses of computing, networking, storage maintenance, and operational issues. BigQuery is optimal for running complex analytical queries on large relational databases (RDBMS), which can execute terabytes of data in seconds and petabytes of data in minutes. This serverless predictive analysis software solution allows you to run queries without the worrisome need to manage the infrastructure.

Any organization manipulating large datasets using RDBMS tables needs to consider using BigQuery.  BigQuery automatically replicates data across multiple locations, ensuring high availability. Businesses using BigQuery can scale seamlessly to store and analyze petabytes of data without purchasing more storage capacity. The advantages of BigQuery are the pay-as-you-go model and the ability to focus on analyzing terabytes of data to derive meaningful insights without the need for a database administrator.

Alteryx: Best for Self-service predictive analytics across an entire organization

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Pros

  • Easy to use interface
  • Capable of creating complex workflows
  • Scales easily
  • Ability to quickly analyze data
  • Report building
  • Excellent customer service

Cons

  • Licenses are expensive
  • At times, slow and clunky
  • Multiple users can make Alteryx unstable
  • Lacking data visualization options
  • Automated Machine Learning (ML)—automatically create predictive models and provide explanations
  • Code-free interface—allows non-programmers to use Alteryx effectively
  • Pre-built predictive models—pre-built models help non-programmers use data scientist’s techniques
  • Data transformation—converts source data to a specified format
  • Workflow data—generates output messages, warnings, and errors to any data source
  • Drag-and-drop functionality—facilitates an easier learning process for Alteryx’s users
  • Free trial version
  • $2,300 per user annually
  • $5,195 Designer, per user annually

Alteryx helps companies build reliable data processes that are efficient, easy to recreate, and minimize errors. Alteryx can prepare, clean, test, analyze, and blend data more easily than tools like Excel, SQL, and other similar tools. Alteryx can access multiple data sources, such as Extensible Markup Language (XML), Microsoft Access, and MapInfo. Alteryx is faster than PowerBI at downloading data from databases and is better at data preparation than a popular data visualization tool like Tableau.

Alteryx is a data analytics platform that focuses on executing complex data processing tasks with very little or no programming skills. Alteryx does not require business personnel to possess business intelligence expertise. Alteryx Server is a scalable server-based analytics solution that allows you to schedule and automate workflows, manage and share data connections, and share analytical applications.

IBM SPSS: Best for businesses with personnel with multiple skill levels

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Pros

  • Efficiently runs complex statistical analysis
  • User-friendly interface
  • Handles large amounts of data
  • Display data tables easily and quickly

Cons

  • Very expensive
  • Has a steep learning curve
  • Difficult to use with no training
  • Has limited functionality
  • Custom tables—capable of summarizing large data sets easily
  • Data documentation—stores metadata dictionary with the data file
  • Modeler program—allows personnel to build and validate predictive models using advanced statistics
  • Text Analysis—draw insights from qualitative inputs via open-ended questions
  • Visualization Designer—allows personnel to use their data for visual representations

Fourteen-day free trial version

Base subscription $99 a user/ per month

IBM SPSS Base v26 $1,290 annually

IBM SPSS Statistics Standard v26 $2,850 annually

IBM SPSS Statistics Professional v26 $5,730 annually

Businesses requiring advanced statistical analysis tools should consider this predictive analytics software solution. IBM SPSS can conduct multiple types of data analysis and interpretations on research projects or business reports. IBM SPSS uses machine learning (ML) algorithms, text analysis, and open-source extensibility, meaning the software can expand its capabilities and functionality without significant changes to the code.

IBM SPSS Statistics is a powerful solution that can meet any statistical needs of any business industry. This predictive analysis product is used in education, healthcare, government, retail, and market industries. One of IBM SPSS’s strengths is retrieving data from a Server Intelligent Storage (SIS), an enterprise storage process, or a data warehouse in real-time situations, such as a live conversation when decisions are made at the moment.

Minitab: Best for organizations with quality engineers, process analysts, and Six Sigma professionals

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Pros

  • No special training required
  • User-friendly interface
  • Easy to create tables and graphs

Cons

  • Minitab is costly
  • Licensing is difficult
  • Limited coding capabilities
  • Basic statistics—Minitab provides correlation analysis, regression analysis, and descriptive statistics
  • Analysis of Variance (ANOVA)—analyzes how far numbers are spread out from the mean value
  • Statistical process control—controls the quality of the data and prevents errors and mishaps
  • Graphic tools—allow users to easily insert their data in graphical forms like scatterplots, box plots, histograms, and many other graphs.
  • Design of Experimentations—allows users to check the interrelation between different sets of variables and how the variables affect the final outcome.

Minitab free version is limited to thirty days

Student licenses are available that range between $30 and $100

Single-named user $1,780 annual subscription

Minitab offers industry-specific business solutions with different price options

Minitab is a statistical and data analytics software for businesses and is most effective when used on continuous improvement, quality improvement projects, and data science. Minitab’s tools are designed to focus on quality analytics and are ideal for manufacturing and industrial companies. Minitab enables businesses to predict better outcomes, design better products, and enhance business processes that will generate higher revenues.

Minitab is used by businesses to help them understand their data and make data-driven decisions. Minitab is user-friendly and doesn’t require advanced statistical knowledge to use it effectively. Minitab uncovers trends and hidden relationships between data variables. Minitab removes the difficulty of a user doing their statistical calculations and provides simple methods for inputting statistical data and manipulating data to extrapolate answers.

TIBCO Data Science: Best for organizations that use ML algorithms

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Pros

  • Detailed documentation available
  • TIBCO Spotfire visual and analytic tool
  • Multiple data source access

Cons

  • Difficult installation process
  • Expensive software
  • TIBCO Spotfire—TIBCO’s data analysis platform 
  • Event processing—helps companies respond to critical real-time events
  • Integration—allows businesses to interface with a variety of systems, applications, and data sources
  • Customer support—provides training, customer support, and documentation
  • Role-based security—the ability to assign a role to any asset in the system
  • Built-in version control—manages changes to software and includes audit logs with an approval process

Offers a 30-day free trial

TIBCO Spotfire Desktop $650 annually

TIBCO Spotfire Cloud $200 per month or $2,000 per year

TIBCO Spotfire Platform requires a price quote

TIBCO data analysis platform helps businesses create unique solutions using ML and open-source development tools. This predictive analysis tool supports no-code and coding algorithms. Data scientists and laypersons can create ML pipelines using a point-and-click user interface or code. TIBCO helps reduce the delay between an event and an action needed by using an automated decision-making process.

TIBCO data analysis software solution uses ML tools to help make faster data-driven decisions by seamlessly connecting to data sources or applications. With TIBCO’s ability to deliver real-time data instantly, businesses can get instant access to cybercrime detection data, quicker insight into errors, and improvements in customer service by automatically identifying customers waiting for a predetermined time. Real-time data improves operational efficiency by identifying and ensuring managers are appropriately informed when something is amiss or incorrect.

The Purpose of Predictive Analytics Software  

Manufacturing, healthcare, banking, retail, utilities, governments, and many other business industries use predictive analysis software to improve an industry business function, such as streamlining business operations, boosting revenues, preventing fraud, identifying high-risk patients for chronic diseases, and predicting future outcomes. The purpose of predictive analysis software is to help organizations make better decisions, minimize risks, or improve a specific business function that enhances overall business performance.

The Benefits of Using Predictive Analytics Tools

A primary feature of predictive analytic tools is predicting future outcomes using accurate historical data. Additionally, predictive analytical tools benefit businesses at organizations’ micro and macro levels. The benefits of predictive analytics software typically fall into these four categories:

  • Decision Making – Using accurate historical data, a business can improve a function by analyzing historical data to determine potential outcomes.
  • Risk Reduction – Formulates a risk management strategy by prioritizing a remedy that will minimize the highest risk potential to an organization.
  • Customer insight – Businesses can develop marketing campaigns to reach specific customers by analyzing customer feedback, social media data, and purchasing trends.
  • Operational Efficiency – Reviewing historical data to understand and manage resources better, predictive analytical tools can make businesses operate more efficiently.

Key Features of Predictive Analytics Software

The availability of different predictive analytical software on the market can make the decision difficult when it comes to selecting a predictive analytical software solution. To maximize the potential uses of this type of software, you want the following key features included in a predictive analysis software solution:  

  • Statistical Techniques
  • Machine Learning
  • Predictive Modeling
  • Data Mining
  • Neural Networks

To use the full potential of predictive analysis software, the software must be able to execute the four pillars of predictive analytics:

Descriptive: Analyzes past events to determine what happened.

Diagnostic: Analyze an event to determine why this happened.

Predictive: Uses AI technology and statistics to predict the most likely outcome.

Prescriptive: Uses predictive analysis and descriptive or diagnostic results to make a data-driven decision with an anticipated outcome based on collective analysis.

A good predictive analytics software solution must be able to execute the following actions:

  • Analyze structured and unstructured data
  • Generate datasets and data visualization charts from combined data
  • Generate and use predictive models to forecast probabilities
  • Import and export data from other data-collecting techniques

Types of Predictive Analytical Models and How They Work

Predictive Analysis models work by assessing historical data, discovering patterns in data, and observing trends that are not apparent to a human. The types of predictive analytics models are:

Regression models: Evaluate changes in the dependent variable (a number) to see if the independent variable(s) influenced any changes in the dependent variable. An example is the relationship between height and weight.

Classification models:  Uses ML to categorize data into one or more groups based on the input variable. For example, an administrator uses this model to classify email as spam by using a decision tree.

Time series models: Uses historical trends and patterns in time series data to predict future outcomes. Examples are weather patterns or stock prices.  

Clustering models: Categorizes data into a certain number of clusters that help you identify similarities. Cluster models use ML techniques to group like items, and marketers can use this model to group similar customers for advertisement purposes.

Neural network models:  Mimics how the human brain works using interconnect nodes that work together to solve complex problems independently. The types of learning in neural networks are supervised, unsupervised, and reinforcement. Facial recognition and recognizing handwriting are examples.

Decision trees: Uses supervised learning for classification or regression models on non-parametric data, which is data that does not fit a known distribution.  Non-parametric data can be ordinal or interval data. An example of ordinal data is a customer survey using a scale of 1 to 5, and interval data can be credit scores (300 to 850). 

Ensemble models: Consists of multiple predictive models created to improve the accuracy and performance of predicted outcomes. Using multiple models helps make better predictions than any one contributing model.

Challenges of Using Predictive Analytics Software

Predictive analysis is helpful in many ways for businesses, but it comes with challenges. Intelligent, data-driven, and accurate decisions can only be made if the data is high-quality. Companies that lack access to the necessary data or cannot validate the historical data is accurate can potentially still make bad decisions.

Businesses may have difficulty interpreting results from large amounts of data and turning the insights into actionable strategies. Businesses lacking the skills and expertise to interpret the results can still be susceptible to not making the best decision. Investing in a data quality process, securing the data, and developing the necessary skills and expertise in-house to make informed decisions will minimize these challenges.

How to Select Predictive Analytics Tools

Industry-specific businesses have predictive analytics software solutions available on the market, so each decision-maker needs to determine their business type. After identifying your specific business industry, decision-makers must verify that a predictive analysis software solution includes the key features and functions correctly.

Hig-quality predictive analytics tools should also be able to execute the four pillars of predictive analysis software. Using a 30-day trial version or communicating with a company that uses a solution you are interested in purchasing is an excellent way to test and validate that the product will meet your business needs.

Looking for the latest in Data Analysis solutions? Check out our Data Analysis Software Buyer’s Guide

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FAQ

Predictive analytics tools analyze historical and current data to anticipate future events. They employ methods like regression, decision trees, and neural networks for their predictions. Over time, these tools have transitioned into what we now know as data science and machine learning instruments. They cater to various analytical needs, from descriptive to prescriptive. Modern versions of these tools are more user-centric, offering automated features for both specialists and everyday users.

Predictive analytics tools offer numerous benefits for businesses. They enable data-driven decision-making, helping companies anticipate future trends and events. Key advantages include:

  1. Enhanced decision-making by predicting potential outcomes.
  2. Effective risk management by identifying and prioritizing potential risks.
  3. Gaining deeper customer insights for targeted marketing campaigns.
  4. Boosting operational efficiency by analyzing historical data to optimize resource management.
Don Hall Avatar
Don Hall
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Don Hall is a contributing writer to Technology Advice and Enterprise Storage Forum, where he covers data storage technology, storage hardware and software, and data networking. He worked for more than two decades as an IT Supervisor for the federal government and as IT Operations Supervisor for an IT Military Command managing programmers, cybersecurity staff, and infrastructure and networking personnel. Previously he worked as an application programmer. Don earned a B.S. in Business Information Systems from San Diego State University and has certificates in Technical Communication and web development with an emphasis in Java/Open Source. He has also had an active CompTIA Security + (ce) since 2011, and a Network +(ce) since 2015.