Top 7 Predictive Analytics Tools

Predictive analytics is a data technology for harnessing company data, detecting patterns, and helping businesses prepare for possible events. Businesses use dedicated software, including business intelligence and advanced analytics platforms, to visualize predictions.

These days nearly every enterprise wants to have predictive analytics capabilities to better understand their future possibilities. This enterprise expectation corresponds with a growing interest in Big Data and artificial intelligence solutions – both of which support predictive analytics. 

For more information, also see: Data Management Platforms

Table of Contents

• Top 7 Predictive Analytics Tools
  
• IBM SPSS Statistics: Best for Dashboard Capabilities
  
• SAS Advanced Analytics: Best for Variety
  
• SAP Predictive Analytics: Best for ERP Data
  
• TIBCO Data Science/Statistica: Best for Collaboration
• Oracle Data Science: Best for Cloud Management
  
• Q Research: Best for Market Research
  
• H20: Best for Data Science Professionals
  
• Predictive Analytics Key Features
  
• How to Choose a Predictive Analytics Tool
  
• Should You Use Predictive Analytics?
  
• Bottom Line: Predictive Analytics Tools
  

Predictive Analytics: Vendor Comparison Chart

Top 7 Predictive Analytics Tools

Here is a list of eight predictive analytics software solutions worth considering as the company begins their selection process:

For more information, also see: Top Data Warehouse Tools

IBM SPSS Statistics: Best For Dashboard Capabilities

IBM SPSS Statistics is a popular predictive analytics tool. It offers a user-friendly interface and a strong set of features including the SPSS modeler, which provides advanced statistical procedures, helps ensure precision, and provides positive decision-making. All of the analytics lifecycle features are included, such as data preparation and management to analysis and reporting.

Pricing:

IBM provides multiple payment options, including a subscription plan, term licenses, and academic plans. IBM also offers a free trial to allow users to try it before they buy it.

Features:

• Linear Elastic Net Regression: Estimates linear regression models for dependent variables with one or more independent variables.
• Custom Tables: Gives customization to customers’ data sets.
• Bootstrapping: Approximation of sampling distributions by an estimator by resampling the original data set.

Pros:

• Easy statistical analysis of large data for beginners.
• Improves efficiency with coding.
• Interactive dashboard.

Cons: 

• Expensive software.

SAS Advanced Analytics: Best For Variety 

SAS is a leader in various analytics markets and offers an incredibly long list of different predictive analytics and other advanced analytics products. That list is so long that it might be difficult to figure out which tool(s) a company needs for their historical data management purposes. With so many different tools available, chances are good that SAS has exactly what a company needs.

Pricing:

SAS Advanced Analytics does not have pricing on their website. They have contact sheets in case a company needs to request demos, free trials, and price quotes.

Features:

• Quality-tested algorithms: Updates to reflect the latest statistical methodologies to analyze the past, present, and predict the future.
• Diverse areas with one environment: Provides analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis.
• Customization: Create task-specific graphics for effective interpretation and communication of results.

Pros:

• Great for data exploration.
• Processes large data sets.
• Develop ML algorithms.

Cons: 

• Difficult learning process.

SAP Predictive Analytics: Best For ERP Data

If a company will primarily be using their predictive analytics solution to analyze data that resides in SAP software or the SAP Analytics Cloud, such as their ERP data, SAP Predictive Analytics might be a good fit. The company has quite a few different options available when it comes to features. Whoever is using the system from business analysts to data scientists, the tool allows a company to create, operationalize, and monitor the predictive models. It has advanced machine learning and security features for its predictive models.

Pricing:

SAP Predictive Analytics does not have exact pricing for the product. SAP has a store for their products, and customers can call sales for more information.

Features:

• Rapid insight: Creates insights with 1,000’s and 10,000’s variables with no expert intervention.
• Discover hidden insights: Build predictive models and discover hidden relationships in the data, which can help make predictions about future events.
• Automated analytics: Allowing data scientists to focus on the work they need without the tedious tasks.

Pros:

• Great integration with SAP Lumira.
• Convenient diagram.
• Different databases can be connected.

Cons: 

• Complicated for novices.

TIBCO Data Science/Statistica: Best For Collaboration

TIBCO Data Science/Statistica puts the emphasis on usability, with a lot of collaboration and workflow features built into the tool to make business intelligence possible across an organization. This makes it a good choice for a company if they expect lesser-trained staff will use the tool. It also integrates with a wide range of other analytics tools, making it easy to extend its capabilities. This is also the only tool on the list that emphasizes its IoT/embedded capabilities.

Pricing:

Like many analytics tools, TIBCO Data Science/Statistica does not have prices listed online. They do have a free trial page, and for more information, a company can contact sales.

Features:

• Faster insights: The tool can easily go through large amounts of data to find and access the information needed to quickly pull the most relevant insights.
• Streamlined data access: A company can access all of their data from an environment to simplify complex data sources and prepare them for analytics.
• Faster data prep: Prep and combine data faster using enhanced joins and unions, and gain performance improvements for data sources.

Pros:

• Provides reliable results.
• Quick at calculating.
• The point-and-click process for neural networks is helpful.

Cons: 

• Default analytic models are difficult.

Oracle Cloud Infrastructure (OCI) Data Science: Best For Cloud Management

Oracle Cloud Infrastructure (OCI) Data Science is a cloud-based big data and machine learning platform that includes predictive analytics capabilities. OCI Data Science is an analytics platform for teams of data scientists to build, train, deploy, and manage machine learning models using Python, and open source tools. It takes models into production and keeps them healthy with MLOps capabilities, model deployments, and model monitoring.

Pricing:

Oracle provides a pricing page for their analytics tool. The tool also has a free lab option for potential customers.

Features:

• Flexible data access: Data scientists can access and use any data source in any cloud or on-premises. This provides more potential data features that lead to better models.
• Data labeling: With data labeling, data scientists can assemble data, create and browse data sets, and apply labels.
• Model explanation: Automated model explanation helps data scientists understand the behavior of the model and provides guides on what caused the model’s predictions.

Pros:

• Works quickly.
• Built-in APIs.
• Wide range of solutions.

Cons: 

• Setup can be complicated.

For more on ML: Key Machine Learning (ML) Trends

Q Research: Best For Market Research

Q Research focuses on one sector: market research. If a company needs a predictive analytics solution only for market research and marketing analytics, this application has all the capabilities a business could ever want. This highly automated platform streamlines the predictive analytics process so that users can spend less time running the tool and more time strategizing for the next marketing campaign.

Pricing:

Q Research has a pricing page that offers a standard license and a transferable license. Q Research also offers a free trial and the ability to book a demo.

Features:

• Automates work: Everything that can be automated is automated, from formatting data, statistical testing, and generating tables, to updating analyses and reproducing reports.
• DIY advanced analysis and visualization: Automates all the steps that can take people years of training to learn.
• Create reports to share insight-filled stories: Visualize information to show patterns that matter and design reports with integration.

Pros:

• Good prices.
• Ease-of-Use.
• Ability to expand skill sets.

Cons: 

• Updates occasionally can interfere.

H2O: Best For Data Science Professionals

If a company is interested in an open-source predictive analytics tool with data mining features, put H2O at the top of the list. It offers fast performance, affordability, advanced capabilities, and extreme flexibility. The dashboard for H2O offers a veritable smorgasbord of actionable insights. However, this tool is more for the expert data science crowd than for citizen data scientists. 

Pricing:

Open source versions are free. H2O also gives customers the opportunity to request a demo.

Features:

• Automatic scoring pipelines: H2O’s AI provides scoring pipelines that can be deployed to production for interpreted models.
• Automatic feature engineering: The tool aims to help with the problem of feature creation by automatically building new features from a data set.
• The flexibility of data and deployment: H2O’s analytics tool gives flexibility on how and where the company’s applications run.

Pros:

• Great analytical and prediction tool.
• Open source tool.
• Well documented for training.

Cons: 

• Needs containerization facilities.

For more on data mining: What Is Data Mining? Types & Examples

Predictive Analytics Features

Predictive analytics enables companies to actively monitor their data systems, detecting trends for decision-making. Organizations use predictive analytics to get forecasts and provide additional insight. 

Here are some predictive analytics features:

• Reduce cyberattack risk: Predictive analytics can detect unusual activity to reduce reaction time and any cybersecurity breaches.
• Customer segmentation: By organizing a customer base into groups, predictive analytics can make decisions based on customer groups and industry. 
• Organizational improvement: Companies can use predictive analytics models to keep track of their inventory, manage their resources, and operate efficiently.
• Automates work: Everything that can be automated is automated, from formatting data, statistical testing, and generating tables, to updating analyses and reproducing reports.

How To Choose A Predictive Analytics Tool

How does a company choose predictive analytics tools with predictive models that fit their needs? Experts suggest that organizations begin the selection process by defining their exact data analytics and data source needs. Asking the following questions may help:

Who within the organization will be using the predictive analytics tools? 

Will the company have dedicated data scientists who will be running the software and interpreting the predictive analytics model? Or does the company need a solution designed for regular business users and “citizen data scientists” to understand the predictive models? 

The company may also require a tool that can meet the needs of both groups for a well-rounded business intelligence approach. Some tools offer automation and artificial intelligence or machine learning support that simplifies analytics for all kinds of users.

What will be the initial use case for your predictive analytics? 

Is the company planning to use predictive analytics for market research? Fraud detection? Supply forecasting? Machine learning? Predictive maintenance? Data mining? Depending on the company’s industry and their immediate needs, some use cases will make more sense for some companies rather than others. 

The company may also need to determine if classification or regression models are a better strategic fit for the organization. Defining which use case needs to be solved most immediately will determine what features and capabilities the company needs in a predictive analytics software platform.

How will the company’s predictive analytics needs change over time? 

Many organizations start with a predictive data analytics use case that can pay off in a short amount of time, but they hope to expand their use of predictive analytics to other business goals over time. Make sure the company can choose a tool that can meet their current and future outcome expectations.

What other tools does your predictive analytics solution need to support or integrate with? 

If the company will be pulling historical data from a CRM or ERP into their chosen analytics platform, it might make sense to choose an analytics solution designed to support the existing software. Or if the company is already using open source big data software like Spark, the company might need a different tool.

What is your preferred deployment model? 

If the company’s data or other applications leverage cloud computing, they might prefer a cloud-based solution for their predictive analytics techniques. But if most of the company’s data is on-premises, it might make more sense to choose a tool they can deploy on their own servers.

What is the company budget? 

Some vendors offer upfront pricing on predictive analytics tools, but most will make the company speak with someone on the sales team in order to get a quote. The company will likely need to do quite a bit of legwork to figure out the actual cost.

For more information, also see: What is Big Data Analysis

Should You Use Predictive Analytics?

Many companies say they believe predictive analytics will be important to their future data analysis success. However, fewer companies have put predictive analytics software into production or fully integrated the software into their business models.

Part of the problem may be the difficulties involved in choosing the right technology for an organization’s needs. Rushing to make a purchase in order to keep up with the data analytics trends is a sure way to end up with an expensive tool that disappoints expectations.

After going through the questions that help organizations begin the selection process, they can define their exact data analytics and data source needs.

Bottom Line: Predictive Analytics Tools

Predictive analytics is valuable to every company. Predictive models are used for reducing cyberattack risks, managing resources, organizing data, managing equipment maintenance, and finding the best solutions possible for a company. 

Many tools can be helpful for predictive analytics, but a company must research and consider many variables to know the best choice for their needs.

For more information, also see: The Data Analytics Job Market