The 8 Best Predictive Analytics Tools On the Market Today

Akshat Biyani, CareerFoundry Contributor

At its core, data analytics involves methodical processes that work toward deriving actionable insights from raw data. There are four levels of analytics, each answering a different question about any set of data that’s been collected. 

‘What is happening?’ is answered by descriptive analytics.

‘Why is it occurring?’ is answered by diagnostic analytics.

‘What is likely to happen?‘ is answered by predictive analytics.

‘What is the best course of action?‘ by prescriptive analytics.

Learn more: What Are the Different Types of Data Analysis?

In this article, we will focus on predictive analytics and the tools that data analysts use to generate insights and answer the question: “What is likely to happen?” First, we will explain what predictive analytics is, then we’ll introduce you to some of the best predictive analytics tools available on the market right now, listing their pros, cons, and other features of each product.

If you’d like, you can use the clickable menu to skip to a section you are interested in.

  1. What is predictive analytics?
  2. What are the best predictive analytics tools?
  3. Key takeaways

1. What is predictive analytics?

Predictive analytics focuses on the insights derived from statistical modeling and machine learning techniques to identify the likelihood of future outcomes based on present and historical data. Moreover, it determines which—if any—patterns are likely to re-emerge and allows businesses to make more informed and data-driven decisions about the deployment of their resources. Predictive analytics, therefore, has widespread applications in innumerable industries. It allows for the opportunity to maximize profits by taking advantage of probable upcoming opportunities, and the chance to minimize losses by disinvesting in scenarios that predictive analytics deem risky. 

There are numerous predictive analytics tools in the market, and sometimes choosing the right one can appear daunting. Despite having a common purpose, these tools vary across several factors including function, price, usage, and number of users. To make your life a little easier, this post explores and evaluates some of the best predictive analytics tools in the market so that you can find the perfect fit for your organization’s needs! 

2. What are the best predictive analytics tools?

SAP Analytics Cloud

Commonly used by: Commercial businesses

Pros:  Hassle-free collaboration, no software installation needed, reduced data modeling work, great visualizations

Cons: Poor mobile application interface, latency, needs a stable internet connection for big data uploads

SAP Analytics Cloud is focused on business intelligence, predictive analysis, and planning. A large number of features such as financial planning, predictive forecasting, and data exploration and visualization are possible with the help of this software. 

SAP Analytics Cloud also allows large datasets from multiple sources to be consolidated and imported into the cloud. Another useful attribute of this tool is the ability to customize data access based on each user’s level of authorization. It is a well-rounded predictive analytics tool and is the obvious choice if you work within the SAP ecosystem.

 After a 30-day free trial, the price for business intelligence starts at $22 per user, per month.

IBM SPSS

Commonly used by: Researchers, SMBs, commercial businesses, and governments

Pros:  Very easy to use, includes a broad set of statistical instruments, users don’t need prior programming knowledge, great for handling large amounts of data

Cons: Most features are only accessible to paying users, the interface is now outdated, and the software is occasionally slow

SPSS is a predictive analytics platform that helps users with advanced statistical analysis. It was initially used to conduct research for the social sciences, since it is relatively easy to use. SPSS allows for the access to—and management of—large volumes of data through a variety of simplified tools distributed between its two modules: SPSS Statistics and SPSS Modeller.

SPSS Statistics offers an exhaustive list of tools and techniques such as ad hoc analysis, hypothesis testing, and geospatial analysis to research and solve complex business problems. Meanwhile, SPSS Modeler is much broader; it is used for data mining and text analysis, and accordingly models the data to gain insights and predict the future.

SPSS Statistics starts at $99 per month, per user; SPSS Modeler starts at $4,950 per user, per year.

Alteryx

Commonly used by: Commercial businesses

Pros:  Easy drag and drop GUI, great for combining data from different sources and handling large datasets

Cons: Price, requires professional training, needs better visualizations 

Alteryx is an end-to-end self-service platform that offers a variety of different products for varying business needs. Its solutions are classified by industry, department, role, ecosystem, etc. Alteryx also has an intuitive drag-and-drop UI, which makes it extremely user-friendly. 

Using Alteryx, it is also possible to combine disparate data sources and effortlessly process a large amount of data since it can automate every step in the data analytics process, from data preparation to data science. Furthermore, it can be integrated with R and other statistical packages for maximum efficiency and usage. This software makes data science accessible to everyone, which can be a blessing to small and mid-size companies that don’t have the resources to hire many data science engineers. 

Alteryx’s pricing begins at $5,195 per year, per production license.  

H2O

Commonly used by: Commercial businesses, single business users, SMBs, and startups

Pros: Great for automation, allows for seamless integration and deployment in applications, excellent customer service

Cons: Needs more documentation and better visualizations

H2O is a widely-used open-source machine learning platform that is both fast and scalable; it is one of the best predictive analytics tools available in the market. H2O.ai is a leading AI cloud company, which is on a mission to democratize AI for everyone. H2O uses familiar interfaces, and it has readily available algorithms for analytical projects that are relatively simple to use, making it an excellent analytical and prediction tool. H2O also works seamlessly with big data technologies. 

Companies like AT&T, Unilever, Walgreens, Goldman Sachs, NVIDIA, and Wells Fargo use the H2O AI Hybrid Cloud platform to discover new ideas and gain new insights. Additionally, various other products by H2O—such as H2O Driverless, Sparkling Water, and Wave—are used by numerous Fortune 500 companies. 

There’s a 14-day free trial for certain H2O products, and the price varies based on a company’s specific needs.

RapidMiner Studio

Commonly used by: Data scientists, businesses, and startups

Pros: Fast at reading databases, great work-flow visualization, offers enormous flexibility, active online community that can help solve problems

Cons: Speed issues with large databases, data cleaning, old-fashioned graphs, and fewer statistical methods

RapidMiner is an open-core visual workflow designer software that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It allows users to perform all kinds of transformations, calculations, and filters with or without coding. 

For beginners, RapidMiner’s convenient drag-and-drop interface, hands-on tutorials, sample processes, and proactive recommendations provided by “Wisdom of Crowds” make it one of the best tools for data analytics. It also has a diverse and comprehensive library of 1,500+ algorithms and functions, making it significantly easier for users to build the best models for their specific requirements. 

RapidMiner is free for educational use. The paid versions of the software range from $120-$15,000 per year.

KNIME Analytics Platform 

Commonly used by: SMBs, startups, single business users, and free users

Pros: Allows for easy integration with many other open-source softwares, intuitive to learn, allows users to easily isolate and fix workflow steps

Cons: Cumbersome UI, slow to load, and has poor visualizations

KNIME is a free and open-source data analytics platform that integrates various machine learning and data mining components through its “Building Blocks of Analytics” concept. This platform is one of the easiest to use; it’s possible to use this platform to design and execute great workflows without knowing much about coding.

Workflow is visually presented as connected nodes, making it effortless to track, isolate, and troubleshoot different steps. Users can connect to multiple data sources and process large datasets with KNIME, as well as build machine learning models for classification, regression, dimension reduction, clustering, etc., using advanced algorithms. Furthermore, KNIME allows users to optimize and validate models with the help of visual charts and summaries. 

KNIME offers both free and paid versions. However, to run a large query you may have to purchase a server license. The pricing starts at $52,000 for a large server that facilitates five users and four cores for execution.

SAS Advanced Analytics

Commonly used by: SMBs, governments, and commercial businesses, particularly within the banking, retail, and healthcare sectors

Pros: Great visual dashboards, easy to execute and maintain, ability to process large datasets, complex survey analysis, large SAS Advanced analytics online support, easy-to-navigate drag and drop display, and advanced data preparation tools

Cons: Price, steep learning curve, the software loads slowly, and it needs better reporting capabilities

SAS offers plenty of analytical tools and data mining products, as it is one of the leading companies in this market. SAS Advanced Analytics products can perform a number of different tasks such as data mining, statistical analytics, forecasting, text analysis, optimization, simulation, etc. This software also includes a number of built-in models that don’t require coding. 

SAS Advanced Analytics has a lot of statistical options available, with a large number of critical analytical capabilities and a combination of available functions. Users can combine various modeling techniques, and SAS will produce robust reports on your created models.

SAS Advanced Analytics offers custom pricing depending on the project scope.

TIBCO Statistica

Commonly used by: One-person businesses, startups, enterprises

Pros: Great suggestions for modeling framework, seamless point and click process, extremely user-friendly 

Cons: Difficult to create custom graphics, needs NLP features, and speed can sometimes be an issue

TIBCO Statistica is a data science and machine learning platform that is paving the way towards making data science accessible to everyone with its easy click-and-go learning curve. Moreover, TIBCO Statistica prioritizes usability and collaboration; it allows multiple people to work on the same project and facilitates sharing of relevant data with a team. 

This software can handle and cleanse large datasets from multiple data sources; it has an extensive range of statistical methods and an easy-to-navigate interface that allows users to rapidly create statistical and machine learning models with built-in algorithms and functionalities. TIBCO Statistica is an excellent option for your business if ease-of-use and collaboration are high on your list! 

Prices can range between $535 to $6,880 per year for this software. 

Key takeaways

So, there you have it! In this post, we have evaluated the top eight predictive analytics tools to help you determine relevant potential future outcomes for your business. In order to choose the right tools for your organization, you need to think about which stage of the data journey your business is at and understand the goals of implementing these predictive analytics tools into your data analytics cohort.

To learn more about data analytics, check out this free, 5-day data analytics short course, or read the following posts for more introductory topics:

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