These days nearly every enterprise wants to have predictive analytics capabilities to better understand their historical data. This enterprise expectation corresponds with a growing interest in Big Data and artificial intelligence solutions.
In recent surveys, over 90 percent of organizations say they believe predictive analytics will be important to their future data analysis success. But far fewer respondents 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 your 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. Learn more about how to select the best predictive analytics software and some of the top players in the predictive analytics market here:
Also read: Data Analytics Market Review
How To Choose A Predictive Analytics Tool
How do you choose predictive analytics tools with predictive models that fit your 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 your organization will be using the predictive analytics tools? Will you have dedicated data scientists who will be running the software and interpreting the predictive analytics model? Or do you need a solution designed for regular business users and “citizen data scientists” to understand the predictive models? You 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? Are you planning to use predictive analytics for market research? Fraud detection? Supply forecasting? Machine learning? Predictive maintenance? Data mining? Depending on your industry and your immediate needs, some use cases will make more sense for you than others. You may also need to determine if classification or regression models are a better strategic fit for your organization. Defining which use case needs to be solved for most immediately will determine what features and capabilities you need in a predictive analytics software platform.
- How will your 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 you choose a tool that can meet your current and future outcome expectations.
- What other tools does your predictive analytics solution need to support or integrate with? If you’ll be pulling historical data from a CRM or ERP into your chosen analytics platform, it might make sense to choose an analytics solution designed to support your existing software. Or if you’re already using open source big data software like Hadoop or Spark, you might need a different tool.
- What is your preferred deployment model? If your data or other applications leverage cloud computing, you might prefer a cloud-based solution for your predictive analytics techniques. But if most of your data is on-premises, it might make more sense to choose a tool you can deploy on your own servers.
- What is your budget? Some vendors offer upfront pricing on predictive analytics tools, but most will make you speak with someone on the sales team in order to get a quote. You will likely need to do quite a bit of legwork to figure out the actual cost.
Also read: The Pros and Cons of Edge Computing
Top 8 Predictive Analytics Tools
Because every organization has a different answer to the questions above, there is no one best predictive analytics tool. Here is a list of eight predictive analytics software solutions worth considering as you begin your selection process:
IBM SPSS Statistics
You really can’t go wrong with IBM’s predictive analytics tool. It’s been around for a long time and offers a robust list of capabilities, including the SPSS modeler. Another plus is that IBM offers easy-to-understand pricing. However, while its user interface has gotten a recent facelift, it still may be too complicated for most business users who don’t have a lot of experience in analytics and data science.
Explore our in-depth look at IBM predictive analytics solutions
SAS Advanced Analytics
SAS is the leader in various analytics markets and offers an incredibly long list of different predictive analytics and other advanced analytics products. In fact, that list is so long that it might be difficult to figure out which tool(s) you need for your historical data management purposes. The company also doesn’t provide upfront pricing, making it tougher to compare with other solutions directly. Still, with so many different tools available, chances are good that SAS has exactly what you need.
Explore our in-depth look at SAS for predictive analytics
SAP Predictive Analytics
If you will primarily be using your predictive analytics solution to analyze data that resides in SAP software or the SAP Analytics Cloud, such as your ERP data, the SAP solution might be a good fit for you. The company has quite a few different options available when it comes to features, but like SAS and several others, it does not disclose pricing. It also does not have a public cloud deployment option. On the positive side, it does have advanced machine learning and security features for its predictive models.
Explore our in-depth look at SAP solutions for predictive analytics
TIBCO Statistica
TIBCO 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 your company if you 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.
Explore our in-depth look at TIBCO for predictive analytics
H2O
If you are interested in an open-source predictive analytics tool with data mining features, put H2O at the top of your 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. If you’ve invested in trained staff, this could be your tool.
Explore our in-depth look at H2O
Oracle DataScience
Oracle got into the predictive analytics market when they purchased a highly regarded startup called DataScience, and they have since expanded and matured their portfolio. This solution is most useful for organizations using Oracle’s database and cloud services.
Explore our in-depth look at Oracle DataScience
Q Research
Q Research focuses on one market: market research. If you need a predictive analytics solution only for market research and marketing analytics, this application has all the capabilities you 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. On the downside, it doesn’t have the capabilities to do other types of predictive analytics.
Not looking for a marketing-only solution? Qlik Sense might be a better platform for you.
WebFOCUS by TIBCO 
WebFOCUS was previously owned by Information Builders before it was acquired by TIBCO. TIBCO offers a complete suite of BI analytics, and data management tools that include predictive analytics capabilities. If an end-to-end data solution is on your shopping list, this might be a good option. Also, it offers tools for both the expert data scientist and business users. It’s an appropriate all-around option for a company whose staff has various levels of data expertise. Like many of the others on the list, however, pricing is available only upon request.
Explore our in-depth look at Information Builders WebFOCUS
Predictive Analytics Tool Honorable Mentions
In addition to our top picks, there are a number of other promising predictive analytics tools out there. Here are some others to consider:
RapidMiner Studio
RapidMiner is another solid tool to consider that has won accolades from analysts, including Gartner, which named it a leader in its Magic Quadrant for data science and machine learning platforms. Users tend to praise RapidMiner for its user interface, ease of use, and strong data mining strategies.
KNIME Analytics Platform
KNIME provides both an open-source as well as a commercially supported version of its Analytics Platform product. The KNIME offering is often seen as easy to use, while still providing advanced capabilities including machine learning (ML) automation. It also offers prescriptive analytics capabilities, which makes it a good tool for future business roadmap planning.
Dataiku
For those looking for a platform that has strong collaboration and artificial intelligence, the Dataiku Data Science Studio (DSS) is another solid option. The offering has had some production scalability challenges in the past which are being improved.
FICO Predictive Analytics
The FICO Predictive Analytics platform is a good choice for decision management-based capabilities, especially for organizations in the financial services sector.
Halo Software by Logility
Logility’s Halo Software offers a business intelligence platform that is positioned for supply chain and other business analytics use cases in the manufacturing and distribution market verticals.
Altair Datawatch Knowledge
Altair’s Datawatch offers Knowledge Studio to solve business problems and predict data outcomes, emphasizing an agile framework and strategy. Users frequently praise this tool for its intuitive interface.
Read next: Top Data Analytics Tools & Data Analysis Software
Predictive Analytics Comparison Table
| Editions Available | Key Features | Integrations | Delivery | Target Market | Price | |
| IBM SPSS Statistics | Base, Standard, Professional and Premium |
Data preparation Bootstrapping Advanced statistics Regression Custom tables Missing values Categories Complex samples Conjoint predictive analysis Exact tests Forecasting Decision tree Direct marketing Neural networks |
R, Python, Excel | Cloud or desktop | SMBs and enterprises, as well as government, education and healthcare organizations | $99 per user per month and up |
| SAS Advanced Analytics | SAS Enterprise Miner, SAS Factory Miner, SAS Machine Learning on SAS® Analytics Cloud and others |
Descriptive analytics and predictive modeling Open, code-based model development Dynamic group-by processing Model comparison, assessment and scoring Distributed, in-memory analytical processing Flexible deployment |
Other SAS tools, Python, R, Lua, Java, Teradata, SAP HANA | Public or private cloud or on-premises | Organizations of all sizes, particularly in banking, retail, government and health care | On request |
| SAP Predictive Analytics | Predictive Factory, Predictive Analytics Server for Windows and others |
Python API Data security and compliance features Automated data preparation Support for a data set with more than 10,000 columns Predictive modeling Machine learning model and algorithms Network and link predictive analysis Predictive model management Embedded predictive insights Native Spark modeling |
Appstam Advanced Graphics, DTree, Qualex iQ-Gaming Solution, PANA, Bosch SaPHAL, Clariba | On premise or private cloud | Enterprises | Available on request. Price depends on the number of users and selected functionality. |
| TIBCO Statistica | N/A |
Full-spectrum analytics Machine learning Team collaboration Drag-and-drop interface Centralized monitoring, management and deployment Security, governance and auditability Open, flexible and extensible |
Amazon SageMaker, Google TensorFlow, Microsoft Azure, Apervita, H2O, Algorithmia, Oracle, Teradata, R, Jupyter Notebooks, Python, C#, Scala, Kerberos, Ranger and Sentry | On-premise, cloud or edge computing/IoT | Enterprises | Available on request |
| H2O | H2O (open source), Sparkling Water (H2O + Spark), H2O Driverless AI (paid enterprise version) |
Automatic feature engineering Machine learning interpretability Natural language processing Automatic scoring pipelines Time series Automatic visualization Flexibility of data and deployment NVIDIA GPU acceleration |
Hadoop, Python, Spark, leading cloud services | Downloadable software that can be deployed anywhere. | Enterprises | Open source versions are free. Enterprise version has a free trial with pricing available on request. |
| Oracle Cloud Infrastructure Data Science | N/A |
Project-based user interface to enhance collaboration Self-driving machine learning Scalability Self-service Integrates with open source tools Enterprise-grad performance Version control Use case templates Access controls and security |
Python, Plotly, Matplotlib, Bokeh, TensorFlow, scikit-learn, other open source projects, other Oracle cloud services | Cloud | Enterprises | Unknown |
| Q Research | N/A |
Easy updating and automation Full R language support Drag-and-drop interface Statistical testing based on data type Predictive modeling 24-hour free support |
R, Microsoft Office, Qualtrics | Installed desktop software | Market researchers | $1,699 per license per year and up |
| Information Builders WEBFocus | N/A |
Intuitive graphic interface Portals and dashboards Enterprise reporting and predictive analysis Visual discovery insight mode Predictive analytics Location analytics Big data and IoT Smart BI and search |
Integrates with Information Builders’ other tools | Cloud, on-premise or hybrid | Enterprises | Available on request |
