In some recent surveys, more than 90 percent of organizations say they believe predictive analytics will be important to their future success. But far fewer respondents have put predictive analytics software into production. Only a quarter to a third or respondents are actually using the tools today (although considerably more are currently evaluating for future deployment).
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.
How to choose the predictive analytics tools for your needs? Experts suggest that organizations begin the process of selecting predictive analytics tools by clarifying their 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? Or do you need a solution designed for regular business users, sometimes called “citizen data scientists”? Or do you need a tool that can meet the needs of both groups?
- 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? Depending on your industry and your immediate needs, some use cases will make more sense for you than others. And that will determine what features and capabilities you need.
- How will your predictive analytics needs change over time? Many organizations start with a use case that can pay off in a short amount of time, but hope to expand their use of predictive analytics over time. Make sure you choose a tool that can meet your needs now and into the future.
- What other tools does your predictive analytics solution need to support or integrate with? If you’ll be pulling in CRM or ERP data, 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 and Spark, you might need a different tool.
- What is your preferred deployment model? If your data or other applications leverages cloud computing, you might prefer a cloud-based solution. 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 actual cost.
Because every organization will have different answers to these questions, there is no one “best” predictive analytics tool. Here are eight predictive analytics tools 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. 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.
SAS Advanced Analytics
SAS is the leader in the analytics markets and has an incredibly long list of different predictive analytics products available. In fact, that list is so long that it might be difficult to figure out which tool(s) you might need for your purposes. The company also doesn’t provide upfront pricing, making it tougher to comparison shop. Still, with so many different tools available, chances are good that SAS has exactly what you need.
SAP Predictive Analytics
If you will primarily be using your predictive analytics tool to analyze data that resides in SAP software, 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.
TIBCO puts the emphasis on usability, with a lot of collaboration and workflow features built into the tool. This makes it 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 – a new market, but one to keep in mind.
If you are interested in an open source predictive analytics tool, 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 data 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.
Get an in-depth look at H2O
Oracle recently got into the predictive analytics market by purchasing a highly regarded startup called DataScience. The company is still in the process of integrating the tool with its cloud platform, but it seems promising, as DataScience’s tool has received excellent user reviews and ratings. It most likely will be especially useful for organizations using Oracle’s database and cloud services.
Q Research focuses on one market: If you need a predictive analytics tool only for market research, 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. On the downside, it doesn’t have the capabilities to do other types of predictive analytics.
Get an in-depth look at Q Research
Information Builders WEBFocus
Information Builders offers a complete suite of BI analytics, and data management tools that includes 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 data scientists 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 on request.
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 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 and and ease-of-use.
See user reviews of RapidMiner Studio
KNIME Analytics Platform
KNIME provides both an open source as well as commercially supported version of its Analytics Platform. The KNIME offering is often seen as being easy to use, while still providing advanced capabilities including machine learning (ML) automation.
See user reviews of KNIME Analytics Platform
For those looking for a platform that has strong collaboration capabilities 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.
See user reviews of Dataiku DSS
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.
Microsoft R Open/Cortana Intelligence Suite
The Azure cloud based Cortana Intelligence Suite integrates tutorials and templates to help get new users up to speed with the platform, it’s often overshadowed as a tool by Microsoft’s popular Power BI platform.
See user reviews of Microsoft R Open/Cortana Intelligence Suite
Halo Software’s business intelligence platform is positioned for supply chain use cases in the manufacturing and distribution market verticals.
Datawatch (Angoss) KnowledgeSTUDIO/KnowledgeENTEPRISE
Angoss Software Corporation was acquired by Datawatch in January 2018, bringing along with it, the KnowledgeSTUDIO/KnowledgeSEEKER/KnowledgeENTEPRISE data science tools. Enterprise is the flagship product which users praise for its intuitive interface.
See user reviews of Datawatch (Angoss) KnowledgeSTUDIO/KnowledgeENTERPRISE
Predictive Analytics Comparison Table
|Editions Available||Key Features||Integrations||Delivery||Target Market||Price|
|IBM SPSS Statistics||Base, Standard, Professional and Premium||Data preparation
|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 Minder, SAS Factory Miner, SAS Machine Learning on SAS® Analytics Cloud and others||Descriptive and predictive modeling
Open, code-based model development
Dynamic group-by processing
Model comparison, assessment and scoring
Distributed, in-memory analytical processing
|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 datasets with more than 10,000 columns
Machine learning algorithms
Network and link 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 number of users and selected functionality.|
|TIBCO Statistica||N/A||Full-spectrum analytics
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
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 DataScience||N/A||Project-based user interface to enhance collaboration
Self-driving machine learning
Integrates with open source tools
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
Statistical testing based on data type
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 analysis
Visual discovery insight mode
Big data and IoT
Smart BI and search
|Integrates with Information Builders’ other tools||Cloud, on-premise or hybrid||Enterprises||Available on request|