Predictive Analytics Software Buying Guide

Drew Robb

Updated · Feb 16, 2016

While many vendors sell analytics software, not all of them provide predictive analytics, a far more specialized field. Unlike more traditional analytics, predictive analytics typically involves sophisticated techniques such as predictive modeling, data mining, machine learning and statistical algorithms. The focus is on predicting future outcomes or trends. 

So who are the key players? Forrester Research includes SAS, RapidMiner, FICO, Alpine Data Labs, Alteryx and Microsoft among its predictive analytics leaders and important challengers.

The only vendor with serious market share is SAS, with 33 percent of the predictive analytics software market. Next comes IBM, which commands 15 percent of the market despite the fact it doesn’t bill itself as a predictive analytics specialist.

Outside those two, no vendor holds more than a 2 percent share of the market. Clearly, there is room for advancement for vendors that provide predictive analytics software. The market is growing; more companies are becoming interested in predictive analytics as vendors introduce more user-friendly technologies.

Selecting a predictive analytics provider is only part of a successful initiative. We recently shared tips for getting your predictive analytics project started as well as tips on data preparation for predictive analytics.

In this software buying guide, we’ll look at product offerings from five vendors:

  • Market leader SAS
  • Information Builders
  • Alpine Data Labs
  • MicroStrategy
  • RapidMiner


SAS offers a portfolio of predictive analytics software for different user roles. Its flagship is SAS Enterprise Miner, an integrated workbench for data mining and machine learning. It streamlines the predictive analytics process to create analytical models from vast amounts of data.

This is accomplished via a collection of statistical, data mining and machine learning algorithms. Decision trees, bagging and boosting, time series data mining, neural networks, memory-based reasoning, hierarchical clustering, linear and logistic regression, associations, sequence and Web path analysis and more are involved. There are even industry-specific algorithms such as credit scoring.

In 2015 SAS unveiled an add-on to SAS Enterprise Miner to help automate massive model development projects in analytics-driven organizations. SAS Factory Miner provides a Web based, automated, customizable, environment for building, comparing and retraining predictive models at scale across multiple business segments. The software tests multiple models simultaneously and identifies the best performing model for each segment based on predefined performance statistics. If desired, it allows manually fine-tuning predictive analytics models.

SAS users can take advantage of workflow templates provided with the product, or they can interactively build custom templates that include the various steps of a predictive analytics workflow, such as data transformation, data filtering, variable selection and model building. All assets are created automatically to deploy selected models into SAS or third-party production environments such as Hadoop, Web services or a database like Oracle, DB2, Greenplum or SAP HANA.

Information Builders

Part of the Information Builders (IBI) suite of business analytics solutions, WebFOCUS RStat is an integrated BI and data mining environment that is said to bridge the gap between backward and forward-facing views of business operations.

“With RStat, companies can access and cost effectively deploy predictive models as intuitive scoring applications, so business users at all levels can make decisions based on accurate, validated future predictions instead of relying on gut instinct alone,” said Bruce Kolodziej, Predictive Analytics sales manager, IBI.

WebFOCUS RStat provides a single platform for business intelligence, data modeling and scoring. It is integrated with App Studio and WebFOCUS Reporting Servers, with access to more than300 data sources for BI developers and data miners. It offers data exploration, descriptive statistics and interactive graphs, hypothesis testing, clustering and correlation analysis. It has the ability to build and export models for prediction and classification, evaluating those models and incorporating their findings into reports, Kolodziej said.

Alpine Data Labs

Alpine‘s predictive analytics software aims to take a full stack approach with the Alpine Data Platform comprised of three layers: Alpine Core, Alpine Connect and Alpine Touchpoints. Alpine Core is the central analytics engine that allows users to run analytics in-database and in parallel, and build predictive models using a visual development interface. Alpine Connect is the model management and collaboration layer.

“Data scientists need to interface with business stakeholders in order to deliver relevant results,” said Steven Hillion, co-founder and chief product officer. “Connect provides a way for them to collaborate on analytics projects inside of the platform in a Facebook-like manner.”

The final part of the solution is Alpine Touchpoints, which provides an interface for business users to create predictive analytics applications that serve up the intelligence from underlying models in a business context.


The MicroStrategy platform offers three interfaces for the development and consumption of predictive analytics: Desktop (for offline analysis on a PC and Mac), Web (across any browser) and Mobile (with native apps for iOS and Android). The products offer a library of over 350 native statistical and predictive functions, and they can integrate with other open source and third-party advanced analytical models.

With native support for regression and time series analysis, logistic regression and decision trees, clustering and association, MicroStrategy users can build and deploy predictive analytics applications that range from forecasting and predictive classification, to segmentation and market basket analysis.  It supports PMML (predictive model markup language), as well as function plug-ins written in C++ and the open source statistical R language.

“Users can add R analytics that execute within the MicroStrategy engine just like our native analytics, without requiring the overhead of additional R Servers or unnecessary data movements,” said Stefan Schmitz, vice president of Product Management at MicroStrategy.


According to Forrester Research, RapidMiner offers solid enterprise predictive analytics software that also includes cloud capabilities. It can deal with hundreds of data loading, data transformation, data modeling and data visualization methods and has access to data sources such as Excel, Access, Oracle, IBM DB2, Microsoft SQL, Netezza, Teradata, MySQL, Postgres, SPSS and

Integrating specialized predictive analytics algorithms into RapidMiner is said to be simple, and it can run on every major platform, operating system and device.

Drew Robb
Drew Robb

Drew Robb is a writer who has been writing about IT, engineering, and other topics. Originating from Scotland, he currently resides in Florida. Highly skilled in rapid prototyping innovative and reliable systems. He has been an editor and professional writer full-time for more than 20 years. He works as a freelancer at Enterprise Apps Today, CIO Insight and other IT publications. He is also an editor-in chief of an international engineering journal. He enjoys solving data problems and learning abstractions that will allow for better infrastructure.

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