Predictive Analytics - Tools

Tools

Historically, using predictive analytics tools—as well as understanding the results they delivered—required advanced skills. However, modern predictive analytics tools are no longer restricted to IT specialists. As more organizations adopt predictive analytics into decision-making processes and integrate it into their operations, they’re creating a shift in the market toward business users as the primary consumers of the information. Business users want tools they can use on their own. Vendors are responding by creating new software that removes the mathematical complexity, provides user-friendly graphic interfaces and/or builds in short cuts that can, for example, recognize the kind of data available and suggest an appropriate predictive model. Predictive analytics tools have become sophisticated enough to adequately present and dissect data problems, so that any data-savvy information worker can utilize them to analyze data and retrieve meaningful, useful results. For example, modern tools present findings using simple charts, graphs, and scores that indicate the likelihood of possible outcomes.

There are numerous tools available in the marketplace that help with the execution of predictive analytics. These range from those that need very little user sophistication to those that are designed for the expert practitioner. The difference between these tools is often in the level of customization and heavy data lifting allowed.

Notable open source predictive analytic tools include:

• KNIME
• Orange
• R
• RapidMiner
• Weka

Notable commercial predictive analytic tools include:

• Angoss KnowledgeSTUDIO
• IBM SPSS Statistics and IBM SPSS Modeler
• KXEN Modeler
• Mathematica
• MATLAB
• Oracle Data Mining (ODM)
• Pervasive
• SAP
• SAS and SAS Enterprise Miner
• STATISTICA
• TIBCO