Predictive analytics is an integral part of our daily lives. At this very moment, predictive solutions are busy at work, monitoring financial transactions for fraud and abuse, recommending movies and other products, or selecting the next best offer you will get from your favorite store. As much as it permeates our lives today, the application of predictive analytics is bound to increase. For example, boosted by Big Data and cost efficient processing in the cloud, predictive maintenance applications are on their way towards becoming ubiquitous.
Predictive analytics is an integral part of our daily lives. At this very moment, predictive solutions are busy at work, monitoring financial transactions for fraud and abuse, recommending movies and other products, or selecting the next best offer you will get from your favorite store. As much as it permeates our lives today, the application of predictive analytics is bound to increase. For example, boosted by Big Data and cost efficient processing in the cloud, predictive maintenance applications are on their way towards becoming ubiquitous.
Predictive maintenance solutions are based on the idea that one is able to know that a machine or equipment is going to fail, and take proactive actions to ensure process reliability and safety. By using data from sensors that capture vibration information from rotating equipment, my team built a predictive maintenance solution that alerted personnel of eminent breakdowns. For that, we used a combination of statistical tools. For example, we used R, an open-source statistical package for data analysis, IBM SPSS Statistics for analysis and model building, and the Zementis ADAPA platform for model deployment. Since all these systems support PMML, the Predictive Model Markup Language, instead of spending time translating code from one system to another, we were able to concentrate on the problem itself and use the tools we trusted the most to get the job done.
PMML is the de facto standard used to represent predictive analytics or data mining models. With PMML, a predictive solution may be built in one system and deployed in another where it can be put to work immediately. The adoption of PMML by all the major analytic vendors is a testimony to their commitment to interoperability and the advancement of predictive analytics as a critical factor to the betterment of society. PMML is developed by the Data Mining Group (DMG), a committee composed not only by commercial and open-source analytic companies including IBM, SAS, Zementis, FICO, Salford Systems, Microstrategy, Togaware, KNIME and Rapid-I, but also by analytic users such as NASA, Visa, the San Diego Supercomputer Center, and Equifax.
Predictive analytics and open standards can provide yet another tool for safe guarding operations and ensuring safety and process reliability. While predictive analytics can offer solutions to alert us of problems before they actually happen, open standards such as PMML are key ingredients for ensuring that the building and deployment of predictive maintenance solutions is application independent and so agile and transparent.
We recently wrote a series of two articles for the IBM developerWorks website that covers PMML and predictive maintenance. To read both articles in their entirety, please refer to the following links:
1)What is PMML? Explore the power of predictive analytics and open standards
2)Representing predictive solutions in PMML: Move from raw data to predictions
