Context is King to Operationalize Predictive Analytics

contextisking

Companies have invested significantly in Big Data solutions or capabilities. They usually start with adding more sensors on their equipment or perhaps bringing all of their historical data into a Big Data repository like Hadoop.  They have taken the first step towards a “Big Data” driven solution. The challenge is that “tackling” the data does not bring any tangible value.  This is why Predikto focuses so much of our R&D and technology in the “Action” related to the analytics.

Once data has been tackled, the next step is to perform some kind of data crunching or analytics to derive insight and hopefully perform an “Action” that brings real value to the business. Predikto is laser focused on Predictive Analytics for Industrial Fleet Maintenance and Operations “Moving Unplanned to Planned”. We spend a lot of time figuring out what “Action” our customer will take from the configuration of the Predikto Enterprise platform software.  Up to now, I have not mentioned Context. So, why is Context King?

The reason is that once our platform and the power of Predictive Analytics is able to provide an actionable warning that a piece of equipment is at a high risk for failure, Context becomes the next huge hurdle.  The first reaction by a user of our software is “Why should I care about this Predikto warning?”, “Why is this relevant?”, “Why is this warning critical?”, “I don’t trust this warning from a machine learning algorithm?”, etc…  You get the point.

This has driven Predikto to invest heavily in technologies and capabilities that help the maintenance expert or equipment engineer with “Context” as to why they should care or why this warning is important.  Users are able to easily drill through all of their maintenance, asset usage history, diagnostic codes, sensor data, and any other data which has been loaded into our platform.  The “Context” is King in order to empower the subject matter expert to confirm or validate prior to automatically performing the “Action” our software is recommending.

Next time you are rolling out a Big Data solution, focus on the key activities a user will take after you have tackled the data.  What automated action recommendation can I give experts and what Context can I provide to help them make a more informed and confident decision.