Getting Semantic about Sensors

The dialogue this week has been on data analytics. From aggregating raw sensor data to Web visualization, the story has been Big Data.  Big Data, as a concept, is as nebulous as the cloud.  Often wanting to avoid sounding too parochial to a vertical, the discussion is so general I think I am hearing about snake oil.

From a garbage in garbage out stand point, often the demos involve something that uses crowd sourcing, which is useful to judge sentiment, but not necessarily accurate.

My discussion with Bright Wolf Co-Founder James Branigan and Director David Houghton, quickly got me past my normal concerns about the analytics discussions.  Bright Wolf as a solution is white labeled by various customers and they did not get into specific details of how they were being used, only that they were profitable, initially self-funded, and have some 50 customers with some from the Fortune 50. 

They started by sharing that they had a strong history of large system development.  However, Bright Wolf was developed using the semantic Web for smart sensors and devices to start—continuing with the same semantic structure for the Enterprise and the Cloud. What you have then is a structure independent of some of the problems associated with integrating to cloud platforms or device specifications.

While speaking to Branigan and Houghton, I brought a conversation I had the night before about analytics being the next area of growth in the industry. Analytics will cause the rethinking of requirements for data from the sensors. This rang true to Branigan and Houghton, since they see the gaps between the intelligence available and the information needed.  IMHO, with processing so cheap, sensors are going to go through a transformation where the measurements will be contextualized and controls will be nuanced as well.

If change is the one constant we can expect, it’s nice to companies like Bright Wolf that have built in anticipation of it.