The Internet of Things (IoT), a network of connected devices that can send and receive data over the Internet, is a hot topic. On the consumer front, IoT buzz has centered around health monitoring devices (think activity trackers such as FitBit, cardiac monitors, diabetes monitors, child anti-kidnap devices) and “smart” home devices like thermostats, appliances, and the like. On the business front, a tagged or sensor enabled piece of equipment or any business asset can be monitored and analyzed. This might include a sensor on a pressure valve on a piece of drilling equipment, a tagged piece of construction material or even food moving to market.
You can monitor these IoT enabled “things” for theft, environmental conditions and so on. You can also analyze the data flowing from them. For instance, a popular example of IoT enabled analysis is preventive maintenance. An oil rig might have a number of components on it generating data that is streaming from the rig. This might include data on temperature, pressure, humidity, viscosity of lubricants, how many times a part moves, and so on. Back at the home base, a model might be generated based on the characteristics of parts that have failed in the past (i.e., a decision tree model that might produce a rule stating that if the pressure exceeds value X and temperature exceeds value Y, then the probability of failure is 80%). Based on these models, and new data coming from the rig, alerts can be generated as to when certain parts should be replaced.
This is just one example of what can be done. The list of IoT enabled analytics is getting longer by the day.
TDWI is seeing increased interest by organizations in IoT data. For instance, in my most recent Best Practices Report on Next Generation Analytics, we asked respondents what kind of data they are analyzing now and they expect to be analyzing 3 years from now. The figure below illustrates some interesting results for both machine generated and/or IoT data and real time streaming data (which might also be IoT data). While in both cases, usage today for this kind of data is under 20%, it looks like usage will double in the next three years (bringing the total to 50% of respondents), if users stick to their plans.
What does this mean for your organization? It means that organizations should start thinking about how IoT can impact their business. It might be in operational intelligence, or situational intelligence, or asset management. It might be in the quantified workplace, with smart buildings or workers wearing sensors that track their movements. Clearly, many organizations are still early on in their analytics journey, and IoT can seem overwhelming. The strategy might be a stepwise one. It may (but probably should not) happen overnight. You might tag and track some assets to begin. The point is to start learning more about it now and how it will affect your processes and culture; since that may be a big hurdle.
Fern Halper is Research Director for Advanced Analytics at TDWI.