At EMA, we recently finished IoT research to identify trends and adoption of device related implementations. The research identified projects, industry adoption, business drivers for IoT adoption, and types of devices leveraged for different types of analytical applications. All of the data supports the increasing adoption of IoT and a focus on data driven organizations that understand that effective data management will help support their overall business and competitive advantage.  With big data implementations that are more likely to support analytics, organizations are more apt to take advantage of technology to support better business processes.

IoT is the most direct example of how technology can support better business and process efficiencies. For instance:

  • Quality control within manufacturing plants to ensure working order of machinery and product creation
  • Smart meters and smart city technologies to support better traffic flow and environmental policies
  • Patient support through insight and monitoring of medical devices
  • Personal wearables to track activities

Leveraging device data means organizations, cities, etc. can gain additional insights into all of these areas and more. In terms of analytical insight, this creates an opportunity for businesses that was not as accessible just a few years ago.  The ability to leverage data and connect to devices, ensure that data is stored within a big data store and access that data to gain insight through analytics. All of this is complicated but becoming more accessible based on solution provider support.

The reality, however, is that storing and accessing the data is not enough. Even analytics only provide limited value if the data is not acted upon. Athletes use wearables to track their performance, workouts, heart rate, and other metrics to ensure that their training is on track and that they can meet their overall goals. All of this data gets uploaded and certain businesses can combine all of that data to identify trends, provide overall comparisons to users, and develop products and services to meet customer needs. This outlook provides a limited overall view. For people managing their health, being able to combine this data with medical devices can provide more proactive insight into health both on the individual and societal level.

This example provides insight into consumer use and highlights that organizations need to combine their IoT data with other forms of analytics to get relevant insight. Without the ability to gain more perspective and actually act upon the insights gleaned from IoT, it becomes hard to sell the value proposition. As organizations leverage more IoT data and become more mature with their analytics initiatives, they will be able to leverage analytics more and take advantage of a data driven approach to create a stronger link between data assets and business decision-making and action.