Prescriptive IoT Analytics: What Should Be Done?
by Exosite, on June 30, 2016
Whether in large scale industrial IoT applications or connected consumer good applications, efficiency driven by data is a key selling point. In order for an IoT analytics strategy to be complete, a company must understand the use cases and importance of prescriptive analytics. The final stage of IoT data analytics maturity involves deriving actionable items from predictions made in previous stages. When thousands of IoT connected devices are online and their data is being routed through various machine-learning algorithms, they can provide users and administrators with incredibly valuable insights into what the fleet is doing and what action should be taken to modify its future behavior. The previous descriptive, diagnostic, and predictive IoT analytics blog segments took the process one step closer to no-touch decision making; prescriptive analytics takes it the rest of the way.
Decision Support Vs. Decision Automation
Efficient and effective decision support and/or decision automation is the culmination of a mature data analysis program. Complex systems of information that would take months of analysis to understand can be streamlined into a decision-making algorithm that runs in a fraction of a second. However, the role prescriptive analytics play in supporting decisions or automating them depends heavily on the application.
Some applications call for results that support and inform decision makers so that they can efficiently make the best possible decisions for the company and device fleet. For example, decision support was provided in the form of a prioritized maintenance schedule for an elevator fleet, but the actual scheduling was left to a human who had a more intimate understanding of the different criteria for which products need to be serviced first.
Other connected product applications may include problems that have greater complexity or require faster response times and are, therefore, automated so that no human touch is required. A device that is about to fail and may cause injury, perhaps a pump on a load-bearing crane, will shut down safely and a replacement part will be ordered automatically.
For a complete description of best analytic practices, download the full IoT analytics white paper below or contact us directly to kick-start your IoT solution today.