Predictive, advanced, and Prescriptive Analytics within the IoT Analytics Market



For the stakes related to machinery cost, enhanced productivity and performance, the importance of analytics in IoT is steadily appropriated within many industries, such as within the industrial, manufacturing, field, oil, and gas sectors.


Companies like Cisco, Dell, GE, IBM, Microsoft, PTC, and SAP, as well as emerging startup vendors like Blue Yonder, mnubo, Mtell, Predixion, and Seeq underscores the momentum and importance of analytics in IoT.

From lack of skills and awareness on how to manage, track, and analyze all data, to the need to harmonize IoT ecosystem components without creating or simply shifting the bottlenecks, the IoT market challenges are real.

However, overall, the dynamics are positive. ABI Research forecasts global revenues from the integration, storage, analysis, and presentation of IoT data to triple over the forecast period and top US$30 billion in 2021, with a 29.4% CAGR.

“Descriptive analytics currently generate more than 75% of IoT analytics revenue,” says Ryan Martin, Senior Analyst at ABI Research. “But over the next five years, rapid uptake of advanced analytics will overtake descriptive analytics’ share of revenue to the extent that predictive and prescriptive analytics will account for more than 60% of IoT analytics revenue by 2021.”

ABI Research reveals that, the general shift from batch to event-based processing signals a growing interest in real-time/streaming analytics as a lever for IoT value creation. Data analysis from this firm suggests that early adoption of predictive and prescriptive analytics is occurring in more developed, mature M2M/IoT verticals. 

“The need to harmonize IoT ecosystem components without creating or simply shifting the bottlenecks that come with the management of high-velocity variable data puts pressure on connectivity providers, edge analytics platform players, and system integrators to stand up new and distributed frameworks.” “The purpose of these frameworks is to not only support and add value to data, but to also be able to do so at any level,” says Ryan Martin, Senior Analyst at ABI Research.