Ability to wirelessly transfer information to and from otherwise unconnected devices such as the coffee machine, the garage door, the industrial equipment and the power meter is what fueled the initial excitement on the Internet-of-Things (IoT) and machine-to-machine (M2M) communications. Big data analytics have enabled raw data collected in huge data lakes across data centres to be filtered, processed and extracted into other applications. Businesses, leveraging cloud services are able to cost effectively manage the collection of data, store the data securely in their data reservoirs, deploy required computing resources and produce the information which is later used as input across all their business processes.
Big data analytics have enabled raw data collected in huge data lakes across data centres to be filtered, processed and extracted into other applications. Businesses, leveraging cloud services are able to cost effectively manage the collection of data, store the data securely in their data reservoirs, deploy required computing resources and produce the information which is later used as input across all their business processes. In the case of IoT/M2M, the processed information is channelled back in almost real-time onto applications that are accessed by end users - both enterprises and also retail customers - on a multitude of end terminals including computers and mobile devices. Without big data, most IoT/M2M applications will not be producing the real-time insights necessary for end users to control and manage the M2M devices/appliances remotely.
IoT/M2M will hence become one of the biggest growth drivers for big data analytics.  According to ABI Research, revenues from integrating, storing, analyzing, and presenting Internet of Things (IoT) data will reach US$5.7 billion in 2015. The firm expects the growth to intensify over the next five years, resulting in IoT/M2M accouting for nearly one-third of all big data and analytics revenues by 2020. Data generated by IoT/M2M however, brings with it a few challenges as it differs substantially from traditional data. ABI Research said that IoT-type data requires time-series databases in storage and equally competitive expertise in analysis and pointed out that there are a number of startups who have started offering big data solutions specifically for IoT/M2M applications, which can be deployed by IoT/M2M service providers to drive their services. At the same time, incumbents, specifically telecom operators will be watching this space with much interest as it presents opportunities for Operators to offer big data analytics as a service for enterprise customers who are already using the operators' network and datacenter services for connectivity and storage of information collected from thousands (and sometimes millions) of their M2M devices. Operators are also the connectivity providers for end-users of the M2M applications, such as Smart Home Apps or the Connected Car Apps, whose inputs are provided by these analytics. By merging connectivity services (to the M2M nodes as well as the end-user mobile devices), cloud services (data storage and computing) and big data analytics, Operators will be able to deliver integrated and fully fledged IoT/M2M solutions for a wide range of applications in this fast growing segment.Â
