How M2M is changing the mobile economy

1Executive Summary

Amazon, the poster child of the online presence, revealed that the 2013 U.S. holiday period was its best yet, with more than 36.8 million items ordered on Cyber Monday alone. At the same time, traditional B&M stores in the U.S. received approximately half the footfall they did just three years ago. The advent of the cloud has given enterprises the tools to optimize costs and grow revenues further still. Today, even the most risk-averse bosses are now at the very least open to moving mission-critical enterprise resource planning (ERP) software to the cloud.

As interest in the internet of things (IoT) has grown, so too has business’ focus on machine-to-ma­chine (M2M) communications. This is especially so given that in addition to the advent of the cloud, the falling cost of devices and components, cheaper airtime, new and more mature networking technologies, and the emergence of service delivery platforms (which dramatically reduce M2M complexity) have all accelerated the growth of this market. The operational efficiencies that can be achieved when systems and devices are connected in real time such as better inventory management, remote IT support, automatic data entry, and improved interdepartmental communication are just too great to overlook.

Key highlights from this report include:

  • M2M data is set for a huge explosion, with billions of connected devices predicted by 2020. This necessitates the management of vast swathes of structured and unstructured data.
  • Batch processing of machine data is used to uncover patterns in data that enable smarter business decisions. Hadoop is still considered the de facto standard for big data analysis.
  • To uncover patterns that will provide insight about the past, present, and future, both batch and real-time processing of data streams is desirable. Only then can things like automatic fault detection and diagnostics of equipment ensure that maintenance is carried out optimally. Storm, Jubatus, and AerCloud are examples of real-time or near-real-time machine-learning solutions.
  • As M2M devices and data increase, so too will the probability of hackers targeting these systems to exploit networks, steal data, hijack systems, and compromise workflows. End-to-end security therefore needs to be implemented to fully protect enterprise networks.
  • Firewalls, secure boot, and strong encryption and authentication are all ways to boost security.
Relevant analyst in machine to machine (M2M)
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