Growth projected for M2M content delivery platforms
Oct 24, 2013
Application Enablement Platforms (AEP) are slowly becoming an important element in the M2M ecosystem, according to ABI Research. These platforms ease the data extraction and normalisation activities so M2M applications and enterprise systems can better engage with machine data. They also assist in device and machine management. ABI calculates that revenues from these core capabilities are expected to drive AEP revenues past $800 million by 2018, representing a 32 per cent growth rate.
“For a broad swath of firms pursuing M2M, application development is still done in the traditional manner using SIs, ISVs, and internal teams, but the time-to-market advantages of AEPs is significant,” said ABI research Practice director Dan Shey. “As M2M moves to a broader set of small, medium, and increasingly larger businesses, AEPs will be a critical and necessary enabler.”
A greater acceptance of cloud services, the attractiveness of new business models for MaaS (Machine as a Service), SLAs and lower costs will also help with the growth of AEPs. An equally important driver is evolution of the supplier ecosystem.
“The M2M value chain is still complex which is a drag on adoption,” adds Shey. “More suppliers are augmenting their capabilities to offer nearly one-stop shop services. Pure-play AEPs, SIs, and other mobile suppliers are all leading the charge. For device OEMs, adding services capabilities is almost a necessity to avoid commoditization pressures and capture revenues in the largest revenue segment of the M2M value chain – value-added services.”
ABI Research also forecasts that the M2M analytics industry will grow by 53 per cent over the next five years to reach $14bn in 2018. The forecast includes revenue segmentation for the five components that together enable analytics to be used in M2M services: data integration, data storage, core analytics, data presentation, and associated professional services.
At the moment, most enterprises with relevant data assets are trying to migrate from descriptive and diagnostic insights to predictive analytics. Mastering the predictive phase could then ultimately lead to the final, prescriptive phase of analytics.
“Predictive analytics is becoming one of the hottest areas in the M2M value chain,” said Shey. “Of today’s analytics establishment, SAP and IBM have woken up to the opportunity reasonably early. Of the younger companies, Splunk is an example of a firm that could develop into a true Internet of Things powerhouse if it plays its cards right. Given the far-reaching possibilities of machine learning, we are also expecting major impact from players that successfully apply it to industrial settings.”