We explore the future of analytics driven M2M including the drivers of growth, the potential ecosystem that may address the market and the significance for MNOs.

M2M Analytics: A New Wave of Innovation

By Susan Simmons and Sidhant Jalan

To date, typical enterprise Machine–to–Machine (M2M) projects have replaced legacy processes to serve basic operational needs, e.g., utility smart meters or connected vending machines. These projects are embedding instrumentation, connectivity and intelligence in core business assets. M2M based analytics will utilize the new data streams and system capabilities to enable business transformation and usher a new wave of innovation.

Factors Driving the M2M Analytics Opportunity

M2M Data Growth

The volume of data generated by M2M modules is growing rapidly. Cisco forecasts that the average M2M module will use 330 MB/month by 2017 vs. 64 MB/month in 2012 due to advances in network and device technologies, the falling cost of data and new data-intensive use-cases. Forecasts for connected M2M devices vary based on research house and definitional inclusions, but high subscriber growth is certain: Ericsson forecasts 50 billion connected devices worldwide by 2020, Berg Insight forecasts 290 million connected mobile devices in the US by 2015 and IMS Research forecasts 100 million connected devices per year reflecting 30% CAGR through 2015. This growth in M2M data should ultimately provide enterprises with more real-time, valuable insights.

New Analytical Technologies

M2M data sources like machine logs, biometric sensors and video surveillance generate significant volumes of data that necessitate the use of Big Data technologies to store and process. Web giants like Google and Facebook served as the early pioneers of big data analytics. As a new generation of start-ups builds off their innovations in areas like NoSQL databases, hardware optimization and data science methodology, deep data mining is becoming more accessible to wider enterprise community, including M2M.

In addition, cloud-based deployment models are centralizing the storage of data into common public/private repositories that are easily accessible and can scale with data volume. These deployment models allow for easier access to M2M data and better as-a-service deployment and business models.

However, many early adopters of M2M technologies use proprietary solutions for data management and/or integration with legacy systems. Due to issues with data inconsistency, privacy concerns and the costs involved in transporting and reformatting data, enterprises have been slow to adopt third-party hosted big data analytics solutions.

Business Value through Data Analysis

So far, M2M deployments have generated value through basic process enablement, transaction support, asset management and one-time custom decision support. But these efforts only scratch the surface of the potential for M2M analytics. Over time, M2M data-stores will assimilate sufficient historical data to produce results with high levels of confidence, analytics-based algorithms will gain maturity and adoption, and M2M data will be used for predictive analytics rather than being restricted to reporting and monitoring. Ultimately, analytics-enabled opportunities will change established business models, improve efficiencies and enhance customer experiences.

Figure 1: Value Creation Mechanisms through M2M Analytics

Value Creation Mechanisms through M2M Analytics

M2M Analytics Ecosystem

The M2M analytics market is evolving from the growth in the larger Big Data/enterprise analytics market. M2M analytics players may leverage multiple capabilities, such as access to data or vertical knowledge. However, the market is nascent and various players with different core strengths are staking their claims in this space as part of the larger enterprise M2M enablement market.

Figure 2: Competitive Vendor Analysis

M2M Analytics - Competitive Vendor Analysis

Three factors will be important for successful players in M2M analytics: 1) Scale: implied ability to provide stable, cost-efficient and universal analytics offerings by reaping economies of scale, using industry influence and affecting pricing and solution deployment; 2) Position of Trust: perception and ability to understand and secure sensitive information; 3) Technical advantage: capabilities for developing and deploying analytical solutions which integrate with existing solutions without causing additional governance, deployment and performance impact. Some current players in the M2M ecosystem will look at analytics as a way to supplement their core revenue streams. Others will see M2M analytics as a standalone play. Acquisitions are inevitable.

In the near term, analytical capabilities may be offered as feature extensions within existing solutions. Platform-based solutions may emerge. Cultivating a developer ecosystem by, for example, creating an open source platform or an API-based third-party environment can ensure continuous innovation and drive value from such platforms.

Vertical considerations could also affect the competitive landscape. Health care organizations require highly trusted third-party support for sharing information across the value chain, while utilities may require limited industry level information-sharing. M2M analytics software hosted on the public cloud could be more applicable for platform-based solutions, whereas proprietary private solutions will be hosted inside enterprise data centers. In all scenarios, vendors who can secure customer data and innovate to solve real business problems will be the biggest share winners.

Significance for MNOs

Value-added M2M revenues have been a small fraction of total M2M revenues, but are expected to grow rapidly over the next few years as MNOs expand beyond connectivity to managed solutions across different verticals. According to Machina Research, estimated traffic revenue as a percentage of total M2M solutions revenue (2020) is 2% for utilities, 3% for Automotive, 4% for healthcare. In such a scenario telecom operators will attempt to increase value capture by not just transporting data but also storing, analyzing and mining it.

Mobile operators and other service providers have recently made some strategic moves to garner increased relevance in the M2M value chain, including both cloud and analytics capabilities. Operators have developed M2M service delivery platform capabilities, e.g., Axeda’s partnership with AT&T and Verizon’s acquisition of nPhase. Others, like Sprint and Orange, have signed roaming agreements to expand the number of markets for M2M deployments. Service providers can offer technical benefits such as the ability to integrate network, storage and computing assets to provide optimized real-time analytics performance for mission-critical workloads.


M2M based analytics will not only be a high growth opportunity by itself but will also be a strategic differentiator to remain competitive in the overall M2M market. Ecosystem players should start thinking about their long term strategy for developing M2M analytics solution capabilities. Some of the important questions that need to be addressed by players in this ecoystem are:

  • Which M2M markets benefit greatest from the inclusion of analytics?
  • Should analytical offerings be directly monetized by M2M providers or will they create value by increased core market share capture?
  • What are the security/privacy concerns that need to be addressed to access data?
  • For existing M2M enablement vendors, how should analytics solutions be developed? What are the trade-offs in HW/SW/cloud capabilities?
  • What is the internal strategy for continued innovation given larger evolution of analytics and Big Data technologies?