Big Data and Analytics in M2M Services Image

Big Data and Analytics in M2M Services

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Big data is a factor that will, to a large extent, determine the future growth rate in the M2M industry. Without adequate analytics, and the right practices to take advantage of it, the companies rolling out M2M solutions will be destined to be stuck in the lower realms of applications: monitoring, reporting, and simple rules-based actions. These are worthy areas by their own merit, but the value that can be extracted from them does not necessarily justify large-scale sensor and connectivity deployments. This issue should not also be overlooked when discussing why M2M has not previously managed to evolve as fast as has been generally anticipated. The value of machine data has not, in many use cases, justified the cost of its extraction.

In this technology analysis, ABI Research will explore how big data and analytics are being applied in the M2M industry and what role they can be anticipated to play in the M2M value chain. The first part of the study provides an overview of a standard analytics process and its components, while the second part presents ABI Research's relevant market forecasts. The market's revenues are broken down by industry vertical, segment (data integration, data storage, core analytics, and data presentation), besides which the revenues in the core analytics segment are split by analytics phase (descriptive, predictive, and prescriptive). Included is also a forecast on the installed base of analytics-enabled M2M connections. Finally, the third part of the report examines the leading use cases for M2M analytics and the vendor landscape.

Table of Contents

Table of Contents

  • 1. DATA'S ROLE IN THE M2M VALUE CHAIN
    • 1.1. The Overall M2M Service Value Chain Explained
    • 1.2. Four Components of Data Management and Analytics
    • 1.3. Professional Services
    • 1.4. Three Phases of Data Analytics
  • 2. MARKET FORECASTS FOR M2M ANALYTICS
    • 2.1. Methodology
    • 2.2. M2M Analytics Revenues by Industry Vertical
    • 2.3. M2M Analytics ARPU by Industry Vertical
    • 2.4. Installed Base of Analytics-enabled M2M Connections by Industry Vertical
    • 2.5. M2M Analytics Revenues by Segment
    • 2.6. M2M Analytics Revenues by Analytics Type
  • 3. USE CASES AND COMPETITIVE LANDSCAPE
    • 3.1. Five Leading Use Cases for M2M Analytics
      • 3.1.1. Predictive Maintenance
      • 3.1.2. Product and Service Development
      • 3.1.3. Usage Behavior Tracking
      • 3.1.4. Operational Analysis
      • 3.1.5. Contextual Awareness
    • 3.2. Vendor Landscape

Tables

  1. M2M Analytics Revenues by Industry Vertical, World Market, Forecast: 2012 to 2018
  2. M2M Analytics ARPU by Industry Vertical, World Market, Forecast: 2012 to 2018
  3. Installed Base of Analytics-enabled M2M Connections by Industry Vertical, World Market, Forecast: 2012 to 2018
  4. M2M Analytics Revenues by Segment, World Market, Forecast: 2012 to 2018
  5. Standalone M2M Analytics Revenues by Analytics Type, World Market, Forecast: 2012 to 2018

Charts

  1. M2M Analytics Revenues by Industry Vertical, World Market, Forecast: 2012 to 2018
  2. Installed Base of Analytics-enabled M2M Connections versus Overall M2M Connections, World Market, Forecast: 2012 to 2018
  3. M2M Analytics Revenues by Segment, World Market, Forecast: 2012 to 2018
  4. Standalone M2M Analytics Revenues by Analytics Type, World Market, Forecast: 2012 to 2018

Figures

  1. Share of Value in the M2M Service Chain
  2. Components of Data Management and Analytics Value Chain
  3. Phases of Data Analytics