Big Data and Analytics in IoT and M2M

ABI Research’s first report about the use of big data and analytics in the Internet of Things (IoT) was published in 2013, under the name Big Data and Analytics in M2M Services (AN-1541). That was the first attempt by any analyst house to draw a line in the sand and cover the topic as a dedicated research study. Ever since then, ABI Research has continued to track and analyze the market on an ongoing basis, responding to demand from a growing number of customers that are trying to make sense of how analytics and M2M/IoT intersect with each other.

This report builds on those research efforts, providing a year-end overview of the market and its dynamics. The first part of the study provides an introduction to the topic and the supporting conceptual framework, as well as a summary of the most important trends and recent developments.

The second section presents the relevant market forecasts, which have been updated and refined from the previous iteration. The forecasts are broken down by application segment, geographical region, value-chain component, as well as phase of analytics. Additionally, a high-level revenue projection for the overall big data and analytics market is included in the end of the section.

In the report’s third section, ABI Research covers a number of vendors that operate within different value-chain components and at different stages of business maturity. The included company profiles introduce players that are seeing evident traction in the IoT, targeting the IoT as their main opportunity, or whose technology could, in ABI Research’s view, prove a strong fit for IoT deployments. This company review is also meant to give a better understanding of what types of activities each value-chain component comprises in practice.

  • 1. INTRODUCTION TO VALUE CHAIN AND RECENT TRENDS
    • 1.1. Value Chain in Brief
    • 1.2. Three Phases of Data Analytics
    • 1.3. Key Trends and Observations from 2014
  • 2. UPDATED MARKET FORECASTS
    • 2.1. Revenues by Application
    • 2.2. Revenues by Region
    • 2.3. Revenues by Value Chain Component
    • 2.4. Revenues by Analytic Phase
    • 2.5. Overall Analytic Revenues by Data Type
  • 3. VENDOR LANDSCAPE REVISITED
    • 3.1. Data Integration
      • 3.1.1. Informatica
      • 3.1.2. Peaxy
      • 3.1.3. Primary Data
      • 3.1.4. Splunk
      • 3.1.5. Talend
    • 3.2. Data Storage
      • 3.2.1. Infobright
      • 3.2.2. MongoDB
      • 3.2.3. ParStream
      • 3.2.4. SpaceCurve
      • 3.2.5. TempoIQ
    • 3.3. Core Analytics
      • 3.3.1. Blue Yonder
      • 3.3.2. mnubo
      • 3.3.3. Predikto
      • 3.3.4. RapidMiner
      • 3.3.5. Sensewaves
    • 3.4. Data Presentation
      • 3.4.1. Cyberlightning
      • 3.4.2. Datawatch
      • 3.4.3. N3N
      • 3.4.4. OnYourMap
      • 3.4.5. Space-Time Insight
    • 3.5. Full-stack Software Vendors
      • 3.5.1. IBM
      • 3.5.2. Microsoft
      • 3.5.3. Oracle
      • 3.5.4. SAP
      • 3.5.5. Software AG
    • 3.6. Additional Vendor Notes

Tables

  1. IoT Analytic Revenues by Application, World Market, Forecast: 2011 to 2020
  2. IoT Analytic Revenues by Region, World Market, Forecast: 2011 to 2020
  3. IoT Analytic Revenues by Value Chain Component, World Market, Forecast: 2011 to 2020
  4. IoT Analytic Revenues by Analytic Phase, World Market, Forecast: 2011 to 2020
  5. Overall Analytics Revenues by Type of Data, World Market, Forecast: 2011 to 2020

Charts

  1. IoT Analytic Revenues by Application, World Market, Forecast: 2011 to 2020
  2. IoT Analytic Revenues by Region, World Market, Forecast: 2011 to 2020
  3. IoT Analytic Revenues by Value Chain Component, World Market, Forecast: 2011 to 2020
  4. IoT Analytic Revenues by Analytic Phase, World Market, Forecast: 2011 to 2020
  5. Overall Analytics Revenues by Type of Data, World Market, Forecast: 2011 to 2020

Figures

  1. Components of Analytic Value Chain
  2. Three Phases of Data Analytics
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Research Information

Publish Date
4Q 2014
Code
AN-1623
Research Type
Technology Analysis Report
Pages
20