INDEX

Big Data and Analytics in IoT and M2M

Table of Contents

  • 1. FROM INSIGHT TO ACTION: THE BIG DATA ANALYTICS VALUE CHAIN AND RECENT TRENDS
    • 1.1. The Analytics Value Chain
    • 1.2. The Three Phases of Data Analytics
    • 1.3. Key Trends and Observations from 2015
  • 2. UPDATED MARKET FORECASTS
    • 2.1. Revenue by Application
    • 2.2. Revenue by Region
    • 2.3. Revenue by Value Chain Component
    • 2.4. Revenue by Analytic Phase
    • 2.5. Overall Analytics Revenue by Data Type
  • 3. VENDOR LANDSCAPE
    • 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. TempoIQ
    • 3.3. Core Analytics
      • 3.3.1. Blue Yonder
      • 3.3.2. Falkonry
      • 3.3.3. mnubo
      • 3.3.4. Mtell
      • 3.3.5. Predikto
      • 3.3.6. Predixion Software
      • 3.3.7. RapidMiner
      • 3.3.8. Sensewaves
    • 3.4. Data Presentation
      • 3.4.1. CyberLightning
      • 3.4.2. Datawatch
      • 3.4.3. N3N
      • 3.4.4. OnYourMap
      • 3.4.5. Qlik
      • 3.4.6. 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
      • 3.5.6. Cisco


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The value of big data stems from its ability to surface relevant and actionable insights at the micro level. The proliferation of Internet-connected devices presents a framework for analytics to become much more granular in nature and an opportunity to better align the frequency of reporting to the pace of business operations.

While today the biggest challenge lies in managing the variety–rather than the volume or velocity–of IoT data, the general shift from batch- to event-based processing signals a growing interest in real-time / streaming analytics as a lever for IoT value creation. The need to harmonize these components without creating or simply shifting the bottlenecks that come with the management of high-velocity variable data puts pressure on connectivity providers, edge analytics platform players, and system integrators (SIs) to stand up new and distributed architectures to not only support, but also add value to data at any level.

In this research analysis, ABI Research analyzes what it considers to be the most significant trends and developments related to the IoT analytics industry.  The first section includes the conceptual framework supporting the research, as well as a wrap-up of recent activity.  The second section consists of the updated and refined forecasts on the market’s revenues, broken down by application, region, value chain component, and analytic phase (descriptive, predictive, prescriptive). A high-level revenue projection for the overall big data and analytics market is also included at the end of the section for a view of IoT analytics in a broader industry context.  The final section provides a high-level assessment of a number of relevant vendors to exemplify the different parts of the value chain.

Table of Contents

  • 1. FROM INSIGHT TO ACTION: THE BIG DATA ANALYTICS VALUE CHAIN AND RECENT TRENDS
    • 1.1. The Analytics Value Chain
    • 1.2. The Three Phases of Data Analytics
    • 1.3. Key Trends and Observations from 2015
  • 2. UPDATED MARKET FORECASTS
    • 2.1. Revenue by Application
    • 2.2. Revenue by Region
    • 2.3. Revenue by Value Chain Component
    • 2.4. Revenue by Analytic Phase
    • 2.5. Overall Analytics Revenue by Data Type
  • 3. VENDOR LANDSCAPE
    • 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. TempoIQ
    • 3.3. Core Analytics
      • 3.3.1. Blue Yonder
      • 3.3.2. Falkonry
      • 3.3.3. mnubo
      • 3.3.4. Mtell
      • 3.3.5. Predikto
      • 3.3.6. Predixion Software
      • 3.3.7. RapidMiner
      • 3.3.8. Sensewaves
    • 3.4. Data Presentation
      • 3.4.1. CyberLightning
      • 3.4.2. Datawatch
      • 3.4.3. N3N
      • 3.4.4. OnYourMap
      • 3.4.5. Qlik
      • 3.4.6. 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
      • 3.5.6. Cisco