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Big Data in Internet of Things (IoT): Key Trends, Opportunities and Market Forecasts 2015 – 2020

Big Data in Internet of Things (IoT): Key Trends, Opportunities and Market Forecasts 2015 – 2020

Billions of devices generate data associated with various enterprise and consumer lifestyle events such as shopping activity, utility usage and other smart grid data, advertisement response, status of goods in transport, weather forecasts, traffic conditions, vehicle maintenance, public transport, downloads and usage of applications, and much more.

Big Data and Analytics software and applications will play a crucial role in making IoT a success. The data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes. However, Big Data in IoT is different than conventional IoT and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional Big Data infrastructure.

This research provides in depth qualitative analysis as well as forecasts through 2020 for Big Data in IoT. The report evaluates key business trends in Big Data deployments relative to IoT and assesses what companies doing in this area to generate a strong market share. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Semiconductor companies
Telecom network operators
Embedded systems companies
Telecom infrastructure suppliers
Big Data and analytics companies
Data as a Service (DaaS) companies
Application developers and aggregators
Cloud-based service providers of all types
Managed service and middleware companies
Data processing and management companies
Application Programmer Interface (API) companies
Identity management, privacy, and security companies
Public investment organizations including investment banks
Sensor, presence, location, and detection solution providers
Private investment including hedge funds and private equity

Report Benefits:

Forecasts (global, regional, and by industry) to 2020
Understand the role and importance of Big Data in IoT
Identify the early beneficiaries and longer-term winners
Identify key market issues and drivers for Big Data in IoT
Identify leading companies for Big Data and Analytics in IoT
Identify emerging trends and implications for Big Data in IoT
Understand the emerging vendor ecosystem for Big Data in IoT
Understand the impact on infrastructure, products, and services
Learn about the differences between Big Data with and without IoT
Identify areas for infrastructure, platform, and software investment

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1 Introduction
1.1 Scope of the Research
1.2 Target Audience
1.3 Companies in Report
2 Executive Summary
3 Big Data in IoT
3.1 By 2020, 50 billion IoT will generate 40 Zetabytes of Data
3.2 Big Data Framework for IoT
3.3 Need for New Protocols, Platforms, Streaming and Parsing, Software and Analytical Tools
3.3.1 Big Data in IoT will need Unified Logging Layer
3.3.1.1 Fluentd
3.3.2 Big Data in IoT will require Lightweight Data Interchange Format
3.3.2.1 JSON (JavaScript Object Notation)
3.3.3 Big Data in IoT will use Lightweight Protocols
3.3.3.1 OASIS MQTT (Message Queuing Telemetry Transport)
3.3.3.1.1 MQTT v3.1.1 is now Open Source OASIS Standard
3.3.3.2 XMPP (Extensible Messaging and Presence Protocol)
3.3.3.3 AMQP (Advanced Message Queuing Protocol)
3.3.4 Big Data in IoT will need Protocol for Network Interoperability
3.3.4.1 Data Distribution Service (DDS)
3.3.5 Big Data in IoT Demands Data Processing on Appropriate Scale
3.4 Big Data in IoT Challenges
3.4.1.1 Storage of Scalable High-volume Data
3.4.1.2 Data Management and Processing Raw Data: Difficult Task in Multivendor Environment
3.4.2 Data Security and Personal Information Privacy are the Biggest Hurdles
4 Key Trends for Big Data in IoT Business
4.1 Large Companies will Lead through M&A and Partnership with Start-ups
4.2 Big Data as a Service will take Major Market Share
4.3 Established Companies in M2M Analytics and Cloud Services will be Early Beneficiaries
4.4 Flexible and Scalable Revenue Model for Big Data Services will be Most Successful
4.5 Big Data brings 40% Savings in Operational Activities and 35% New Business Generation
5 Vendor Ecosystem: Big Data in IoT
5.1 AGT offers Cloud based Analytical Platform for IoT
5.2 AT&T’s M2X: Cloud-based Data Storage Service and Management Toolset Designed for IoT
5.3 Bosch Innovation offers Big Data Processing (BDP) for Data Analysis
5.4 Cisco IOX Framework and Fog Computing: Compute, Store, and Analyze Data at the Edge
5.5 GE Software Launched 24 Predictivity Solutions and GE Predix Platform
5.6 Intel Provides Cloud based Analytics System for IoT
5.7 MongoDB released Ver. 3.0 Database System
5.8 ParStream released New Version of its Analytical Platform
5.9 Real Time Innovation and Connext DDS: Comprehensive Messaging Platform
6 Big Data in IoT Market Forecast 2015 - 2020
6.1 Global IoT Deployment will become a $1.5 Trillion Market by 2020
6.1.1 Consumer IoT
6.1.2 Industrial IoT (IIoT)
6.2 Big Data in IoT Markets 2015 - 2020
6.3 Markets for Big Data Products/Services for IoT 2015 - 2020
6.3.1 Markets for Big Data Collection Infrastructure in IoT 2015 - 2020
6.3.2 Markets for Big Data Storage Infrastructure in IoT 2015 - 2020
6.3.3 Markets for Big Data Analytics and Application Infrastructure in IoT 2015 - 2020
6.3.4 Markets for Big Data as a Service in IoT 2015 - 2020
6.4 Big Data in IoT by Industry 2015 - 2020
6.4.1 Big Data in IoT for HVAC 2015 - 2020
6.4.2 Big Data in IoT for Consumer Electronics 2015 - 2020
6.4.3 Big Data in IoT for Healthcare 2015 - 2020
6.4.4 Big Data in IoT for Manufacturing 2015 - 2020
6.4.5 Big Data in IoT for Oil and Gas 2015 - 2020
6.4.6 Big Data in IoT for Transport and Cargo 2015 - 2020
6.4.7 Big Data in IoT for Utility 2015 - 2020
6.5 Regional Revenue Forecasts for Big Data in IoT 2015 - 2020
6.5.1 North American Market for Big Data in IoT 2015 - 2020
6.5.2 EMEA Market for Big Data in IoT 2015 - 2020
6.5.3 APAC Market for Big Data in IoT 2015 - 2020
6.5.4 CALA Market for Big Data in IoT 2015 - 2020
7 Key Companies
7.1 AGT Group GMBH
7.2 Glassbeam Inc.
7.3 MongoDb Inc.
7.4 netmagic Solutions
7.5 Oracle
7.6 Parstream Inc.
7.7 Splunk Inc.
Figures
Figure 1: Framework for Big Data in IoT
Figure 2: Operational and New revenue Benefits of Big Data in IoT
Figure 3: Global IoT Markets 2015 – 2020
Figure 4: Global markets for Big Data in IoT
Figure 5: Revenue for Big Data Products and Services offered for IoT 2015 - 2020
Figure 6: Markets for Big Data Collection Infrastructure in IoT
Figure 7: Regional Markets for Big Data Collection Infrastructure in IoT 2015 – 2020
Figure 8: Markets for Big Data Storage Infrastructure in IoT
Figure 9: Regional Markets for Big Data Storage Infrastructure in IoT 2015 – 2020
Figure 10: Markets for Big Data Analytics and Applications Infrastructure in IoT
Figure 11: Regional Markets for Big Data Analytics & App Infrastructure in IoT 2015 - 2020
Figure 12: Markets for Big Data as a Service in IoT
Figure 13: Regional Markets for Big Data as a Service in IoT 2015 – 2020
Figure 14: Big Data in IoT by Industry Segment 2015 – 2020
Figure 15: Big Data in IoT for HVAC 2015 – 2020
Figure 16: Regional markets for Big Data in IoT for HVAC 2015 – 2020
Figure 17: Big Data in IoT for Consumer Electronics 2015 – 2020
Figure 18: Regional Markets for Big Data in IoT for Consumer Electronics 2015 - 2020
Figure 19: Big Data in IoT for Healthcare 2015 – 2020
Figure 20: Regional markets for Big Data in IoT for Consumer Electronics 2015 – 2020
Figure 21: Big Data in IoT for Manufacturing 2015 – 2020
Figure 22: Regional markets for Big Data in IoT for Manufacturing 2015 – 2020
Figure 23: Big Data in IoT for Oil and Gas 2015 – 2020
Figure 24: Regional markets for Big Data in IoT for Oil and Gas 2015 – 2020
Figure 25: Big Data in IoT for Transport and Cargo 2015 – 2020
Figure 26: Regional Markets for Big Data in IoT for Oil and Gas 2015 – 2020
Figure 27: Big Data in IoT for Utility 2015 – 2020
Figure 28: Regional markets for Big Data in IoT for Utility 2015 – 2020
Figure 29: Regional Markets for Big Data in IoT 2015 – 2020
Figure 30: North American Market for Big Data in IoT 2015 – 2020
Figure 31: North America Market for Big Data in IoT Products/Services 2015 - 2020
Figure 32: North American Market for Big Data in IoT by Industry Segment 2015 – 2020
Figure 33: EMEA market for Big Data in IoT
Figure 34: EMEA Market for Big Data in IoT Products and Services 2015 – 2020
Figure 35: EMEA Market for Big Data in IoT by Industry Segment 2015 – 2020
Figure 36: APAC Market for Big Data in IoT
Figure 37: APAC Market for Big Data in IoT Products and Services 2015 – 2020
Figure 38: APAC Market for Big Data in IoT by Industry Segment 2015 – 2020
Figure 39: APAC Market for Big Data in IoT
Figure 40: CALA Market for Products and Services offered by Big Data in IoT 2015 - 2020
Figure 41: CALA Market for Big Data in IoT by Industry Segment 2015 – 2020
Tables
Table 1: Global IoT Markets 2015 - 2020
Table 2: Global markets for Big Data in IoT
Table 3: Revenue for Big Data products and Services offered for IoT 2015 – 2020
Table 4: Markets for Big Data Collection Infrastructure in IoT 2015 – 2020
Table 5: Regional Markets for Big Data Collection Infrastructure in IoT 2015 – 2020
Table 6: Markets for Big Data Storage Infrastructure in IoT
Table 7: Regional Markets for Big Data Storage Infrastructure in IoT 2015 – 2020
Table 8: Markets for Big Data Analytics and Applications Infrastructure in IoT
Table 9: Regional Markets for Big Data Analytics and App Infrastructure in IoT 2015 - 2020
Table 10: Markets for Big Data as a Service in IoT
Table 11: Regional Markets for Big Data as a Service in IoT 2015 – 2020
Table 12: Big Data in IoT by Industry 2015 – 2020
Table 13: Markets for Big Data in IoT for HVAC
Table 14: Regional markets for Big Data in IoT for HVAC 2015 – 2020
Table 15: Markets for Big Data in IoT for Consumer Electronics
Table 16: Regional Markets for Big Data in IoT for Consumer Electronics 2015 – 2020
Table 17: Big Data in IoT for Healthcare 2015 - 2020
Table 18: Regional markets for Big Data in IoT for Healthcare 2015 – 2020
Table 19: Regional markets for Big Data in IoT for Manufacturing 2015 – 2020
Table 20: Regional markets for Big Data in IoT for Oil and Gas 2015 – 2020
Table 21: Regional Markets for Big Data in IoT for Transport and Cargo 2015 – 2020
Table 22: Regional markets for Big Data in IoT for Utility 2015 – 2020
Table 23: Regional Markets for Big Data in IoT 2015 – 2020
Table 24: North American Market for Big Data in IoT
Table 25: North American Market for Big Data in IoT Products/Services 2015 – 2020
Table 26: North American Market for Big Data in IoT by Industry Segment 2015 – 2020
Table 27: EMEA market for Big Data in IoT
Table 28: EMEA Market for Big Data in IoT Products and Services 2015 – 2020
Table 29: EMEA Market for Big Data in IoT by Industry Segment 2015 – 2020
Table 30: APAC Market for Big Data in IoT
Table 31: APAC Market for Big Data in IoT Products and Services 2015 – 2020
Table 32: APAC Market for Big Data in IoT by Industry Segment 2015 – 2020
Table 33: CALA Market for Big Data in IoT
Table 34: CALA Market for Products /Services offered by Big Data in IoT 2015 - 2020
Table 35: CALA Market for Big Data in IoT by Industry Segment 2015 – 2020
Table 36: AGT Group
Table 37: Glassbeam Inc.
Table 38: MongoDB Inc.
Table 39: Netmagic Solutions
Table 40: Oracle Corporation
Table 41: ParStream Inc.
Table 42: Splunk Inc.

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