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Capturing Big Data in Social and Detection Systems: Market Opportunities and Challenges 2013 - 2019

Capturing Big Data in Social and Detection Systems: Market Opportunities and Challenges 2013 - 2019

Every large corporation collects and maintains a huge amount of data associated with its customers including their preferences, purchases, habits, travels, and other personal information. In addition to the large volume, much of this data is unstructured, making it hard to manage. This so called “Big Data” is a challenge for industry verticals yet also an opportunity.

The enormous growth potential of Big Data caught the attention of various large players such as IBM and Oracle, to name only a couple, who have developed a key strategic focus in this area and various vertical-specific offerings.

By now many people across various industry verticals understand the basic issues or at least have heard that Big Data is a big deal. However, most of the focus to date has been on the importance of optimizing data management and analytics. There has been very little analysis about challenges and opportunities for capturing Big Data.

This report provides critical evaluation of the front-end of Big Data: capturing data from various sources. This report focuses specifically on social systems as well as obtaining data through sensing/detection with an emphasis on RFID. The report includes Big Data revenue forecasts, predictions about the future of Big Data and social/detection, and related recommendations.

Key Findings:

  • Social, search, and ecommerce systems are a key source of unstructured data
  • Future Big Data systems will rely upon an automated feed from various commercial and private sources
  • Big Data is a big opportunity for those companies that position themselves for future systems integration
  • There is an important role for hybrid Big Data and API solutions that combine the best of both worlds for data
Target Audience:
  • Big Data companies
  • RFID and NFC companies
  • Social media and networks
  • Presence detection companies
  • Fortune 1000 corporations of all types
  • Database and related infrastructure providers
  • ICT infrastructure and service providers of all types
  • Data aggregators, storage and management providers
  • Telecommunications infrastructure and service providers
  • Government including homeland security and law enforcement


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.


1.0 Executive Summary
2.0 Introduction
2.1 What Is Big Data?
2.2 The Important V’s Of Big Data
2.3 Big Data Growth Drivers
2.4 Detection: Sensors, Presence, Location, And More
2.5 Sensors In Consumer Sector
2.6 Sensors In Industry
3.0 Big Data Systems, Processes, And Procedures
3.1 Data Management And Big Data Management Systems
3.2 Big Data Framework & Technologies
3.3 Data Sources For Big Data
3.4 Big Data Analysis Challenges And Solutions
4.0 Major Big Data Companies
4.1 Ibm
4.1.1 About The Company
4.1.2 Big Data Strategy
4.1.3 Big Data Solutions- Products And Services
4.1.4 Related Products
4.1.5 Our Analysis
4.2 Datameer
4.2.1 About The Company
4.2.2 Big Data Strategy
4.2.3 Big Data Products And Solutions
4.2.4 Our Analysis
4.3 Amazon Web Services
4.3.1 About The Company
4.3.2 Big Data Strategy
4.3.3 Products And Solutions
4.3.4 Our Analysis
4.4 Hp- Big Data
4.4.1 About The Company
4.4.2 Big Data Strategy
4.4.3 Products And Solutions
4.4.4 Related Solutions
4.4.5 Our Analysis
4.5 Spotfire
4.5.1 About The Company
4.5.2 Big Data Strategy
4.5.3 Product And Solutions
4.5.4 Related Products
4.5.5 Our Analysis
4.6 Intel
4.6.1 About The Company
4.6.2 Big Data Strategy
4.6.3 Products And Solutions
4.6.4 Related Solutions
4.6.5 Our Analysis
4.7 Emc
4.7.1 About The Company
4.7.2 Big Data Strategy
4.7.3 Products And Solutions
4.7.4 Related Products
4.7.5 Our Analysis
5.0 Big Data Sectors
5.1 Big Data And Healthcare
5.1.1 Known Issue And Challenges
5.2 Big Data And Tracking Industries
5.2.1 Known Issues And Challenges
5.3 Big Data In The Public Sector
5.3.1 Known Issues
5.4 Big Data In Retail Business
5.4.1 Known Issues And Challenges
5.5 Big Data In Finance
5.5.1 Known Issues And Challenges
6.0 Rfid And Social Integration
6.1 Rfid, Big Data Integration& Social Integration
6.2 Nfc Technology Overview
6.3 Current Market Dynamics& Social Media
6.4 Nfc In Mobile Commerce
7.0 Big Data Representation
7.1 Big Data Growth
7.2 Big Data State
7.3 Big Data Trends In Social Apps
8.0 Big Data Market
8.1 Big Data Applications
8.2 Big Data And Rfid
8.3 Big Data Ecosystem
8.4 The Big Data And Rfid Ecosystem
8.5 Big Data - Privacy And Public Policy Issues
9.0 Big Data And Rfid In Payment Systems
9.1 Current Applications
9.2 Big Data And Payment Systems
9.3 Market Analysis
9.4 The Future Of Big Data And Payment Systems
10.0 Big Data And Rfid Consumer Applications
10.1 Big Data And Rfid In Security Systems
10.2 Big Data And Rfid In Healthcare
10.3 Big Data In Rfid Amr Applications
11.0 Big Data & Rfid Tracking Systems
11.1 Rfid Tracking System
11.2 Rfid Implementation Domain And Big Data
11.3 Growth Analysis Of Rfid Tracking Systems
11.4 Predicting The Most Successful Rfid Applications
11.5 Mobile Payments And Tracking System
11.6 Known Issues And Challenges
12.0 Big Data In Rfid Challenges
12.1 Real-time
12.2 Evaluation
12.3 Data Validation
12.4 Data Representation
12.5 Conclusions
13.0 Big Data – Cloud Applications
13.1 Saas And Big Data
13.2 Big Data And Connected Devices
13.3 Big Data Applications
13.4 Future Applications
13.5 The Potential For Google In Big Data, Rfid, And M2m
14.0 Big Data Analytics And Future Applications
14.1 Future Applications Using Big Data And Social Media
14.2 The New Ecosystem
14.3 Advertisement
14.4 User Tracking
14.5 Business Models
15.0 Big Data Forecasts
15.1 Overall Global Big Data Revenue 2013 - 2019
15.2 Global Big Data Revenue By Functional Area 2013 - 2019
15.3 Regional Big Data Revenue 2013 - 2019
16.0 Summary And Recommendations
Figures And Tables
Figure 1 Structured And Unstructured Big Data
Figure 2 Determining Relevancy In Big Data
Figure 3 Top Big Data Business Challenges
Figure 4 The Rich Data Social Science Future
Figure 5 Data Volume Of The Future
Figure 6 Enterprise Control Of Big Data
Figure 7 Api Growth 2005 - 2012
Figure 8 Big Data Social And Detection Landscape
Figure 9 Big Data Growth
Figure 10 Overview Across Verticals
Figure 11 Big Data Ecosystem
Figure 12 Big Data In Everyday Life
Figure 13 Big Data Domains
Figure 14 Big Data & Rfid
Figure 15 Big Data Rfid Lifecycle
Figure 16 Bid Data Verticals
Figure 17 Big Data Components
Figure 18 Big Data Stages
Figure 19 Big Data As A Service (Bdaas)
Figure 20: Global Big Data Revenue: 2013–2019 ($m)
Figure 21: Functional Big Data Revenue: 2013–2019 ($m)
Figure 22: Regional Big Data Revenue: 2013–2019 ($m)
Figure 23 Functional View Of Big Data Relationships
Figure 24 Sdm Systems And Players
Figure 25 Big Data Relationships Among Players
Table 1 - Big Data Market

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