Today 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,
unscheduled and schedule maintenance of vehicles, aviation delays and timings,
downloads and usage of applications, and much more.
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Data generated is typically raw, unstructured, and
in need of processing and presentation to decision makers. This can ultimately
take the form of tables, charts and infographics that will help company
executives to design strategies, performance improvement plans, etc.
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.
IoT in consumer and industrial sectors generate huge amount of data through mobile devices, sensors, events, web access logs, machines, apps, clicks etc.
IoT in consumer and industrial sectors generate huge amount of data through mobile devices, sensors, events, web access logs, machines, apps, clicks etc.
Target Audience:
Regulatory and Policy Groups
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Business and Financial Institutions
Associations and Technology Groups
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Venture Capitalists, Consultants and more
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Exporters, Importers and Traders
Business and Financial Institutions
Associations and Technology Groups
Corporate and Institutional Investors
Government and International Bodies
Lawyers, Bankers, Libraries, Embassies
Patent Offices and Technology Executives
Venture Capitalists, Consultants and more
Management Consulting and Advisory Firms
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Table of Content
1 Executive Summary 3
2 Overview of Big Data in IoT 4
2.1 Big Data Framework for IoT 4
2.2 Big Data in IoT Challenges 5
2.2.1 Storage of Scalable High-volume Data 5
2.2.2 Data Management and Processing Raw Data: Difficult Task in Multivendor Environment 6
2.2.3 Data Security and Personal Information Privacy are the Biggest Hurdles 7
2.1 Big Data Framework for IoT 4
2.2 Big Data in IoT Challenges 5
2.2.1 Storage of Scalable High-volume Data 5
2.2.2 Data Management and Processing Raw Data: Difficult Task in Multivendor Environment 6
2.2.3 Data Security and Personal Information Privacy are the Biggest Hurdles 7
3 Big Data in IoT Trends and
Outlook 9
3.1 Big Data as a Service will take Major Market Share 9
3.2 Flexible and Scalable Revenue Model for Big Data Services will be Most Successful 10
3.1 Big Data as a Service will take Major Market Share 9
3.2 Flexible and Scalable Revenue Model for Big Data Services will be Most Successful 10
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