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IoTにおけるデータ収益化 - ユーティリティ、自動車、医療:コスト削減額と収益

Data Monetisation in IoT - Utilities, Automotive and Health Verticals: Calculating the Cost Savings and Revenue Generation for 2020

発行 IDATE DigiWorld 商品コード 322279
出版日 ページ情報 英文 42 Pages
納期: 即日から翌営業日
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IoTにおけるデータ収益化 - ユーティリティ、自動車、医療:コスト削減額と収益 Data Monetisation in IoT - Utilities, Automotive and Health Verticals: Calculating the Cost Savings and Revenue Generation for 2020
出版日: 2018年01月02日 ページ情報: 英文 42 Pages
概要

当レポートでは、IoTにおけるデータ収益化について調査分析し、主として産業分野別に、実際のコスト削減額と新しい収益額について、体系的な情報を提供しています。

第1章 エグゼクティブサマリー

第2章 調査手法と定義

第3章 イントロダクション:産業分野別のデータ収益化オプション

  • ビッグデータの概要
  • 産業分野別のビッグデータの主要機会
  • IoTにおける産業分野別の発展

第4章 ユーティリティにおけるIoTデータの収益化

  • 市場概要
  • 利用可能データ
  • 促進要因と障壁
  • コスト削減額と新しい収益機会

第5章 自動車におけるIoTデータの収益化

  • 市場概要
  • 利用可能データ
  • 促進要因と障壁
  • コスト削減額と新しい収益機会

第6章 医療におけるIoTデータの収益化

  • 市場概要
  • 利用可能データ
  • 促進要因と障壁
  • コスト削減額と新しい収益機会
目次
Product Code: M17330MRA

Summary

This report goes beyond the hype, to calculate the real cost savings and new revenue generation in both value (billion EUR) and as a percentage of the market for 2020.

On the one hand the IoT market is growing, mainly vertical per vertical, and with it the amount of data generated through the various connected things.

On the other hand the concept of big data is transforming the way data is used in the verticals, beyond the early successes of the large OTTs such as Google, Facebook and Amazon.

Together, there is genuine excitement as to the monetisation potential when combining this exponential growth of data together with evolution of big data analytics. The three verticals examined are the utilities, automobile and health verticals.

Key questions:

  • What is the real amount of cost savings and revenue generation obtainable through the use of IoT data?
  • ...not just as theoretical stories, but as concrete figures in billion EURs as well as a percentage of the market?
  • Data monetisation; is it all hype, and is a reality check required?

Table of Contents

1. Executive Summary

2. Methodology & definitions

3. Introduction: data monetisation options for verticals

  • 3.1. Introduction to big data
  • 3.2. Major opportunities of big data for verticals
  • 3.3. Vertical development in IoT

4. The monetisation of IoT data in utilities (electricity, gas and water meters)

  • 4.1. Market overview
    • 4.1.1. Main application: the smart meter
    • 4.1.2. Value chain
  • 4.2. The data available
  • 4.3. Drivers and barriers
    • 4.3.1. Drivers
    • 4.3.2. Barriers
  • 4.4. Cost savings and new revenue opportunities for 2020
    • 4.4.1. Cost-saving opportunities for 2020
    • 4.4.2. New revenues for 2020

5. The monetisation of IoT data in automotive (connected cars)

  • 5.1. Market overview
  • 5.2. The data available
  • 5.3. Drivers and barriers
    • 5.3.1. Drivers
    • 5.3.2. Barriers
  • 5.4. Cost savings and new revenue opportunities for 2020
    • 5.4.1. Cost-saving opportunities for 2020
    • 5.4.2. New revenues for 2020

6. The monetisation of IoT data in healthcare (remote patient monitoring)

  • 6.1. Market overview
  • 6.2. The data available
  • 6.3. Drivers and barriers
  • 6.4. Cost savings and new revenue opportunities for 2020
    • 6.4.1. Cost-saving opportunities for 2020
    • 6.4.2. New revenues for 2020

List of tables and figures

Tables

  • Table 1: Main potential uses of big data by vertical players, by type of activity
  • Table 2: Main applications in the utility industry
  • Table 3: Total cost savings and new revenues through data for 2020 in the utility vertical
  • Table 4: Breakdown of cost-saving calculation through IoT data for utility industry
  • Table 5: Breakdown new revenue calculation through IoT data for utility industry
  • Table 6: Summary of key elements for the connected-car data market
  • Table 7: Total cost savings and new revenues through data for 2020 in the automobile vertical
  • Table 8: Breakdown of cost-saving calculation through IoT data for automotive industry
  • Table 9: Breakdown of new revenue calculation through IoT data for automotive industry
  • Table 10: Total cost savings and new revenues through data for 2020 in the health vertical
  • Table 11: Breakdown of cost-saving calculation through IoT data for health industry
  • Table 12: Breakdown new revenue calculation through IoT data for health industry

Figures

  • Figure 1: Variety of data sources
  • Figure 2: Data characteristics per vertical
  • Figure 3: Breakdown of the IoT market by vertical, 2016-2030 (Communication market excluded*)
  • Figure 4: Smart metering (and potentially smart grid) services value chain
  • Figure 5: Functionalities enabled on the entire smart grid distribution chain
  • Figure 6: Top benefits for a smart home system
  • Figure 7: Main players of the automotive value chain
  • Figure 8: Intel predictions for the data generated by future vehicles.
  • Figure 9: Commitments on data control
  • Figure 10: How General Motors uses the automobile data
  • Figure 11: Preferred parties for connected-car data sharing
  • Figure 12: How automobile data is shared
  • Figure 13: Would you allow your car to track your location and report it anonymously, to enable (for instance) your carmaker to improve the next generation of your car?
  • Figure 14: Cost breakdown within the automotive industry
  • Figure 15: Percentage of total R&D spend by industry sectors in 2016
  • Figure 16: General Motors OnStar connected-car service portfolio
  • Figure 17: OnStar plans and pricing
  • Figure 18: UBI interest growing with insurance discounts
  • Figure 19: Player shares of online advertising revenue, 2016
  • Figure 20: Initiatives by giant players in connected healthcare market
  • Figure 21: Different sensors on the human body
  • Figure 22: Who owns medical records?
  • Figure 23: End-to-end interoperability solutions advised by Continua Design Guidelines
  • Figure 24: Global installed base of connected healthcare devices 2015-2021
  • Figure 25: Philips HealthSuite digital platform

Geographic area & Players

World

Actors

  • Accenture
  • Adobe
  • Allianz
  • Alstom
  • Amazon
  • American Council for an Energy Efficient Economy
  • BMW
  • Capgemini
  • Christus St. Michael Health System
  • Cisco
  • EDF
  • Electronic Data Systems
  • Enel
  • Energy Information Administration
  • Engie
  • E-On
  • European Commission
  • Facebook
  • Genenia
  • General Electric
  • General Motors (GM)
  • Google
  • Hadoop
  • HL7
  • Hughes Electronics Corp.
  • IBM
  • IBM Continua
  • Itron
  • LoRa
  • LPWA
  • M2O City
  • MapReduce
  • Medtronics
  • Microsoft
  • National General Insurance
  • National Health Service
  • Oracle
  • Philips
  • PLC
  • PSA
  • Sagemcom
  • Salesforce
  • Schneider Electric
  • Sensus
  • SIGFOX
  • Tesla
  • TomTom Telematics
  • Tunstall

Slideshow

Key figures and takeaways

  • IoT data monetisation: cost saving effects greater than new revenue generation

Introduction: Big data and the development of IoT in verticals

  • Big data: disruptive concept for data monetisation
  • Vertical developments in IoT; the choice of Utilities, Automotive and Health

Cost savings & revenue generation in the verticals

  • Utilities overview: smart meter implementation driven principally by regulation
  • Utilities: Cost savings 1.4% of market, revenue generation 0.025% of market
  • Automotive overview: connected cars to provide ever increasing pool of data
  • Automotive: Cost savings 0.9% of market, revenue generation 0.3% of market
  • Health overview: market in transition with new entrants challenging the traditional
  • Health: Cost savings 1.15% of market, revenue generation 0.4% of market
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