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市場調查報告書

超越資料收益化 - OTT的機會:金融、零售、通訊、連網型物體

Data Monetisation - Opportunities Beyond OTT: Finance, Retail, Telecom, Health and Automotive

出版商 IDATE DigiWorld 商品編碼 322279
出版日期 內容資訊 英文 70 Pages
商品交期: 最快1-2個工作天內
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超越資料收益化 - OTT的機會:金融、零售、通訊、連網型物體 Data Monetisation - Opportunities Beyond OTT: Finance, Retail, Telecom, Health and Automotive
出版日期: 2016年10月17日 內容資訊: 英文 70 Pages
簡介

本報告提供主要各產業資料收益化選擇調查分析、巨量資料市場概要、資料收益化的機會、各產業現狀(高價商品、搭配出售銷售、直接廣告/行銷、附加價值服務、中介、資料轉賣)、各產業的現實機會等相關的系統性資訊。

第1章 摘要整理

第2章 調查手法、定義

第3章 各產業資料收益化選擇

  • 巨量資料概要
    • 市場內容
    • 市場結構
    • 市場規模
  • 各產業機會
    • 各產業巨量資料的主要機會
    • 資料收益化和產業
    • 產業間的差異
  • 跟整合業者的作戰

第4章 資料收益化和金融

  • 收集資料的種類
  • 隱私政策
  • 主要機會

第5章 與資料收益化零售

  • 零售的收集資料的種類
  • 隱私政策
  • 主要機會

第6章 資料收益化和電信業者

  • 收集資料的種類
  • 隱私政策
  • 主要機會

第7章 資料收益化和智慧產品

  • 簡介
  • 收集資料的種類
  • 隱私政策
  • 機會

圖表

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目錄
Product Code: M16196MR

This report describes the existing but fast-shifting degree of data monetisation in the verticals of finance, telecom, healthcare and automotive. Woven through all this is the thread of the influence of such cross-cutting trends as social networks and user commitment or hesitation.

A rich palette of examples of players' early moves and latent plans illustrates the vibrancy of the issue.

The clear potential for each vertical is examined in detail, as are bumps on the road ahead. Privacy, especially in finance and healthcare, as well as in shopping and telecom habits, can be cited as one example.

Table of Contents

1. Executive Summary

2. Methodology & definitions

3. Data monetisation options for verticals

  • 3.1. Introduction to big data
    • 3.1.1. Market description
    • 3.1.2. Market structure
    • 3.1.3. Market size
  • 3.2. Opportunities for verticals
    • 3.2.1. Major opportunities of big data for verticals
    • 3.2.2. Data monetisation and verticals
    • 3.2.3. Differences between verticals
    • 3.2.4. The battle with platforms

4. Data monetisation and the telcos

  • 4.1. Types of data collected
  • 4.2. Privacy policies
  • 4.3. Main opportunities

5. Data monetisation and the health industries

  • 5.1. Introduction
  • 5.2. Types of data collected
  • 5.3. Privacy policies
    • 5.3.1. Regulations
    • 5.3.2. Manufacturers' privacy policies
  • 5.4. Main opportunities
    • 5.4.1. Improvement of healthcare
    • 5.4.2. Regulation-compliant data custodian
    • 5.4.3. Development of new services associated with products (servicisation)
    • 5.4.4. Insights and aggregated data sales

6. Data monetisation and the social networks

  • 6.1. Types of data collected
  • 6.2. Privacy policies
  • 6.3. Main opportunities
    • 6.3.1. Ad networks
    • 6.3.2. APIs
    • 6.3.3. Insights and aggregated data sales

7. Data monetisation and the automotive industry

  • 7.1. Types of data collected
  • 7.2. Privacy policies
  • 7.3. Main opportunities
    • 7.3.1. Connected-car service
    • 7.3.2. Intelligent Transport System
    • 7.3.3. Car-as-a-service
    • 7.3.4. Insights and aggregated data sales
    • 7.3.5. Tools and APIs

Tables

  • Table 1: Main potential uses of big data by vertical players, by type of activity
  • Table 2: Key options for data monetisation
  • Table 3: Summary view of major opportunities for the four verticals
  • Table 4: Opportunities for verticals around data monetisation
  • Table 5: Key options for data monetisation for telcos
  • Table 6: Key options for data monetisation
  • Table 7: Examples of data available to social network platforms
  • Table 8: Key options for data monetisation in social network
  • Table 9: Key options for data monetisation in social network
  • Table 10: Car-sharing services by different players

Figures

  • Figure 1: Variety of data sources
  • Figure 2: Type of data used by big data users
  • Figure 3: Key application areas of big data projects
  • Figure 4: Key use cases for big data
  • Figure 5: Global text analytics market: segmentation and forecast, 2013-2020
  • Figure 6: Big data value chain
  • Figure 7: Big data landscape
  • Figure 8: Worldwide big data revenue forecasts, 2011-2020
  • Figure 9: State of big data investments
  • Figure 10: State of big data investments
  • Figure 11: Main challenges with big data
  • Figure 12: Data characteristics per vertical
  • Figure 13: Adoption of big data per vertical
  • Figure 14: Respective positioning of verticals regarding data monetisation
  • Figure 15: Types of data collected by telcos
  • Figure 16: Highly valued data types to telcos
  • Figure 17: "How we use your information" - privacy policy of O2
  • Figure 18: "How we share your information" - privacy policy of O2
  • Figure 19: O2 “Bolt Ons' allow for additional sales on top of standard tariffs
  • Figure 20: Open carrier-billing API by Orange
  • Figure 21: Direct-to-bill on Facebook: some operators offer easy two-step process
  • Figure 22: Orange Datavenue platform
  • Figure 23: Swisscom Mobile ID service
  • Figure 24: The business cycle of cross-screen ads of AT&T AdWorks
  • Figure 25: How PrecisionID works
  • Figure 26: Verizon statement on user opt-out from advertising
  • Figure 27: i-concier service by NTT DOCOMO
  • Figure 28: Screenshot of a Smart Steps insight result
  • Figure 29: AT&T M2X for M2M applications
  • Figure 30: Fitbit Surge allowing heart rate and sleep monitoring
  • Figure 31: IntelliVue Cableless/wearable patient monitor by Philips
  • Figure 32: Different sensors on the human body
  • Figure 33: Type of data collected at Fitbit
  • Figure 34: Use of personal data at Fitbit
  • Figure 35: Exception of the share of identifiable data at Fitbit
  • Figure 36: The priority of disease treatment by personalised medicine, in two years
  • Figure 37: Technology enabler for data-driven personalised medicine
  • Figure 38: Validic data integration platform (clinical, fitness and wellness)
  • Figure 39: Incentive points gained on Walgreens Balance Reward app to Fitbit users
  • Figure 40: Philips Lifeline portfolio
  • Figure 41: Pricing of Philips Lifeline
  • Figure 42: Data resale business model
  • Figure 43: Benefits and rewards
  • Figure 44: Vitality Status levels linked with different saving rates
  • Figure 45: Withings Pulse
  • Figure 46: ELSIE genome queries for different diseases, conditions and therapeutic agents
  • Figure 47: Fitbit API Terms of Service regarding license
  • Figure 48: Type of information that Facebook collects
  • Figure 49: How is data being used and shared in Facebook?
  • Figure 50: With whom Facebook selects to share user data
  • Figure 51: How users select who shares their data
  • Figure 52: Range of confidence in different players in terms of data privacy and security
  • Figure 53: Users' conditions for sharing data
  • Figure 54: Demographics for audience targeting
  • Figure 55: Facebook advertising revenues (2009 - 2015)
  • Figure 56: Facebook promotion of Skyscanner
  • Figure 57: Blue Jay for law enforcement
  • Figure 58: Social data analytics market
  • Figure 59: How General Motors uses the automobile data
  • Figure 60: How automobile data is shared
  • Figure 61: Commitments on data control
  • Figure 62: Preferred parties for connected-car data sharing
  • Figure 63: OnStar connected-car services
  • Figure 64: OnStar value proposition
  • Figure 65: Cooperative ITS Corridor - joint development by the Netherlands, Germany and Austria
  • Figure 66: Surge pricing with Uber
  • Figure 67: Uber shares user data with Starwood Hotels & Resorts
  • Figure 68: Usage-based insurance solutions by Vodafone and Cobra
  • Figure 69 Skypatrol Defender vehicle financing solution

List of players reviewed

The data monetisation activities of the following companies and brands are reviewed in this report:

  • American Express
  • Apple
  • AT&T
  • Axa
  • Bank of America
  • Barclays
  • Cardlytics
  • Citigroup
  • Cobra
  • Coimbra Genomics
  • CountAbout
  • DataSift
  • Discovery
  • Early Warning
  • Facebook
  • Fitbit
  • General Motors
  • GNIP
  • Google
  • John Hancock
  • Intuit
  • JPMorgan Chase
  • Macy's
  • MasterCard
  • Mint
  • NTT DOCOMO
  • Open Bank Project
  • Opera Mediaworks
  • Oracle
  • Orange
  • Philips
  • Plaid
  • RunKeeper
  • SFR
  • SingTel
  • Skypatrol
  • Skyscanner
  • Sprint
  • Swisscom
  • Telefónica/O2
  • Twitter
  • Uber
  • Validic
  • Verizon
  • Vibes
  • Visa
  • Vitality
  • Vodafone
  • Walgreens
  • Withings

Slideshow contents

Data monetisation options for verticals

  • Big data: disruptive concept for data monetisation
  • Big data technologies and market structure
  • Big data market size

Opportunities for verticals

  • Adoption of big data by verticals
  • Opportunities for verticals
  • Differences between verticals
  • Finance
  • Telecom
  • Healthcare
  • Social networks
  • Automotive

Outlook

  • Major opportunities for the verticals
  • The battle with platforms
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