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Data monetisation

Market report - 17/10/2016 Data monetisation

Opportunities beyond OTT: finance, telecom, social networks, health and automotive

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.


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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
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
• Telefσnica/O2
• Twitter
• Uber
• Validic
• Verizon
• Vibes
• Visa
• Vitality
• Vodafone
• Walgreens
• Withings
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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