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 des matières

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

Table des figures

Table des figures

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

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

Secteur géographique



  • 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
  • Tesla
  • TomTom Telematics
  • Tunstall


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

Autres détails

  • Référence : M17330MRA
  • Livraison : on the DigiWorld Interactive platform
  • Langues disponibles : Anglais
  • Tags : automotive, big data, data monetisation, health, iot, utilities

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