Connected Cars: Driving the Data Highway

Lionel Grealou Automotive Circular Economy Data Digital 3 minutes

Image credit: PEXEL

Disruptive change

The automotive industry is going through a disruptive change – moving into the era of full connectivity, full mobility, full integration, full immersive experience. First and foremost, connectivity decisions are an important factor in determining the value proposition and the supporting business model for connected car services. There is an increasing demand for connectivity mobility, supported by the ongoing digital revolution. This is opening the door to new services and a wide range of customer experience intelligence.

Approximately 104 million new cars are expected to have some form of connectivity by 2025, with potential revenues reaching US$25 billion, up from US$2.5 billion today.

SBD and GSMA, 2012

Advanced connectivity

Connectivity solutions for in-vehicle services include multiple mobility technologies and follow multiple integration strategies:

  • Embedded: both connectivity and intelligence are built directly into the vehicle.
  • Tethered: the intelligence remains embedded in the vehicle while the connectivity is provided through either an embedded modem with a customer’s SIM, or an external modem using the customer’s mobile device via wired or wireless connection.
  • Integrated: the connectivity is based upon integration between the vehicle and the owner’s handset, in which all communication modules and the intelligence remain on the handset. However, the human machine interface generally remains in the vehicle (but not always).

The future of connected cars will include increasing mobile data associativity and integration. Mobile devices will become control devices to operate connected car features. Autonomous driving will become the norm. Data will flow from smart mobile devices, to smart connected cars and smart cities and infrastructure. Laws and regulations will police how data is managed, who owns what is collected from the connected cars, how driving or not-driving is regulated, etc.

Connectivity technologies, including operative systems, hardware and software, in-vehicle embedded electronics are growing in complexity – which can relate to a number of factors, that need to be managed, such as:

  • Number of in-vehicle embedded systems – including number of different operating systems, programming language and software.
  • Number of electrical and software interfaces and integration solutions.
  • Number of sensors and data sources.
  • Number of lines of code (though not a qualitative measure).
  • Number of changes / updates.

Digital and social PLM for data complexity management

Design, engineering and manufacturing are increasingly required to adapt to fast-changing technologies. Product Lifecycle Management (PLM) – which already extends to (or include) Product Data Management (PDM), Electrical-CAD (ECAD), software, electrical, embedded and electronics (EEE) – helps manage the virtual and digital product lifecycle, including managing EEE data complexity and how it integrated with the other functions. 

Digitization is a step change even greater than the internet. Exponential technology advances, greater consumer power and increased competition mean all industries face the threat of commoditisation. 

Ernst & Young, 2011

Disruptive changes in the automotive industry require digital PLM, big data, business analytics and social media in order to marry up:

  • Internal data sources: engineering, manufacturing, financial , procurement, sales and marketing.
  • External data sources: customer experience data, their usage and requirements, connected objetcs – in their various forms of interpretation.

Data will combine multiple sources from ER&D centers, factories, dealerships, consumer smartphones, smart connected vehicles, smart cities. Big data require interpretation of the business decisions and insight required to make these decisions – which, in turn, require information which come from data.

Key challenges with connected cars include legislation and monitoring of critical data. This links to fundamental data ownership and security questions, such as: Who owns the data? Where is data located? Which data is valuable? How can data be transmitted for analysis? How can this data be overlaid with other types? How do we address data security and consumers’ privacy concerns? How is data shared and feedback into the engineering and manufacturing process? How much data management and intelligent interpretation can be automated? Can machines help human learn and interpret data?

What are your thoughts?


This post was originally published on LinkedIn on 20 March 2016.

About the Author

Lionel Grealou


Lionel Grealou, a.k.a. Lio, helps original equipment manufacturers transform, develop, and implement their digital transformation strategies—driving organizational change, data continuity and process improvement, managing the lifecycle of things across enterprise platforms, from PDM to PLM, ERP, MES, PIM, CRM, or BIM. Beyond consulting roles, Lio held leadership positions across industries, with both established OEMs and start-ups, covering the extended innovation lifecycle scope, from research and development, to engineering, discrete and process manufacturing, procurement, finance, supply chain, operations, program management, quality, compliance, marketing, etc.

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