Data Driven Mobility – Improving Mobility Systems Through Holistic Data Utilization

Individual Mobility. What does it take to bring you from A to B?

From an individual’s point of view, mobility simply is the possibility to be mobile, to go from one place to another – no matter where these places are. Typical criteria to evaluate this possibility are availability, accessibility, possible destinations, time to destination, cost, safety, comfort, reliability, sustainability etc.

To do so, you can either walk or use a means of transport – which can be any kind of vehicle from bicycle to car, from boat to drone, from e-scooter to airplane, from subway to cable car or even horses and donkeys.

Dropping the relatively rare (and in the context of this article irrelevant) case that you employ a personal chauffer, captain or pilot and are a passenger in your own vehicle, this leaves you with two options: You must either operate your own vehicle or use one rendered to you through a mobility service – such as public transport, ride hailing, bike sharing, airplanes or whatever.

Infrastructure. What you just expect to be there.

A precondition for the proper usage of these vehicles is a functioning infrastructure. Mobility infrastructure comprises a broad bandwidth of things and services, e.g.:

  • The structure the vehicle needs to operate (such as walkways, bike lanes, streets, rails, tunnels, bridges, waterways or air routes)
  • Means to enter or leave the vehicle (parking spaces, parking structures, stations, ports, airports etc.)
  • Traffic control elements (such as road markings, traffic signs, traffic lights, barriers, access control, traffic and parking surveillance etc.)
  • Energy provision (such as fuel stations, chargers or overhead lines)
  • Structures and services to maintain and repair vehicles as well as infrastructure
  • Data networks (such as mobile internet access)
  • Regulatory framework including legal requirements for vehicles and mobility services as well for their operation, tolls and taxes

In Short: To be mobile, you need your own vehicle or access to a mobility service. And in both cases, you depend on the availability of the appropriate infrastructure.

Collective Mobility. Unfortunately, other people want to be mobile, too.

Securing all these requirements alone would be tough enough, but as we all experience on a daily basis: How well one person can realize his or her mobility needs also depends strongly on the mobility patterns of all others. Jammed streets, overcrowded subways, limited availability of sharing vehicles, scarcity of parking spaces, local and global emission limits keep you from doing what you would do if you would be the only one out there. Why the heck must all others use this road, bus or service when I want to?

How well everyone in a given area can fulfill their mobility needs at the same time is what we call Collective Mobility. Securing and optimizing Collective Mobility is one of the primary regulatory tasks of cities, regions or countries and means nothing less than controlling the interplay of all vehicles being used, mobility services being rendered, and infrastructure being operated and maintained in a given mobility system. And we all know how rudimentary especially big cities handle this challenge today.

From opinions to knowledge. Big Data helps understanding.

A promising strategy to improve in this endeavor is utilizing what I call the “Internet of Mobility”: Vehicles, users, infrastructure – they all are getting more and more equipped with sensors (and hence create more and more data) and become more and more connected to the internet. There’s hardly one person on the street without a smart phone, service systems share their data and most importantly the sum of connected cars out there on the roads, which – from a data analyst’s point of view – represent nothing less than a huge, densely distributed network of powerful, mobile, and somewhat over-motorized sensor clusters constantly transmitting really big data ready to be collected and analyzed.

Such data is already available und used today, but at comparably low quantity and quality, and we are only at the beginning of holistically utilizing it. To picture what you get from conventionally connected cars compared to having digital twins: Most cars out there today leave you a little sticky note at the fridge door saying, “I drove 54 kilometers today, my tank is half full, all doors are locked, and I am generally doing fine.” However, cars with state-of-the-art connectivity have you on the phone 24/7, telling you constantly about each and every feeling and perception they have.

It is this increase and improvement of available data and especially the consolidated analysis of vehicle, user, and infrastructure generated data that allows the holistic optimization of mobility systems in the future. Here, I see mainly five main dimensions:

1. Improve Vehicle Operations

Real-time knowledge of infrastructure conditions and availability

  • facilitates parking and fueling/charging,
  • enables the early detection and even prediction of mobility-inhibiting factors such as traffic jams, potholes, slippery road surfaces or any other hazards, and
  • is the basis for any kind of autonomous driving.

2. Improve Mobility Services

Time- and location-based knowledge of user behavior and service utilization allows providers to

  • select the optimum vehicle and features for their service offers,
  • optimize both number and distribution of the vehicles used in their sharing or ride hailing schemes (including public transport), and especially
  • make mobility services as a whole more attractive (e.g., than driving your own vehicle) by optimizing the interplay between various offers (e.g., ride hailing, public transport and parking structures).

3. Improve Vehicle Condition

Real-time knowledge of all vehicles’ technical condition allows

  • detection of technical problems and thus facilitation of their solving, and
  • prediction of maintenance or repair needs and thus keeping vehicles smoothly running whilst avoiding breakdowns.

4. Improve Infrastructure Operations

Environmental data provided by connected cars allows

  • detection and prediction of infrastructure maintenance needs (e.g., broken traffic lights, worn road markers, damaged streets, bridges, or structures),
  • detection and prediction of general traffic capacity overload.

5. Improve Mobility System as a Whole

Combining and analyzing data rendered by all users, infrastructure and vehicles in a given mobility system allows the responsible authorities to

  • monitor, predict and control traffic flow and emissions,
  • decide targeted measures to improve the mobility system with regards comfort, safety and costs based on the received insights, and
  • detect and follow up on traffic violations.

As with all other forms of digital transformation, this approach comes with a twofold challenge: Firstly, the technical realization of the data utilization cycle (generate, transfer, aggregate, analyze, act, measure). Secondly, the persuasive and convincing efforts required to get all people involved supporting this change – sometimes letting go processes they are not only used to for years but have helped to establish and thus are personally attached to.


You think you really understood digital transformation …

.. but you still have such a weird feeling that your future son-in-law says his job is being a blogger.

… but you still don’t believe anyone can make real money from playing computer games.

… but you don’t understand how digitalization could ever change your customers’ expectations.

… but you wonder why the brand you successfully established twenty years ago suddenly has competitors you didn’t even know last year.

… but you still expect your employees to always do their jobs perfectly.

… but you still feel somehow intellectually superior when you scroll through your printed daily newspaper in the morning.

… but you still want to keep your CD collection.

… but you still think your kids can’t hurt themselves on the internet.

… but you still wonder how the formerly isolated madmen suddenly all come together.

Are we really as digital as we think?

Of course, we all feel like we are at the forefront of the digital transformation: we don’t write letters anymore, we chat on WhatsApp instead of making phone calls, we no longer go to the department store but order online from Amazon, book parking and bus tickets with our smartphone, pay casually with our AppleWatch at the supermarket checkout, and, thanks to Netflix, now hardly need grandparents’ TV programs.

In job and education, Covid 19 has led us to be able to conduct business meetings, lectures and school lessons almost confidently online (even if the latter certainly still have some catching up to do here). And some people are already analyzing their production and sales data with AI tools and thus get new and often surprising insights.

Nevertheless, the question remains: have we really understood what the current digital transformation means? How does it differ from the digitalization of recent decades, what opportunities and risks does it entail? But also: what cultural and social change does it bring? And are we prepared to join these changes, or are we not secretly trying to at least partially maintain the status quo we are so familiar with?

What is so different now?

is the transformation of existing products and processes through digital technologies – across industry, business, politics and society – which can range from their simpler application to the enrichment of content and functions to their complete replacement by radically new solutions.

In retrospect, the creation and editing of texts, tables, images, drawings, music and videos on computers can be seen as the first stage of digitization. The worldwide connection of these computers, first stationary, then also mobile, represents stage two and three of digitization and has led to completely new dimensions in communication and cooperation. As a fourth stage, the landslide-spreading of smartphones has not only led to their users being able to access the internet anytime, anywhere; above all, they have enabled behavioural and location-related offers through their cameras, microphones and position sensors and the mass upload of the data generated by them.

Each of these stages has not only produced new solutions and new players, but has also heralded the end of many long-established products, companies and professions. Victims of the first stage include, for example, the manufacturers of cameras, tapes, typewriters or anything necessary for technical drawing. The second and third stages have severely restricted conventional postal services, faxes or long-distance calls, completely abolished services such as telex and rendered data carriers such as floppy disks, CDs or DVDs superfluous. Stage four has dug the water out of many conventional service providers via Location Based Services, such as the taxi companies through app-based ride-hailing services.

The fifth stage we are currently in (which does not mean at all that the previous stages would be even halfway completed) is technically defined by the ability to collect and structure huge amounts of data continuously provided by the growing number of computers, smartphones, connected vehicles and other things (so-called “big data”) and then analyze it with the help of AI-based analytics tools to come up with valuable information: What conditions really depend on whether a particular product is purchased? Which functions of a vehicle are actually used most often – and which are not? Which content of a website is attractive and leads to online purchase – and which ones are not? On the one hand, customer- and requirement-based offers can be derived from such analyses (such as the well-known “customers who have purchased product X have also purchased products Y and Z”); on the other hand, advanced analytics tools make it possible to predict the behavior of people and technical systems with ever greater accuracy. This applies to the experience-based forecasting of traffic jams, maintenance requirements of networked machines, plants or vehicles, or even of human misconduct. In medicine, data analysis supports the early identification of diseases, in finance the prediction of market or price movements. And some online retailers even claim that they can not only predict their customers’ future needs by analyzing their buying behavior, but also, for example, predict a divorce by comparing behavioral patterns, before the parties have even made the decision.

In addition to all these technical possibilities, however, digitalization also brings with it a significant social change, especially at this current stage: the growth of a “digital culture”, the work and lifestyle of a generation that has grown up with digitization (as well as some older people who have adopted this style) and which differs significantly from the usual – as the following examples are to illustrate . :

  • Low product and brand loyalty. Those who buy quickly with “one click in” are also gone with “one click out” just as quickly. Loyalty is not expected to be rewarded. New entrants to the market are viewed with interest and enthusiasm rather than with scepticism and doubts about quality and reliability. This also applies to loyalty to their employer.
  • Broad level of information: Customers are not only fully informed about the products and services they are interested in, but also about their suppliers. Often no purchase advice is required, because the customer has informed himself so well in advance that he knows more about the product in question than the seller. Digitals are value-oriented, bright, and sensitive: those who are associated with the exploitation of local workers or the cause of environmental damage in internet forums are quickly out of the running despite having attractive offers.
  • Feedback culture: Digitals are used to getting and giving feedback quickly and easily. A like here, three out of five stars there. The fact that the experiences of a dissatisfied customer are published in internet forums and networks around the world just minutes later, and how best to govern such cases, is still new territory for many established companies.
  • Transparency: Those who want to use digital data should not hope for their quick approval, but should clearly highlight the added value they gain from the transfer of their data. In the same way, if you want to lead digitals as their manager, you should express your expectations clearly and stick to agreements.
  • Benefits instead of owning: Those who have grown up with streaming services instead of their own CD or DVD collection also have less need to own things like tools, cars or bicycles. Digitals are much more receptive to all types of “X as a Service”.

Why is this so relevant for companies: because they are confronted with these digitals in four different ways today: as enlightened customers of their products and services, as sophisticated and not unrestrictedly loyal employees and executives, as factual politicians and legislators who set the legal framework for digital products and processes, and last but not least as critical and rather thematic than partisan voters of these politicians. Dealing intensively with the content and impact of digital culture is therefore a strategic must for companies.

What are the opportunities, risks and changes?

The ability to predict the behaviour of people and systems obviously offers a variety of entrepreneurial opportunities: those who know exactly what the market wants, how their products are actually used and what condition they are in, and thus can offer their customers individual service and product offerings, are not only clearly at an advantage of competition, but can also be able to improve their entire value creation process from development to production and distribution to service and recycling, and thus plan and deploy their capacities in a much more effective and efficient manner:

  • Targeted product management including individual product and service offerings
  • Design to factual requirements (no over- or under-dimensioning)
  • Early detection of design and production defects
  • Individual forecasting of maintenance requirements
  • Detection of repair needs
  • Managed return / recycling procedures at the end of life

Precisely because this increases the attractiveness of the offers for customers so enormously, competitors who fail to enter these technologies and exploit their potential will lose touch relatively quickly. One aspect often overlooked in the euphoria about the obvious opportunities, which can not only slow down the desired digital transformation in the company, but in fact stop it, is the corporate and management culture. While digital change is already relatively broadly anchored in society, executives and employees of established companies often still struggle with it. The use of big data and AI and the associated digital transformation are seen in part as a massive threat to their often laboriously worked-out role and importance in the company, with three aspects of fear in the foreground:

  1. Devaluation of personal success: Many executives and specialists see the established – and indeed successful – products, processes and procedures of the past as a one of the main reasons for their personal success, and possible changes as an attempt to devalue them, as well as a betrayal of their own values.
  2. Loss of “dominance knowledge”: The basis for the use of big data and analytics in the enterprise is the merging of all available data into a data lake or digital twin accessible to all parties. But it is precisely this disclosure that is seen as a danger. “Only my department and I determine the exact sales figures. If you want to know how many of which products were sold in which markets, you have to come to me and ask me to do so. I will certainly not make that data available to everyone now. In addition, our mistakes would be immediately transparent to everyone.”
  3. Rationalization of one’s own workplace: As in manufacturing through automation, the use of big data and analytics also eliminates the need for jobs in other areas – but here those of specialists and executives. The assessment and forecasting of sales or usage data, for example, has long been the responsibility of highly specialized and highly regarded departments in the companies, whose expertise can now be increasingly replaced by powerful analytics tools – which also generate the required forecasts at the touch of a button, at any time and in a comprehensible manner.

What to do?

If the digital transformation is to be successful in the long term, it must never be limited to the introduction of advanced IT technologies, but must also drive forward the necessary change of internal processes and corporate culture in the sense of a change program prescribed and demonstrated by the companies upper management. This includes creating understanding and perspectives, and supporting the personal change of each person concerned individually through qualification. However, this also includes consistently dealing with those managers who are closing themselves off from change for personal reasons and thus ultimately fail to take the decisions necessary for change and the long-term maintenance of competitiveness.

Making Connected Cars Work. The Next Dimension of Automotive Development.

Automotive Development.

From product development’s point of view, a passenger cars certainly have always been one of the bigger challenges. In contrast to the majority of other product categories, their customer relevant functions and properties (e.g. agility, passive safety, cabin comfort or exterior and interior design) are not fulfilled by one specific vehicle component (such as engine, body, seats, drivetrain or chassis), but by a complex interplay of mostly all these components. Over time however, the boundaries of the automotive system to be considered during development have gradually expanded.

Level 1: When cars were just cars. The classic art of complete vehicle integration.

Development of a premium passenger has always been carried out as a sequence of development cycles. Starting from the initial vehicle concept, each of these cycles included dimensioning and designing the components, testing and optimizing them using virtual or real prototype parts, and then merge them into a complete vehicle – again virtual or real – in order to test and optimize it. This vehicle integration process includes the proper positioning of all components within the complete vehicle in consideration of available space and required clearance (so-called geometric integration), validating manufacturability (so-called production integration) and last not least ensuring the desired vehicle properties and functions mentioned above (so-called functional integration).

Both the electric components (such as lights, window lifters or power steering) and the very few electronic devices (such as engine control units, navigation systems or anti blocking systems) were separate systems, sharing power supply but running independently. The conventional cars that came out of this first level vehicle development process were highly integrated and optimized “systems of electromechanical components”.

Level 2: System integration. How to develop computers on wheels.

This approach proved itself very apparently insufficient when after the millennium premium automakers made almost every component software controlled and interlinked all these electronic systems to a “system of systems”, however without including proper validation of the electronic functionality in their development processes. As a result, these systems lacked the appropriate technical maturity, and early customers regularly despaired of the rather unpredictable behavior of their vehicles. Especially the luxury class flagships, filled up to the roof with the latest electronic features, surprised their owners by suddenly and unexpectedly opening windows and sunroofs, switching wipers on and off, or stalling the engine.

To quickly come out of this rather embarrassing (and costly) situation, automakers hurried to enhance their existing vehicle integration process by a system integration process to comprehensively validate hardware and software together – first on component level, then on domain level, and eventually on complete vehicle level. While control software changes on component level had typically been executed uncontrolled before, they now had to abide by a strict release process that led to thoroughly validated hardware-software sets (so-called integration levels). Ultimately, the desired system reliability of this “computer on wheels” was secured before the car left the manufacturing plant and was handed over to the customer.

Level 3. Connected cars. Things in the internet of things.

Even though no one never called it so, car radio was the first data based feature provided in vehicles. Having a working component installed was not enough, a car radio could only fulfill its duty, when broadcast stations continuously provided their service. And just like carmakers never had a contract with filling stations obliging them to deliver fuel and oil, they just trusted in the radio stations to keep on delivering appropriate data.

Decades later, data transmission in the other direction, namely from the car to a backend system, was used to indicate imminent service requirements. First via diagnosis cable, then via mobile internet connection. But as these teleservices apply to manufacturers or dealers rather than customers, their reliability has been considered rather uncritical, and their development went not as part of but in parallel to the vehicle development.

However, when data based vehicle features (such as audio streaming, traffic flow information or remote control via smart phone) are rendered to customers, they must be developed as a part of it. But in addition to the electronic systems on-board the vehicle, these data based features also require off-board elements, i.e.:

  • Data provider delivering the required data (e.g. traffic flow or weather conditions).
  • One or more backend servers, where this data is collected, stored and if necessary processed.
  • A mobile network to exchange data between cars and backend servers.
  • Additional connected systems that exchange data with backend servers or cars (e.g. smartphone apps or internet portals).

By this expansion of the system boundaries, cars become connected, become “things in the internet of things”. To ensure its functionality, first of all the respective development process must cover these off-board elements. Then, just like for any other component, quality criteria for data and their provision process must be defined:

  • Data availability: Data must be generated, transmitted and aggregated in a reliable and timely manner. Providing e.g. 10 minutes old data in a traffic flow information system makes it useless at that time and certainly leads to major customer dissatisfaction. Responsibility for data availability lies with data providers, but also with mobile network and backend operators.
  • Data quality: Getting stuck in a traffic jam in a road that was indicated free by the traffic flow information system or realizing that the parking structure the connected parking system has guided you to is fully booked are only two examples for what can happen to the customer if data is available but of poor quality. Here, responsibility lies with data providers.

To safeguard proper function of data based features, the automotive development processes for connected cars must include the appropriate methods, sub-processes and milestones that ensure robust provision of functional data for as long as the respective data based feature is used.

From producer to service provider. The almost unnoticed transformation.

The truly radical change for automakers though stems from the fact that in contrast to conventional vehicle functions, where their job is normally done when the car passes end-of-line inspection at the manufacturing plant, dependable operation of connected cars and their data based functions requires permanent efforts and support over the whole vehicle lifespan. For manufacturers, it is not sufficient anymore to hand over cars and – if required – provide repair and maintenance services, their role now includes continuously operating the vehicle’s features.

This is especially true for autonomous vehicles. In order to safely steer through traffic under all possible conditions, their control systems (which can be considered the most complex data based automotive feature by far) must continuously exchange a tremendous amount of data with their backend, and someone (i.e. the manufacturer) has to ensure that this functions safely and reliably.

At the end of the day, organization and processes must adapt to that transformation. As interaction with the vehicle increases “after sales”, companies have to clarify responsibilities for the ongoing operations of data based features as well as the management of their quality. The classic silo structure – development, production, sales and aftermarket – does not seem to be the right answer here anymore.

First published on LinkedIn on 8. October 2020

Talkin’ Bout a Transformation …


Whether eagerly yearned for or grudgingly conceded: By now, you have probably accepted that electric cars are inexorably on the rise. And even though you are sure that at least in some places there will still be cars with combustion engines on the road by 2050, it surely looks like EVs and Plug-in Hybrids will prevail in the cities. However, what you still find far less clear – even though you witness more and more public chargers around – is how EV drivers will be able to cope with the limited range of their vehicles in connection with the perceived scarcity of charging stations. And while at the same time some nerdish engineers reiteratively broadcast that fuel cells and hydrogen will solve this problem for ever, you still cannot get rid of this uneasy mental image of a huge crater stretching over a highway after a car crash with a poorly maintained hydrogen vehicle involved.


Then, as you read through your business strategy journals, you are told over and over that car ownership, the century old mobility pattern number one, is in rapid retreat. Urban teenagers, whose fathers were dreaming of fancy sports cars when they were their age, don’t even go for a driver’s license anymore. If train, bus or bicycle is not an option, people would not buy or lease cars but rather share a car or call a ride hailing service like Uber, the affordable and app-steered successor of what has long time been known as a taxi. But what is worrying you even more is that new digital service providers are said to take over the complete mobility business soon, with automakers being downgraded to basic hardware providers and public transport companies begging for contracts.


On top of that, automakers claim they will soon bring autonomous vehicles on the road. Not just something like an extra-advanced driver assistance system, but cars with neither steering wheel nor pedals but lots of extremely expensive sensors and software that must be extensively tested and meet standards initially developed for military aircraft. And while in spite of all confidence in engineering you still wonder how these cars would ever make it safely through unsecured road works or snowstorms and – even more significant – who apart from ride hailing providers would actually want to buy them, you witness the heralded date from which on these robocars should populate our cities’ streets being postponed year by year.


And as if all this wasn’t bad enough, some of the young guys around you, the ones wearing sneakers, a full beard and watching e-sports, tell you that data is the new gold, that big data means even more gold, and that your company should work agile, fail fast, provide something you would call completely unacceptable but they call minimum viable product, scale and ultimately indulge yourself in a so called digital transformation. All that of course independently from whether you are in automotive, mobility services, energy, public transport, insurance, law, or whatever. After thinking it over, you are left with the feeling that this is not all new but still kind of frightening. If only you would understand all these fancy IT buzzwords.


If your work was related to mobility for the last couple of years, all of the above probably sounds familiar. The battle-hardened manager, now somewhat disoriented and undetermined in this overgrown jungle called mobility of the future. How do all these bits and pieces fit together? The good news is: No one has ever been brought from one place to another by software alone. But the fact that vehicles and smartphones send and receive an exponentially increasing amount of data, that they are connected to back-end servers and with each other, and that artificial intelligence can create astonishing and valuable information from this data, will not only improve vehicle and service functionalities but dramatically change the way they are developed, produced or rendered, marketed and sold – and especially how vehicles and their private or corporate customers are served after sales.

The key for survival and success is embracing change. At the end of the day, the question is neither if you should proactively engage in a digital transformation nor when you should do it (the answers are yes and now). The sole question is how – and can usually not be answered sufficiently by the people who brought your company to where it is today …


First published on LinkedIn on 5. August 2020