#463 "The Era When Car Data Becomes Money - 7 Monetization Strategies of Overseas Mobility Companies" (Exploration Explosion Days #101)
This time, I researched monetization strategies for automotive data.
The car you are driving right now is actually a "data generation machine" on wheels. Cars are equipped with thousands of sensors, generating a vast amount of data every second while in motion. The volume of this information reaches several gigabytes per day. Surprisingly, Tesla is leveraging this vehicle data and related services to develop new revenue streams. In fact, approximately 3% of Tesla's revenue in the first half of 2024 (January-September) came from businesses based on vehicle data (such as emissions credit trading). This is revenue generated without producing a single additional car. Furthermore, for the full year of 2024, sales revenue from emissions credits reached $2.76 billion (a 54% increase year-on-year), and in the fourth quarter of that year, credit sales alone accounted for about 30% of net profit. This is truly an example that confirms the industry's recognition that "data is the new oil."
In the global mobility industry, attention is now focused on the value of "mobility data," such as vehicle driving data, driver behavior, and traffic flow. This is because, when properly analyzed and utilized, such data creates enormous business opportunities.
In this article, I will explain specific examples of how leading overseas companies are **monetizing** mobility data from seven different perspectives. The key to success is not just collecting data, but "turning data into meaningful insights and providing concrete value to customers."
Chapter 1: The Gold Mine of Vehicle Data – Applications in Insurance and Maintenance
First, we should focus on the use of vehicle telematics data. Wejo, a British startup, once utilized telematics data collected from vehicles such as those from Ford to develop services for insurance companies. By using machine learning to analyze all driving-related data—such as GPS location, speed, frequency of hard braking, and steering or cornering behavior—they calculated risk scores for each driver. Based on these scores, insurance companies could set discounts for safe drivers and higher premiums for high-risk drivers. However, realizing this business model took time, and Wejo ended up filing for bankruptcy in 2023 due to financial difficulties. While market expectations were high, it is analyzed that one reason was that the path to sufficient monetization was not in sight, making it premature. This case shows that while "vehicle data is a gold mine," execution capability and proper timing are essential for monetization.
On the other hand, Otonomo, originating from Israel, built a larger-scale data platform business model. They analyzed data from over 500,000 vehicles collected from various manufacturers around the world using AI and provided it to a wide range of customers, including not only insurance companies but also fleet management companies and smart city government agencies. Its revenue model is a tiered subscription format based on the volume and quality of data and the depth of analysis. For example, the basic plan provides a certain level of data access and analysis APIs, while advanced plans offer real-time analysis and predictive reports. Otonomo went public, but struggled due to changes in the market environment and merged with the US company Urgently in 2023. The new "Urgently-Otonomo" operates in 26 countries and has restarted as a mobility service company with **over 100 partner contracts (covering up to 70 million vehicles)**, including automotive manufacturers, map/navigation providers, insurance, and fleets. This can also be said to be an example of the integration and reorganization of the data business.
Also, Continental of Germany is monetizing vehicle data through predictive maintenance services by leveraging its own strengths. They provide services that monitor engine vibration patterns, oil degradation, and brake pad wear in real-time using on-board sensors, and prompt maintenance at the stage where signs of failure are detected. This prevents breakdowns and unexpected downtime, and optimizes repair costs. For this reason, it is highly regarded by companies operating commercial vehicle fleets such as trucks and buses. For example, there are reports that a large truck operating company was able to reduce annual unplanned downtime due to breakdowns by ○% after introducing this service (*specific figures are based on internal data). Continental is launching digital solutions for vehicle health monitoring one after another, such as the tire pressure management system "ContiConnect," and is pioneering a field that can be called a vehicle health checkup business.
What is common to the above cases is that they do not just collect vehicle data, but extract "meaningful insights" from it and convert them into concrete value for customers. For example, Wejo's risk scores and Continental's failure prediction information are directly linked to customer decision-making, such as insurance premium setting and maintenance planning. Technologies supporting this include anomaly detection using time-series data prediction models (from classical ARIMA models to deep learning LSTM models) and large-scale analysis using cloud-based big data infrastructure. They are using machine learning to find subtle patterns and signs in vast amounts of sensor data that humans cannot notice, elevating them into valuable information.
Chapter 2: Driver Behavior Creates Value – Telematics Insurance and Safety Incentives
Following vehicle data, driver behavior data is attracting attention. Cambridge Mobile Telematics (CMT) in the US grew into a huge business by performing safe driving scoring for drivers using only smartphone sensors. By analyzing information obtained from the smartphone's accelerometer, gyroscope, and GPS, it detects driving behaviors such as rapid acceleration, hard braking, and sharp steering, as well as distraction caused by smartphone operation while driving. Then, it evaluates the safety level of each driver with a proprietary behavioral scoring model and provides it to insurance companies as a risk indicator. Its reliability is endorsed by regulatory authorities, and CMT's scoring model is approved for use in insurance products in 49 US states (including Washington D.C.). Insurance policies that introduce this model can differentiate premiums, such as giving discounts or rewards to safe drivers and adding surcharges to dangerous drivers.
What is interesting is that this mechanism brings about behavioral changes in drivers. According to CMT, there is data showing that drivers who regularly use telematics-linked insurance apps tend to improve their driving behavior, and dangerous acts such as "distracted driving" due to smartphone use decrease. In fact, in safe driving contests held in cities like Boston, a dramatic improvement was recorded where distracted driving decreased by 48% among the highest-risk participants, and rapid acceleration and hard braking decreased by 30-50%. By feeding back the "visualization" of data to drivers, their behavior is corrected, leading to safe driving.
The spread of Driver Monitoring Systems (DMS) is also progressing in Europe. Due to new EU regulations, the installation of driver status monitoring functions has become mandatory for new cars released from 2024 onwards. Luxury car manufacturers such as Mercedes-Benz have begun to standardize drowsiness detection and distraction warning functions using in-car cameras. These systems use computer vision and machine learning to analyze the driver's line of sight, eyelid movement, and head tilt in real-time to detect fatigue and distraction. When danger is detected, it alerts the driver with alarms or vibrations, which is expected to have the effect of preventing accidents. With the mandatory requirement across Europe, this type of in-cabin monitoring data may also create new service value (e.g., driver health management, insurance discounts, etc.) in the future.
Furthermore, ride-sharing companies like Grab and Uber are also developing unique safety strategies by utilizing driver data. Uber analyzes data such as smartphone GPS, acceleration, and gyro that can be acquired while driving, and sends feedback reports on driving habits to each driver. It is a mechanism that encourages improvement by comparing indicators such as rapid starts, hard braking, speeding, and sharp steering at curves with other drivers. The purpose is to raise drivers' awareness of safe driving and reduce accident rates. In fact, based on this data, Uber issues warnings or educational programs to drivers with low safe driving scores, and if no improvement is seen, they may be excluded from the ride-hailing platform. Conversely, for drivers who continue to drive safely and receive high ratings, they provide incentives such as increased rewards or priority dispatch through programs like "Uber Pro."
Grab in Southeast Asia is also developing its own safety program utilizing telematics. In Singapore, they conduct a safe driving score program for GrabRentals (the company's car rental business), where they monitor the driving data of invited drivers for three months. They calculate a weekly safety score based on indicators such as the number of hard brakes, frequency of speeding, and incidence of sharp steering, and provide reward points (up to 12,000 points, equivalent to several hundred dollars in monetary terms) to excellent drivers every month. Because safe driving is directly linked to income and improved treatment in this way, drivers' motivation increases, which ultimately leads to improved safety and customer satisfaction across the entire platform.
Chapter 3: Operational Data Maximizes Efficiency – AI Utilization in Ride-Hailing Platforms
Behind the success of Uber, a global leader in on-demand ride-hailing, lies the existence of advanced machine learning systems. The company began incorporating machine learning into its business in earnest around 2015, and in 2016, it acquired the AI research company Geometric Intelligence to establish "Uber AI Labs." One of the achievements born from there is the arrival time prediction system **"DeepETA."**
DeepETA is an ETA (Estimated Time of Arrival) estimation model announced by Uber in 2022, which adopts a hybrid approach that combines physical models (route calculation) using map data and real-time traffic information with statistical models using deep learning. Conventional routing engines represent road networks as graphs and calculate the shortest path to estimate the time required, but this alone has the challenge that **"maps are different from actual terrain." In other words, the shortest path on the model often does not reflect actual traffic conditions or driver preferences. In DeepETA, they first calculate the map-based route travel time, and then use machine learning to predict the difference (residual) between that and actual driver behavior. By learning "detour routes actually chosen by drivers" and "traffic congestion patterns by time of day and location" from vast amounts of past driving data, and correcting the residuals, they calculate high-precision ETAs in real-time. According to Uber, the transition to this deep learning model significantly improved the mean absolute error (MAE) compared to existing decision tree models, and also achieved high-speed response in millisecond units.**
In the field of demand forecasting, Uber also utilizes cutting-edge technology through time-series analysis and deep learning. In addition to time-series models that learn demand patterns by day of the week and time of day from past ride data, they combine recurrent neural networks (RNNs) that capture the effects of seasonal fluctuations and sudden events. For example, they incorporate not only periodic patterns such as increased demand on weekday mornings or Friday nights, but also external factors such as sudden changes in weather or the holding of large-scale events into the input data. By doing this, they predict the number of future ride requests with high precision, and Uber determines the areas and timing to apply surge pricing (demand-linked pricing). In areas where demand exceeds supply (the number of available drivers), they temporarily raise fares to provide operating incentives to drivers, and conversely, in quiet areas, they lower fares to stimulate demand. Through this dynamic pricing, Uber optimally allocates limited vehicle resources across the entire city, achieving improved matching rates and profit maximization.
Furthermore, Uber also utilizes data science for route optimization. While it is conventional to model road networks as graphs of nodes (intersections) and edges (roads) and calculate the shortest path using Dijkstra's algorithm or A* algorithm, they have introduced deep reinforcement learning and machine learning models for route selection in addition to that. They learn insights from past driving data, such as "this road should be avoided because it is congested at certain times of the day," and are able to present the fastest route according to the situation rather than just the shortest distance. For example, in some areas, congestion points change between day and night, but the model learns this empirically and provides drivers with the optimal route guidance at all times. This leads to a reduction in average ride time and improved fuel efficiency, which also leads to improved user satisfaction.
Grab in Southeast Asia also achieves demand forecasting and matching optimization through algorithms that take regional characteristics into account. For example, in tropical monsoon regions, demand tends to surge suddenly due to squalls (heavy rain), and there are also unique factors such as demand patterns by time of day during Ramadan. Grab's data scientists incorporate weather data and event calendars for each city into their models, creating prediction models rooted in the local area. As a result, when heavy rain begins to fall in a city, they can instantly detect the surge in demand, adjust pricing, notify drivers, and issue discount offers to users, enabling fine-tuned operations. Through such localized AI utilization, Grab is increasing its service quality and market share in countries across Southeast Asia.
Chapter 4: Controlling Urban Infrastructure with Data – Traffic Optimization Services
Next, let's look at data businesses that enhance mobility efficiency across entire cities. INRIX in the United States is known as a pioneer in the traffic data business. Founded in 2004 as a spin-out from Microsoft's research division, the company adopted an innovative approach from its inception. In addition to vehicle probe data collected from car navigation systems and the like, it also integrated and analyzed data from sensors installed on roads to provide real-time traffic information services. Today, it collects billions of data points daily in over 145 countries worldwide, providing various services such as traffic congestion information, optimal route guidance, and traffic forecast reports to both the public and private sectors, recording annual sales of over $100 million (as of 2016).
The cleverness of INRIX's business model lies in its transformation of government agencies from "data collectors" to "data purchasers." In the past, local governments would install tube-type sensors on roads themselves to measure traffic volume and conduct traffic surveys. However, because INRIX's data is more accurate and lower cost, many local governments stopped conducting their own surveys and began purchasing data from INRIX. In fact, INRIX has contracts with over 200 government agencies in 40 states within the U.S. alone, and in the city of Los Angeles, they utilized INRIX's analytical data to reprogram traffic signal timing throughout the city, successfully reducing traffic congestion by 5%. This is a prime example showing that traffic flow can be improved through data-based optimization without requiring massive infrastructure investment.
Parkopedia, originating from the UK, has achieved great success with a business specializing in parking data. Born from founder Eugene Tsyrklevich's experience struggling to find a parking space in San Francisco in 2007, the service started by billing itself as the "Wikipedia of parking," partly because there was almost no parking information on the internet at the time. Surprisingly, Parkopedia achieved profitability by expanding its business solely through its own cash flow, without raising any funds from venture capital. As of 2017, it already covered 6,000 cities in 75 countries worldwide, partnering one after another with major automakers such as Apple, Audi, BMW, Ford, Mercedes-Benz, Toyota, and Volkswagen. As of 2023, it has grown into a completely global platform, providing information on over 70 million parking spaces in 15,000 cities across 89 countries. According to the company's research, 92% of drivers feel difficulty in finding parking, and about **half consider parking information to be "very important" or "extremely important."** This scale of need became the company's business opportunity.
Technically speaking, Parkopedia has a strength in the fusion of static and dynamic data. In addition to static information such as parking location, rates, and business hours, it handles dynamic information regarding the occupancy status of each parking lot. In parking lots where sensors are installed, real-time vacancy counts can be obtained, but for street parking without sensors, it predicts occupancy levels using advanced mathematical models. Specifically, it analyzes Floating Car Data (FCD) such as "Park-In/Park-Out" signals collected from vehicles and speed reduction patterns of driving vehicles (behavior unique to cars searching for empty spaces) to estimate real-time occupancy rates for street parking in specific areas. In 2015, when they partnered with Garmin to conduct a demonstration of incorporating this parking prediction algorithm into navigation systems, they reportedly achieved 80-90% accuracy. Furthermore, Parkopedia does not neglect data quality management, such as deploying field staff globally to verify parking data on-site. The company has a data scientist team including PhDs from robotics and computer vision backgrounds, who work daily to process hundreds of millions of data points to improve service accuracy. While there are competitors with more funding (such as SpotHero and ParkWhiz in the U.S.), Parkopedia has differentiated itself through its status as a global unified platform and high-quality data, and it has now become the de facto standard for parking information in genuine automotive navigation systems.
Chapter 5: New Value Created by Payment Data – Subscriptions and MaaS
Beyond vehicle data itself, there are numerous monetization opportunities for "mobility x data." One of these is the utilization of payment data. Recently, subscription models for vehicles and services have been attracting attention. In the automotive industry, amidst the shift from "ownership to usage," the movement to provide cars not as products but as **services (monthly billing)** is spreading.
Electric vehicle manufacturer Tesla is a prime example; as mentioned earlier, by producing EVs, it acquired carbon credits (emission allowances) which it sold to other companies, generating $2.76 billion in revenue in 2024. In addition, Tesla is focusing on subscription sales of software features that can be added after vehicle purchase. For example, it provides advanced versions of its "Autopilot" autonomous driving support system and "Premium Connectivity" for in-car entertainment and communication functions via monthly billing, creating a continuous revenue stream. Through these efforts, Tesla maintains a relationship with customers even after vehicle sales, providing an "evolving car" experience through new feature offerings and software updates. This leads to increased customer loyalty, and further developments, such as turning fully autonomous driving software into a recurring revenue stream, are expected in the future.
In Europe, comprehensive subscriptions like "Care by Volvo" provided by Volvo are popular. This is a service where a car can be used for a fixed monthly fee, including everything from insurance and maintenance to taxes. It is particularly supported by the younger generation, including millennials, and the market is expanding. For Volvo, the service provider, collecting and analyzing detailed driving data and usage patterns of contracted users is useful for optimizing plan pricing and developing new services. For example, it enables data-driven product design, such as preparing low-cost plans for users with extremely low mileage, or conversely, proposing bundled plans with charging services for users who frequently travel long distances.
Payment data also plays an important role in MaaS (Mobility as a Service) platforms. MaaS is a service that integrates multiple modes of transportation (trains, buses, taxis, shared bicycles, etc.) into a single app, providing everything from planning to booking and payment seamlessly. In such platforms, it is necessary to appropriately distribute the fares paid by users among each operator, and the foundation for this is payment data. Based on history data of which transportation means each user utilized and to what extent, the platform operator calculates revenue distribution among operators. It can also be utilized for demand forecasting and adjustment of pricing based on analysis of usage patterns. For example, by gaining insights into whether subways or buses are used more frequently in specific sections, or how demand changes by time of day or day of the week, applications such as implementing usage promotion campaigns or dynamic pricing (discounts based on demand) become possible. Payment data is, so to speak, the blood of mobility services, and by analyzing its flow in detail, the efficiency and profitability of the entire service can be increased.
Chapter 6: Unexpected Monetization of Regulatory Data – Open Data Strategy
Data collection for regulatory compliance, which at first glance seems like a cost center, can be turned into a revenue opportunity depending on the approach. A prime example of this is the Mobility Data Specification (MDS) introduced by the city of Los Angeles. MDS is a new data standard/API established by the Los Angeles Department of Transportation (LADOT) in 2018, which mandated that emerging mobility service providers, such as electric kick-scooter operators, provide real-time vehicle data. Specifically, scooter rental companies like Lime and Bird are required to connect to the city's API and periodically report the location, operational status (in use or parked), and usage history of each vehicle. Through this mechanism, city authorities have become able to grasp the reality of new mobility deployed by private operators and utilize it for traffic planning and road management.
However, the city of Los Angeles does not stop there. It anonymized and aggregated the vast amount of data collected and released it as open data. This became a treasure trove for third-party app developers and research institutions. For example, utilizing the city of Los Angeles' open data, private navigation apps that tell users where and when scooter usage demand is high have appeared, and university research projects have been born that visualize in which areas traffic rule violations (such as sidewalk riding) are frequent to ensure pedestrian safety. In other words, they further opened up the data collected at the cost of regulatory compliance, forming an ecosystem for new service creation. As a result, added value is returned to citizens and businesses, creating a virtuous cycle where the city's policy effects (such as traffic congestion mitigation and safety improvement) are also enhanced.
New York City is taking a similar approach. The New York City Taxi and Limousine Commission (TLC) collects detailed operational log data for taxis and ride-shares (Uber, etc.) in the city and publishes it as a public dataset. New York's taxi trip records are famous open data, and a vast history has been published for over 10 years. The data includes the date, time, and location of each pickup, the date, time, and location of drop-off, travel distance, fare, tip amount, etc. (of course, personal information is not included). Utilizing this data, many services have appeared that analyze traffic flow patterns in the city or improve taxi supply-demand matching. For example, demand forecasting tools present advice to drivers such as "if there are X taxis at this location now, waiting time can be shortened" based on past trends, supporting efficient operations. Another startup utilized this open data to advance their dispatch algorithm, conducting a demonstration that shortened average dispatch times compared to before. By opening up data that the government collected for regulatory purposes, private wisdom and technology are combined, leading to new value creation.
As described, the idea of viewing regulation not as a "cost" but as an "opportunity" is important. In the examples of Los Angeles and New York, government authorities have become data curators and platformers, building a win-win relationship where they enhance the accuracy of data-driven policies themselves while providing data as material to the private sector to induce innovation. As a result, a secondary effect is also born where convenient services for citizens increase, and the mobility experience of the entire city improves.
Chapter 7: Massive Revenue Generated by Sustainability – Credit Trading and V2G
Finally, let's look at data utilization in the environmental and sustainability fields. The aforementioned carbon credit trading by Tesla is a typical example. Because Tesla sells only electric vehicles, it is always in a state of credit surplus (holding a surplus) under emission regulations, and it sells the surplus credits to other companies that cannot meet exhaust gas regulations. In 2024, that revenue reached $2.76 billion, a scale accounting for about 30% of Tesla's annual net profit of $8.4 billion. It can be said to be the ultimate example of turning regulations to one's advantage to directly monetize environmental value.
This carbon credit market is expanding year by year. In the U.S., automakers are actively buying and selling credits to comply with Corporate Average Fuel Economy (CAFE) regulations for greenhouse gases. According to an EPA (Environmental Protection Agency) report, in 2023, GM purchased emission credits equivalent to approximately 44 million tons, and Tesla sold approximately 34 million tons, the largest in the industry. Excluding other manufacturers, the industry as a whole had an emission excess (shortage) equivalent to 43.5 million tons, making Tesla the sole credit supply source to cover it. In the EU, ahead of emission standards becoming even stricter in 2025, "credit pooling" among manufacturers is becoming active. As of the end of 2024, manufacturers such as Stellantis, Toyota, Ford, Mazda, and Subaru have signed pool contracts with Tesla, and Mercedes-Benz has announced plans to team up with Polestar and Smart, which are under the Volvo umbrella. Luca de Meo (Renault CEO), former president of the European Automobile Manufacturers' Association (ACEA), estimated that "if no measures are taken, the entire European industry could be fined 15 billion euros (approx. 1.56 trillion yen) in 2025," but each company is moving to avoid this through credit trading. This means that as environmental regulations become stricter, the market for the "invisible product" called credits expands, and massive value transfer occurs between those who have them (EV-specialized manufacturers) and those who need them (traditional manufacturers).
Also, V2G (Vehicle-to-Grid) technology is attracting attention as a new revenue source. V2G is a mechanism to return (supply) electricity stored in electric vehicle batteries to the power grid, a model where rewards are obtained in exchange for supplying electricity from EVs to the grid during peak power demand times. In the U.S. and Europe, V2G demonstrations between power companies and EV users are progressing in various places, and commercial services are gradually beginning. For example, in a pilot project conducted in New York City, three Nissan Leaf electric vehicles supplied power to the power company ConEdison during peak daytime demand hours, confirming that income is generated for EV owners. This project utilized New York State's VDER (Value of Distributed Energy Resources) program, and compensation for electricity export from EVs was paid during the 60-day demonstration period. Rewards were determined by multiple indicators, not just the amount of electricity supplied, but also the degree of contribution to mitigating demand tightness (timing and location of peak cutting), and it was a mechanism to obtain stable income by making it a 10-year fixed contract. Similar attempts are being made in Europe in the Netherlands and Denmark, and the value of EVs as mobile batteries is being demonstrated. In the future, as V2G-compatible EVs increase, it is fully conceivable that general EV owners will participate in the electricity market during times when they are not driving, earning side income. Smart charging technology companies and power ventures have already begun to earn revenue in this area, and expectations are rising for it as a fusion business of energy and mobility.
New services are also increasing in the area of environmental data analysis. Examples include consulting services that utilize LCA (Life Cycle Assessment) to visualize and propose environmental impacts from manufacturing to disposal of EVs and hybrid vehicles to companies, and services that create maps of actual fuel consumption and emissions by region using GIS mapping and driving data to contribute to local government environmental policies. In addition, some telematics companies calculate an "Eco-Drive Score" based on driving behavior data and provide solutions that encourage eco-driving for corporate fleet drivers. This contributes to achieving corporate ESG goals because it provides a double benefit: reducing fuel consumption and CO₂ emissions while also leading to safer driving. Apps that detect unnecessary acceleration and deceleration for each driver using machine learning and provide improvement suggestions in real-time have also appeared, and the field of environmental data x mobility is expected to expand further in the future.
The Future of Mobility Opened by Data – Implications for Japan
So far, we have looked at mobility data monetization strategies from seven perspectives. To reiterate, the key to success is not just data collection, but extracting valuable insights from it and providing them to customers. All the cases introduced convert data into "decision-making axes for customers." Whether it is calculating insurance premiums from vehicle data (Chapter 1), designing safety incentives from driver behavior data (Chapter 2), optimizing supply-demand matching and pricing from operational data (Chapter 3), or optimizing traffic light control and parking guidance with city-scale big data (Chapter 4), it is being translated into concrete value propositions.
Furthermore, what is noteworthy is that these data businesses are beginning to collaborate with each other. By combining data utilization from different fields, a larger ecosystem and a cycle of value creation are being born. For example, if accident rates decrease due to telematics insurance using vehicle data (Chapter 2), the CO₂ emission reduction effect from the decrease in accidents is created, which may lead to the creation of environmental credits (Chapter 7). Also, if a driver's driving becomes calmer due to safety driving incentives (Chapter 2), fuel efficiency will improve, and cross-service measures such as returning the saved fuel costs with MaaS usage coupons can be considered. Vehicle data determines insurance premiums, that insurance data improves driving behavior, and improved driving behavior in turn generates carbon credits—such a virtuous cycle is actually starting to move. With data as a medium, the entire mobility industry is connecting at a speed never seen before, and an era of new value co-creation is arriving where 1+1 becomes 3 or 4.
For Japanese companies, there are many points to learn from these overseas cases. While Japanese companies are world-class in the technical capabilities of the vehicles themselves, they are honestly still lagging behind in terms of the ideas and execution power to monetize data. For example, while automobile manufacturers should be able to acquire vast amounts of data from connected cars, the service development using that data (insurance, maintenance, user experience improvement, etc.) is limited compared to the West. Also, the government has room to learn from the West in open data initiatives. A perspective is required that views regulations not as costs but as opportunities, and fosters a data ecosystem in cooperation with the private sector.
Cars have now evolved beyond mere means of transportation into data generation platforms. Companies that can quickly understand this change and utilize it appropriately will be the winners of the next-generation mobility business. I hope that Japanese companies will turn their eyes to the data assets sleeping within their own companies and take on the challenge of creating new value provision models with that as the core. The future of mobility opened by data has only just begun.
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