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With Software-Defined Vehicles becoming the most impactful trend in the automotive industry, the amount of data generated by every car is skyrocketing. By adopting Big Data solutions, automotive companies embrace the proper data collecting, labeling, classifying, and streaming to their end-users. Equipped with Big Data technologies, OEMs and mobility providers can improve the driving experience and monetize data by B2B data sharing.
Encouraging automotive enterprises to leverage Big Data, Grape Up engineers and data scientists ensure consulting and software development services that allow for building data-driven solutions.
Read the ebook and learn about technologies and solutions behind Software-Defined Vehicles. Follow the process from prototyping to changing the entire driving experience. Learn from the automotive experts how software impacts automotive, insurance, and other associated industries.
Effective Data Analysis empowers automotive enterprises to optimize processes and operations. Proper solutions allow for finding peak performance points, bottlenecks, and all kinds of obstacles that need to be tackled with efficient execution.
Connected Vehicle Data, involving both the car and the driver, provides limitless opportunities for one of the most impactful trends of the connected car era - Data Monetization. Transferring data into new revenue streams can be achieved by partnering with 3rd parties from different industries like retail, insurance, and e-commerce to access car data and build infotainment applications. On the way to monetize data, automotive companies have to develop solutions allowing them to meet demanding challenges covering user consent, GDPR compliance, and data anonymization.
Expanding Connected Car market allows OEMs to improve their B2B data sharing and enhance or build public APIs, which can later be consumed by 3rd parties, thus opening new revenue streams and putting their data to work. Insurance and Rental companies are interested in leveraging vehicle status or behavioral data among other use cases.
Embracing the value and potential of connected car data through the whole vehicle life-cycle, automotive companies seize new opportunities. With Big Data processing and Machine Learning insights, the automotive offering can be improved to increase customer satisfaction and address upcoming trends.
Leveraging Data Lakes and Data Streams capable of real-time data ingestion and processing empowers automotive enterprises to implement data-driven decision-making. With such solutions, the required part of the information is immediately propagated to all mandatory systems, ensuring the proper flow of information and insights.
Once collected, data can be used for many purposes helping companies improve processes and customer experience. Helping automotive companies make their existing data work for new ML algorithms and pipelines, Grape Up leverage the ETL and EL(T) systems transforming data to the common structures.
Building multiple platforms for storing the data is costly, hard to maintain, and can result in data loss due to incorrect handling. This problem can be resolved by building a common data streaming platform able to handle real-time data for the whole enterprise. Built with scalability and redundancy in mind this kind of platform can effectively withstand and resist problems in having a system of distributed, different platforms and storage systems. The support cost is also lower.
Grape Up experts are highly experienced in creating and maintaining efficient and scalable data streaming platforms based on Kafka that are currently being used among Grape Up customers representing automotive and financial institutions around the world.
Data is a lifeblood of an organization. Each department generates and gathers enormous amounts of data about customers, products, or the environment. This data can be used to enable new revenue streams by creating better offers and improving understanding of customer needs. Data analytics can also result in reducing the cost by improving the bottlenecks and identifying pain points.
Building systems or ML algorithms leveraging the available data can be extremely complicated if the data is siloed and available only inside the department. Democratic access to the data across the enterprise helps to build better products faster and easier while having a single point for data storage makes the operations easier. Storing the heterogeneous data in data lakes and data meshes is easier for developers and data scientists.
For one of our customers, Grape Up engineers have already implemented the modern data storage, starting from the ETL/EL(T) system, through the data lake and ML system built on top of it while maintaining enterprise-grade privacy and GDPR compliance.
The Volkswagen Group presented publicly their “One Digital Platform” concept allowing to connect in-car services as well as run additional services for vehicles like charging or parking. Especially in the current era of people being always online, there are challenges to satisfy customer needs and expectations, which require a reliable IT infrastructure as a base […]
The Volkswagen Group presented publicly their “One Digital Platform” concept allowing to connect in-car services as well as run addition...
The basic definition taken from the Apache Kafka site states that this is an open-source distributed event streaming platform. But