According to the 2024 White Paper on Digital Transformation in the Automotive Industry, the digital transformation of the automotive industry is also facing difficulties in terms of technology:
Technical pain points and coping strategies
Pain point 1: The utilization rate of data assets is low, and the input and output are unbalanced
With the deepening of informatization construction, car companies have accumulated a large amount of data in sales and production. The advancement of digital transformation has further promoted the online transformation of offline processes, bringing richer data precipitation. At the same time, the evolution of data architectures has enabled enterprises to store more information and perform more complex data analysis. However, these "data dividends" have not been translated into business value as expected, but have instead been transformed into "negative data assets" - enterprises invest high costs in data storage and management, but it is difficult to achieve the goal of data empowering business.
The reasons for this are mainly concentrated in the following three aspects:
1. Uneven data quality: Data sources are becoming more and more diverse, such as imports from third-party platforms and distributors, and problems such as inconsistent calibers, false or wrong data mixing, and non-standard filling often occur, which affect the accuracy and efficiency of data fusion and follow-up analysis.
2. The phenomenon of data silos is serious: the data flow path is complex, and the interconnection between different systems is limited, which needs to be connected with the help of technical means such as APIs, which makes it difficult to connect and share data throughout the life cycle, and there are many barriers to analysis and insight, and the role of data in feeding back to the business is greatly reduced.
3. Lack of visibility: Business users have limited data that can be directly accessed and analyzed, and there is a lack of effective data analysis tools. The reason is that, on the one hand, the front-end visualization tools have a high threshold for use and lack of functions, which makes it difficult for users to complete the analysis independently and rely too much on data experts. On the other hand, the data labeling system is imperfect, and the business scenarios that can be observed are limited.
Solution Strategy:
Car companies should accelerate the construction of a dedicated data governance system, establish a data dictionary covering the entire life cycle of automobiles, and improve the management of master data and metadata. At the same time, upgrade the flexible and efficient data architecture: the underlying data lake realizes the fast and clear import of assets; Strengthen data governance and analysis management in the middle layer; The application layer promotes the integration of multi-source data and business visualization insights, and improves data-driven business decision-making capabilities.
Figure: Agile digital architecture
Pain point 2: The response of the technology platform is not agile enough, and the system upgrade resistance is high
In the process of digital transformation, automotive companies are generally faced with the challenge of transitioning from traditional "chimney" systems to agile architectures. The main manifestations are:
1. Disconnect between system functions and business scenarios: The design of system modules cannot be effectively connected with specific business processes and organizational structures, resulting in redundancy or lack of some functions, which not only cannot meet actual needs, but also wastes technical resources and affects the rapid evolution of the system.
2. Blurred boundaries between the middle office and the front end: Common problems such as including the front-end applications of agile services into the middle office scope result in a long function change cycle, making it difficult to keep up with the pace of business and reducing the overall response efficiency.
3. Hindered integration of old and new architectures: For example, if the infrastructure takes precedence over the construction of the service, and the problems such as poor data quality, barriers between systems, and difficulties in interface integration cannot be solved for a long time, the data cannot be effectively entered into the lake, resulting in the business still relying on the old data warehouse, and the data lake is useless.
Solution Strategy:
It is necessary to start from the enterprise business architecture, adopt a top-down approach to reorganize the digital system, and follow the four architectural principles of "SPIG":
1. Small Frontend (S): Based on the existing system functions, it focuses on the core business process, clearly connects departments and users, and builds lightweight and modular front-end SaaS applications to improve iterative flexibility.
2. P (Powerful Platform): Build a PaaS platform including a business middle platform and a data middle platform to support the operation of the business front office, and adopt containerized design (such as introducing DevOps and app stores) to enhance the integration efficiency of technology and business.
3. I (Intelligent Infrastructure): While improving the enterprise cloud infrastructure, we build edge layer capabilities, such as IoT access, edge computing, and heterogeneous gateways, to form an intelligent technical backing.
4. G (Governance Security Bottom Line): On the basis of traditional data governance and physical security, combined with the characteristics of the Internet of Vehicles, the security and controllability of vehicle-cloud data are strengthened, and the data security line is built.
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