In the wave of digital transformation, the automotive industry has accumulated a large amount of business data, but the current enterprises are still lagging behind in data governance and utilization, resulting in a large amount of data resources that have not been effectively transformed into value. We have observed that the industry is facing the following three major challenges:
I. Serious data islands, information barriers restricting collaboration efficiency. Many enterprises adopt a "chimney" IT architecture, various business systems and databases are independent of each other, and the phenomenon of duplicate construction and storage of data is common, and there is a lack of unified data standards. It is difficult to achieve data exchange and sharing between departments, which directly affects operational efficiency and resource allocation.
II. Lack of unified governance, fragmentation of data cognition, and the lack of a unified data resource pool at the enterprise level, and the deviation of the definition, understanding and use of data by different teams, forming a cognitive disconnect. Data users often need to invest a lot of energy in cleaning and processing, which is difficult to support efficient global data analysis.
III. Insufficient Data Value Mining and Lagging Business Response. Many enterprises have not yet established customized data service capabilities around specific business scenarios, resulting in slow response to market dynamics, customer behavior, and operational bottlenecks, and it is difficult to achieve agile adjustment and refined operations.
In order to meet the above challenges, we propose a data middle platform solution for the Internet of Vehicles to support enterprises in digital marketing, sales management, customer life cycle management and other aspects of data capacity building through scientific methodology.
Figure: Internet of Vehicles (IoV) data middle platform solution
The solution covers three core modules: master data platform construction, data asset precipitation and data scenario application
1. Master data platform: Unified data base to support global development
Master data management is the foundation of the data middle platform, carrying subsequent data development and scenario implementation. We define key business objects such as people, vehicles, and dealers as the main data, and uniformly access multiple data sources such as vehicle information, user portraits, behavior tracking points, sales records, after-sales work orders, vehicles, machines, and traffic.
After data access, it needs to be structured in combination with enterprise processes, and ensure data integrity and consistency through extraction, integration, standardization, cleaning, and verification. Master data certified by departmental consensus will be stored centrally as the "source of truth" for the data middle-end.
2. Data asset precipitation: Build warehouses in different domains and consolidate the digital foundation
The formation of data assets is an important achievement of enterprise digital transformation. By building a data platform, enterprises can carry out horizontal hierarchical and vertical domain refined management of the access to massive data.
Starting from business processes and application scenarios, build middle-tier data and business data domains to promote the transformation of raw data into structured assets. Data assets are controllable, available, and identifiable, providing basic support for subsequent analysis and services.
3. Data scenario application: Empower business growth and realize value transformation
The implementation of data scenarios is the key to unlocking the value of data. Empower business through the data middle platform and promote personalized and precise marketing. For example, it provides intelligent delivery services around marketing scenarios to help customers achieve refined operations and business growth.
In terms of strategy formulation, we conduct a comprehensive analysis based on the Internet of Vehicles and user data, integrate the actual online and offline business, identify potential business scenarios, evaluate their feasibility and benefits, and formulate optimal operation plans. By continuously optimizing user experience and business logic, we expand the boundaries of data application to achieve efficient operation and continuous innovation of enterprises.
Conclusion:
The data center of the Internet of Vehicles is not only a data integration tool, but also the core engine to promote intelligent and refined business operations. By building a unified data management system, accumulating high-value data assets, and promoting the deep integration of business scenarios, enterprises will be better able to respond to market changes and achieve data-driven sustainable growth.
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