Industry Clients
Success Stories
FAW Group · Project Background
FAW Group, a Fortune 500 automotive company, plans to build an "integrated, simplified, and software-defined" product and technology platform centered on future automotive product forms. The core purpose is to achieve efficient empowerment of intelligent connected vehicle data, providing high-quality and efficient support for business innovation at the data application layer. FAW Group decided to systematically build data capabilities, establish unified data system specifications and standards, create unified indicators and standardized models with consistent management caliber; unify data from various source ends, break data silos, establish a unified big data center; build full-chain comprehensive data assets, implement data governance and asset management, establish a unified data sharing and exchange center, and enable open empowerment to various systems under data security control mechanisms.
SAIC Group · Project Background
The automotive industry's operational management faces significant pain points and needs across various chains including R&D design, manufacturing, sales, and aftermarket services. These include non-standardized basic data, inconsistent data standards, and varying management calibers across departments, making consistent management actions difficult; siloed construction during informatization leads to data isolation and dispersion, resulting in disconnected data between production R&D and sales marketing, with urgent need for data assetization and management; poor data sharing and lack of unified data sharing centers.
Based on the above, the group decided to launch a data middle platform project to build unified data system specifications and standards, establish unified indicators and standardized models with consistent management caliber; unify data from various source ends, break data silos, establish a unified big data center, build full-chain comprehensive data assets, implement data governance and asset management; establish a unified data sharing and exchange center, enabling open empowerment to various systems under data security control mechanisms.
Project Objectives
- 01
Data Generation Layer Construction
The data generation layer is responsible for collecting and accessing original data from the business end. In terms of standards, establish a comprehensive data lifecycle generation system; technically, optimize vehicle-end data technology to support collection and access of millions of different types of data.
- 02
Data Development Layer Construction
Data foundation development, building basic big data capabilities, implementing metadata management, data lineage relationships, and data cleaning calculations, unified scheduling and operation maintenance to ensure healthy operation of development tasks; data algorithm development, supporting various data mining algorithms and artificial intelligence algorithms, building ultimate human, vehicle, and scenario cognition and interaction capabilities.
- 03
Data Application Layer Construction
The data application layer deeply mines data value, incubates data products, creates a data operation ecosystem, achieving multi-dimensional innovation driven by data for business, life, and vehicles. With the purpose of "data-driven major R&D", precisely insights and mines big data application needs in the R&D process, providing important decision engines for intelligent development of various businesses.
Solution Architecture
Project Results
Enterprise-level Data Lifecycle Management
A one-stop, full-chain, full-process data foundation integrating the DataOps framework, covering data lifecycle management, creating automated data service chains for continuous integration, continuous development, and continuous service, enabling quick query and retrieval of vehicle information at various nodes using just a chassis number.
One-stop Data Development Capability
Technical development capabilities for data access, parsing, processing, algorithms, services, and applications through zero-code or low-code methods; visual task orchestration capabilities; multi-end self-service data query capabilities; data asset and application component API/SDK service opening capabilities; cross-platform, cross-application intelligent job scheduling capabilities, achieving centralized and standardized job management capabilities.
Intelligent Business Scenario Applications
Implementing machine-to-human customer service functions, replacing or buffering 24-hour manual customer service; AI-based business scenario applications, such as visual verification, radar perception, intelligent tagging, and intelligent recommendations; digital twin capabilities based on real-time data, such as latest status information for vehicles and applications.
Establish Intelligent Industry Data Profiling Ecosystems
Based on vehicle profiles, combined with dealer/service provider data to create dealer/service provider profiles, depicting service provider financial/credit ratings, service ratings, market share, etc. Based on service provider profiles, can meet scenarios such as vehicle sales allocation, parts inventory, fault maintenance, and claims.
Strategic User Data Integration & Sharing Framework
Customer-centric, rationally integrating information resources, enabling intelligent applications for various businesses through shared and open methods under security control. Through data mining and analysis service capabilities, accurately and effectively identify customer purchase experience needs, effectively support content precision operations, further win customer trust and build mutual trust, successfully establishing loyal and stable customer relationships.
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