Building an Integrated Data & AI Platform for the Energy Industry to Accelerate Digital and Intelligent Transformation
KeenData Lakehouse is an AI-native data intelligence platform designed to address complex energy data governance challenges. By leveraging AI-driven analytics, it enhances energy efficiency, strengthens operational safety, and enables intelligent, data-driven decision-making across energy enterprises.
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Challenges
Difficult Data Management: Multiple Storage Media and Complex Formats
The digital development of the energy industry has generated a large volume of semi-structured and unstructured business data. The variety of data storage media and the complexity of data formats make data management highly difficult and increase the cost of overall data coordination.
Weak Data Sharing and Collaboration: Independent and Fragmented Systems
Data systems across different stages of energy production are independent and fragmented, with no effective mechanisms for data interconnection. This makes it difficult to achieve data sharing and collaborative analysis, thereby hindering cross-stage business optimization.
Efficiency Improvement Bottlenecks: Lack of Effective Data Support
The improvement of energy utilization efficiency faces technical bottlenecks due to the lack of accurate and effective data support. This makes it difficult to achieve targeted breakthroughs in technical challenges and restricts the overall progress of efficiency improvement.
Solution Architecture
Empowering the future of digital intelligence, our architecture is your optimal solution.
Achievements
Our path to a win-win future is built together with you.
Improving Data Governance Efficiency
Standardized management to lay the foundation for cost reduction. Based on the Data & AI platform, semi-structured and unstructured energy data are governed, storage formats and management standards are unified, data coordination costs are reduced, and a solid foundation is laid for data applications.
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Full-Process Monitoring
Digital twin and real-time insights. AI is used to mine multi-source data to build a digital twin model covering the full process of production, transmission, and consumption, enabling real-time monitoring of energy system status and supporting precise operations.
Equipment and Dispatch Optimization
Intelligent prediction and collaborative efficiency improvement. Machine learning is used to analyze equipment data to predict faults, data from multiple operational links are integrated to enable sharing and collaboration, and energy dispatch is optimized. AI is also used to explore energy efficiency potential and drive dual improvements in energy efficiency and operational safety.
Enterprise Efficiency Breakthrough
Data empowerment to break through barriers and improve efficiency. With the support of precise data from the platform, energy enterprises are assisted in breaking through technical bottlenecks in utilization efficiency, tackling key technical challenges in a targeted manner, and accelerating the process of efficiency improvement.
Data Standardization and Management
Unified standards to improve quality and efficiency. Through data aggregation and standardized governance, highly available data assets are built, quality and security control mechanisms are established, and systematic data management and operational capabilities are formed, laying a solid foundation for data-driven business.
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Data Sharing Capability
Open sharing to enable data circulation. Through a unified data asset portal, data assets can be conveniently queried, acquired, and distributed. Under the premise of security and compliance, cross-departmental and cross-domain data sharing and exchange are realized, comprehensively improving data utilization efficiency and the level of business collaboration.