WhaleDI Supports Integrated Energy Smart Services

• Analytics & Modeling - Big Data Analytics
• Platform as a Service (PaaS) - Data Management Platforms

• Retail
• Telecommunications
• Transportation

• Data Science Services
• Software Design & Engineering Services

From the sorting and analysis of past cases of digital construction in the energy field, four problems are often encountered in the process of digital upgrading of the energy industry, including the difficulty of using data in the early stage of construction, the diverse and volatile needs in the middle stage of construction, and the later stage of construction., the lack of continuous operation capability and the mismatch of safety requirements, etc.

• Data is difficult to use: while the amount of data is exploding, the data chimney still exists. According to the statistical analysis of relevant institutions, about 80% of the data has not been effectively used due to data silos and lack of analysis capabilities.
• Diverse and volatile demands: supply and demand sides, production links, and business departments have different demands for data usage. Business scenarios are complex and have low fault tolerance, making it difficult to get through the CPS (information physical system).
• Lack of continuous operation capability: business applications and data construction are separated, and data services cannot cope with rapid changes in business, resulting in a lack of depth and breadth of operational services and a significant drop in customer satisfaction.
• Mismatched security performance: comprehensive energy services require extensive access to customer data, and it is difficult to avoid data interaction security issues between internal and external networks.

WhaleDI focuses on the digital transformation of the energy industry and has launched a data intelligence solution suitable for the digital transformation of the energy industry in view of the particularity of the energy service scenarios of "big scenarios and small traffic" and the business complexity of massive ecological scenarios that cannot be leveraged. Specifically, based on the methodology of enterprise transformation and upgrading in the era of digital intelligence, WhaleDI enhances the intelligent base of enterprise data and refines the data intelligence centre of the energy industry, and is committed to building a new infrastructure for the energy industry, thereby accelerating the digitalization of the energy industry. Innovation.

• [Data Management - Data Access]
  

  Lean energy management: WhaleDI aggregates and integrates multi-industry, multi-enterprise, multi-level and multi-type energy data, forms a smart energy data view through data fusion and orchestration, and uses WhaleDI AI factory to conduct online energy consumption related models, training, management, optimization, subscription, and effectively solve the three major energy difficulties: 1) the difficulty of comprehensive energy consumption index statistics; 2) the difficulty of locating the root cause of excessive energy consumption; 3) the difficulty of active control and prediction of energy consumption, so that statistics can be checked and located.

• [Product Improvement - Scalability]
  

  Smart decision-making on energy use: based on the comprehensive energy consumption index system and customer detailed portraits constructed by WhaleDI, it can help energy companies summarize the energy consumption characteristics of customers in different industries in the actual business process and explore potential customers for comprehensive energy services. , promote the development of targeted and personalized comprehensive energy service business, provide targeted users with accurate and scientific guidance on energy use, increase user stickiness, and facilitate intelligent decision-making on energy use.

• [Data Management - Data Availability]
  

  WhaleDI provides customers with mature digital intelligence platforms, engines and operation services. While realizing integrated digital intelligence operations and improving the operational efficiency of enterprise data intelligence applications, it helps enterprises quickly establish a digital architecture and accelerate digital transformation with limited resources and trial costs.