IS200AEPAH1AFD A number of enterprises have begun to make beneficial exploration and successful practice.

IS200AEPAH1AFD Inspur has done some practice in data asset quality evaluation and data asset value evaluation. Wang Xiangsheng introduced that Inspur Aolin solidly carried out data asset evaluation work, taking Inspur Aolin analysis model data assets as the main body to carry out data asset evaluation, passed the data quality evaluation on October 23, 2023, and released the first data asset evaluation report backed up by the China Assessment Association on November 16, and obtained a credit limit of 10 million yuan based on this data asset.

IS200AEPAH1AFD The quality of data assets has a decisive impact on the value of enterprise data assets. Inspur launched Inspur Haiyue data quality evaluation tool 3.0, the product built-in national standards, industry standards, external data and other massive reference data, integrated “data quality evaluation standard GB/ T36344-2018” and “Data Asset Evaluation guidance” to establish an authoritative and comprehensive evaluation system, can be targeted at different data assets, Dynamically and accurately generate matching evaluation rules. Through the standardized evaluation process, various types of data such as real-time exchange data, offline data packets, and models are evaluated, covering various industries such as finance, telecommunications, and government, to achieve data quality improvement, improve the reliability of decision-making basis, support data asset evaluation and other key application scenarios, and empower the enterprise data management process.

IS200AEPAH1AFD Yonyou Jingzhi Industrial big data center gathers hundreds of millions of data of 3 million industrial enterprises and precipitates 2,926 industrial mechanism models. Among them, based on the industrial Internet identification big data, can realize the intelligent interconnection of upstream and downstream enterprises in the industrial chain; Based on equipment fault big data, it can help engineers quickly locate fault causes and accurately troubleshoot faults. Based on the large scale model of scrap grading, it can help quality inspection personnel to improve work efficiency and reduce production costs; Based on the large model of safety behavior recognition, it can help production safety managers to find dangerous behaviors in time, real-time early warning, and eliminate safety risks.

As a large state-owned enterprise directly managed by the central government, Angang regards digital intelligence as the core competitiveness of the new round of iron and steel industrial revolution, gives full play to the advantages of massive data and rich application scenarios, and continues to promote digital and intelligent construction. In June 2022, Angang Steel has begun to work on data governance and data asset sorting. In order to make better use of data circulation and data value, Angang has decomposed 17 key applications in five categories from the planning level and program implementation.

IS200AEPAH1AFD Forced number of insufficient power enterprises to release data capabilities

However, at present, a large number of industrial enterprises in China still have insufficient power for digital transformation, and are still in the process of informatization, and they have not formed a data system or a complete data strategy to guide the generation of data assets.

In the view of Wu Dayou, the main person in charge of the International Data Advanced Management Research Institute, the data transformation of industrial enterprises can not just sell equipment and tools. Tools or equipment must have the ability to collect data legally, we need to think about remote data linkage, be able to monitor data, monitor the normal operation of the equipment, be able to predict the possible failure of the equipment in advance, and achieve “zero failure” service.

IS200AEPAH1AFD Now more and more enterprises realize that the digital cost reduction and efficiency increase of the industrial industry will inevitably come to a bottleneck period, because all the digital transformation if the goal of cost reduction and efficiency is to meet the “threshold”, the cost can not be zero, and the benefit will not be infinite. Enterprises want to change business mechanisms or service models, and in this process, data assets into the table is actually forcing enterprises to release data capabilities at the same time, but also forcing digital business models to change. Enterprise data assets will promote the upgrading of traditional business forms of enterprises, and the precipitation of data assets will ultimately create real value for users, so as to generate effective power points.

Zhang Xu, chief data officer of UF Network’s large enterprise customer business group, also believes that if it is only to reduce costs and increase efficiency, “there will be a head”, but the company can provide better goods and products to the society, its future can be very much. He mentioned that UFyou’s “industrial big data +AI” solution is based on big data technology and a new generation of AI technology, integrated into the entire industrial life cycle business scenario, mining the value of industrial big data, helping manufacturing enterprises to achieve reasonable production scheduling, optimize ingredients, quality diagnosis, fault prediction, safety warning, and play with data assets to make production simpler and quality more stable. Lower cost, more scientific decision-making.

Wang Xiangwang believes that the data sources of industrial enterprises are complex and diverse, and the data scale is larger, which needs to be supported by a special data governance and data asset management platform for industrial enterprises, which is the technical guarantee for realizing data assets. In addition, in terms of operation and management, production and operation, industrial enterprises have many scenarios that can drive the transformation from data resources to data products and data services, such as strategic decision-making, production improvement, operation optimization, risk prevention and control, etc., which is an important engine to realize data capitalization, requiring specialized manufacturers to provide service support.

The open source design of industrial data resources is very valuable, and there will be new business conversion opportunities when large enterprises take the lead and open source the ecosystem. Yan Yang predicted that with the development of large models, meta-universes and the advancement of future enterprise data assets into the table, if enterprises can seize the opportunity to grow up, it may become a new “unicorn”.