Abstract
This article introduces the data-driven management system named the NEW ARP (Academia Resource Planning) for CAS (Chinese Academy of Science) researchers. It combines the business-driven mode with the data-driven mode by using big-data technologies to conduct a service-oriented architecture. The article mainly focuses on three major aspects of the research work, including the data-driven application framework, the data-driven business processes, and intelligent decision-making through data-driven innovation. It presents a “service-management-decision support” data ecological environment during scientific research management and plans to explore further analysis based on data governance.
TopBackground
The new generation of information technology is developing in both breadth and depth (Heath, 2019). And deep integration of informatization and multiple business areas is a notable feature of the current development of global informatization. The new generation of information technology, which is represented by cloud computing, big data, and artificial intelligence, is booming and widely used all over the world (Dillon, Wu, & Chang, 2018; Rahm, 2016; Zhu & Zheng, 2018; Newell & Marabelli, 2014). The information process characterized by intelligent service is also profoundly affecting and changing the way of human production, life, and cognition (Steininger, 2019). Accelerating the development of information technology has increasingly become a popular choice for most countries in the world. The transformation of the industrial structure is going faster, and the division of the industrial chain is more detailed. As business applications get richer, big data plays a significant role in discovering user needs and guiding product designs (Bechtel & Jayaram, 2020).
Scientific research management is the management of scientific process activities, involving organizations, projects, funds, personnel, assets, and other aspects related (Zhang, Liu, & Song, 2009; Lin, Cen, & Zhou, 2014; You, Li, & Zhao, 2013). Scientific research activities are exploratory and creative, with strong flexibility and uncertainty (Li, 2011). That makes the management work become a complex system engineering. However, innovative and effective management is an essential auxiliary to breakthroughs in science and technology, which can effectively improve the management level of scientific research institutions and promote scientific and technological progress (Yang, 2016; Zhou, 2019; Kang & Liu, 2021). Therefore, the authors hope to improve the efficiency of scientific research management through effective planning and intelligent managing of personnel, projects, scientific research achievements, and so on (Yongtao, 2019).
Key Terms in this Chapter
CAS: Chinese Academy of Sciences. It’s the linchpin of China’s drive to explore and harness high technology and the natural sciences for the benefit of China and the world.
Scientific Research Management System: A system for the management of scientific research activities to improve the management level of scientific research institutions and promote scientific and technological progress.
Data-Driven: It's a mode that supports business driven by data flow through data aggregation, governance, and analysis technique.
Data-Driven Application Framework: It's a system framework with all the business data connected effectively through data aggregation and governance.
Data-Driven Business Processes: The data-driven business processes are produced by data-driven process engines, and they could adapt to rapid business constructions changing on business demands.
ARP: Academia Resource Planning. It’s a new-type information system for scientific research management used by all researchers of CAS.
NEW ARP: The NEW ARP reconstructs the original ARP system and builds up an information application ecology covering the main scientific research management business by data-driven concept and big data analysis technology.