Reuse of knowledge by efficient data analytics to fix societal challenges

Abstract

Technology-intensive industries spend huge resources in the production of products to commercialize successful products. If the appetite on the market continues to change, the capacity to rapidly and cost-effectively refresh the offerings is an important competitive advantage. Even if components and designs need to be modified as new models are released, their underlying technology and designs can generally be reused to allow rapid economic development. Data are considered an important raw material that can influence multidisciplinary analysis, government, and business efficiency. In this paper, the Efficient data analytics (EDA) method has been suggested to fix societal challenges. The proposed methods aim to share the authors' views and perspectives on the emerging opportunities and challenges of the efficient data revolution.EDA provides four key aspects of technology reuse: strategy, method, culture, and information technology. The dimensions are further broken down into concepts supporting this technology reuse, including design on the technology platform and the reuse assessments. In practice, the system can evaluate an organization's existing reuse capabilities and offer an overall theoretical review of activities promoting technology reuse. To prove the system's concepts, industrial scenarios highlighting real questions of technological growth are used. Besides, the possible societal benefits of EDA in six ways are illustrated: enhanced decision management and incident prediction, data-informed technologies and innovative market models, direct social and climate benefits, community engagement, accountability, and public trust. Some best practice is suggested to capture these advantages. The experimental results suggested EDA increases reusability knowledge in the organization (96.3%), operational cost (95.1%), prediction ratio (97.4%), Community engagement ratio (94.1%), and public trust (98.5%).