What big data analytic technology is used in industry?5 answersBig data analytics technology is extensively utilized in various industries, such as shipping, manufacturing, and tourism. This technology involves processing and analyzing massive amounts of data to extract valuable insights and improve decision-making processes. In the shipping industry, big data analysis is employed to diagnose mechanical faults and enhance innovation. Industry 4.0 relies on big data for communication between machines, forming the foundation for digital factories and smart products. Furthermore, the tourism sector leverages big data technologies to understand tourist preferences and enhance decision-making processes. The application of big data analytics transcends traditional data handling methods, offering revolutionary changes in businesses and scientific research. Overall, big data analytics plays a crucial role in driving innovation and efficiency across various industries.
How big data analytics can influence AI adoption and use in SMEs?5 answersBig Data Analytics (BDA) plays a crucial role in influencing AI adoption and use in Small and Medium Enterprises (SMEs). Research indicates that BDA adoption is influenced by factors such as perceived benefits, top management support, organizational readiness, and environmental factors. BDA acts as a mediator between project performance and various factors like project knowledge management, green purchasing, and project operational capabilities, positively impacting project success in SMEs. Additionally, the acceptance of BDA in SMEs is influenced by factors like infrastructure quality and the perceived difficulty of implementation, emphasizing the importance of investing in a good infrastructure for successful BDA implementation in SMEs. Overall, integrating BDA can enhance AI adoption and utilization in SMEs by providing valuable insights, improving decision-making processes, and fostering innovation.
How to use genAI in MIS/IS research?4 answersGenAI can be used in MIS/IS research by leveraging its capabilities in analyzing and visualizing large genomic datasets. One approach is to develop user-friendly tools with graphical user interfaces (GUI) hosted on web-based servers. These GUI applications, such as Shiny, make complex workflows accessible to researchers without programming knowledge. For example, GENAVi (Gene Expression Normalization Analysis and Visualization) is a GUI-based tool that allows scientists without bioinformatics expertise to perform normalization and differential expression analysis (DEA) of RNA-Seq data. GENAVi combines Bioconductor packages and provides features like visualization of expression, clustering samples, principal components analysis, DEA, and gene set enrichment. It also offers a range of normalization methods and clear session reporting for reproducible analysis. This user-friendly application provides a convenient platform for researchers in MIS/IS to analyze and visualize gene expression data without requiring extensive bioinformatics skills ^[Reyes] ^[Wilkinson] ^[Jain].
What do Big Data analytics do?5 answersBig Data analytics refers to the process of examining large and complex datasets to uncover hidden patterns, correlations, and insights that can help organizations make informed business decisions. It leverages advanced computational techniques to extract insights from voluminous data sets. Big Data analytics can help organizations make sense of the massive amounts of data generated by businesses and individuals. It is essential in supply chain management-related activities as it can manage global, complex, tempestuous, and dynamic value chains. Big Data analytics is a critical success element for organizations in the global and dynamic market. It provides computational approaches for extracting meaningful insights from massive amounts of data generated from various sources. Organizations are leveraging Big Data analytics to understand user behavior and provide valuable solutions. Overall, Big Data analytics enables organizations to gain insights, make informed decisions, and gain a competitive edge in the business world.
How can use big data in auditing?5 answersBig data can be used in auditing to improve efficiency, effectiveness, and the quality of audit results. By utilizing big data analytics, auditors can extract multi-level features from various materials, such as visual characteristics and textual abilities, using deep learning models like convolution neural networks (CNN). Big data analytics can also minimize the risks associated with sampling, allowing auditors to provide a reasonable level of assurance. Additionally, big data technology can be applied in forensic accounting and auditing practices, aiding in fraud detection and resolving internal abnormalities prior to external audits. Optimizing big data can help auditors process data faster, change the sampling paradigm, and resolve audit time pressure issues. Overall, the application of big data technology in auditing is seen as a beneficial trend that can enhance audit processes and outcomes.
How can AI be used to analyze big data?5 answersAI can be used to analyze big data by applying state-of-the-art AI software to help decision makers after major global crises such as climate change, natural disasters, and pandemics. It can also be used to develop an AI model using big data and AI statistical software, which can be valuable to many socioeconomic stakeholders. Another approach is to utilize a multi-feature information retrieval approach and an AI-powered big data MFE scheme to analyze and extract keywords from current events and news. The confluence of Big Data and AI allows companies to automate and improve complex analytical tasks, deriving insights and unlocking the value of big data. Additionally, AI can aid in the resolution of critical healthcare concerns by utilizing big data analysis and data standardization. In industrial systems, AI models can be used for big data analytics, but they are vulnerable to adversarial attacks. Information fingerprinting techniques based on deep learning can be used to detect adversarial samples and ensure the reliability and safety of AI models.