Recent Development in Big Data Analytics for Business Operations and Risk Management

Area of Science:

• Industrial Systems Engineering
• Cybernetics
• Data Science

Background:

• Big data analytics is an emerging field with significant potential for organizations.
• Industrial systems generate vast amounts of data from various sources like sensor networks and internet-based systems.
• Current big data research lacks focus and integration, indicating its early stage of development.

Purpose of the Study:

• To present the challenges and opportunities of big data analytics within industrial systems engineering and cybernetics.
• To examine technological advancements relevant to industrial business systems.
• To analyze the reliability, security, and operational risk management of industrial systems in the context of big data.

Main Methods:

• Literature review and synthesis of existing research on big data in industrial contexts.
• Analysis of technological developments impacting industrial systems.
• Examination of risk management strategies for industrial operations utilizing big data.

Main Results:

• Identified key challenges in big data adoption, including unclear focus and fragmented research.
• Highlighted opportunities for enhanced business operations and risk management through big data analytics.
• Discussed the importance of technological advancements, reliability, and security in industrial big data applications.

Conclusions:

• Big data analytics presents both significant challenges and opportunities for industrial systems.
• Further research is needed to consolidate the field and address specific industrial applications.
• The integration of big data is crucial for improving reliability, security, and operational risk management in industrial settings.