REAL-TIME STREAMING TECHNOLOGY AND ANALYTICS FOR VALUE CREATION

ABSTRACT

Real-time streaming technology and analytics capabilities are growing rapidly, whereas a great number of firms and organizations are considering implementing this technology to meet rising business demands. Traditional computer infrastructures for high performance computing and big data analytics are not able to conduct such tasks. To tackle this obstacle, rapid analysis of streaming data requires significant amounts of computer and data storage capacity, which requires real-time streaming technology and analytics. Real-time streaming has become a crucial component where tremendous volumes of data from thousands of sensors and other information sources are processed so that a company extracting the copious amount of real-time data can react to changing conditions instantaneously. Streaming technology and analytics generate real value from real-time data. This paper presents the current architecture, status, and trend of real-time streaming technology and analytics. It discusses value creation of streaming analytics. The paper describes continuous intelligence and value for streaming analytics and the current architecture and status of streaming technology and analytics; showcases the leading vendors for streaming technology and analytics; discusses various real-world use cases and benefits across various industries; analyzes value creation of streaming analytics; and proposes several research issues, along with challenges and recommendations.

KEYWORDS:

• Streaming technology
• streaming analytics
• real-time streaming
• use cases
• value creation
• symbolic and functional value
• reengineering analytics
• human in the loop
• organization in the loop
• alert responses
• research issues

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ In March, 2021, we did a quoted Google search consisting of “streaming data analytics” “reengineering” and found 3 papers but none addressing the need or consequences of reengineering analytics.

² http://www.fao.org

³ http://en.wikipedia.org/wiki/On-board_diagnostics

⁴ https://www.tibco.com/products/tibco-spotfire/learn/demos?page=0:

⁵ https://www.tibco.com/products/tibco-spotfire/learn/demos?page=0

Additional information

Notes on contributors

Daniel E. O’Leary

Daniel E. O’Leary is a professor at the University of Southern California (USC), in the Marshall School of Business. He received his Ph.D. from Case Western Reserve University and his master’s degree in management science and statistics from the University of Michigan. Dan formerly worked with KPMG in their consulting practice. Professor O’Leary recently was named a Fulbright – Hays Scholar recipient and was a co-author on a paper that was named the winner of the 2020 Paul Gray “Thought Provoking” Paper award. Professor O’Leary’s book, Enterprise Resource Planning Systems book, published by Cambridge University Press has been translated into both Russian and Chinese. Professor O’Leary’s research focuses primarily on the use of emerging technologies, big data and artificial intelligence in business.