What is it about?

Facebook posts, tweets on Twitter, GPS data, and hits on a website are all examples of "Big Data" that firms are increasingly interested in leveraging for business purposes. But how exactly could such disparate data that is external to the firm be linked to the wealth of transaction data within the firm's information system? In this paper, we provide a detailed mapping from external Big Data to a firm's internal accounting information system. We also describe a series of analytic patterns that could be generated to explicitly link transaction data in internal information systems to Big Data so that new business insights can be gleaned.

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Why is it important?

Although Big Data has been successfully leveraged for digital marketing and advertising, exactly how that wealth of Big Data could be linked to a firm's information system has been somewhat of a mystery. This paper leverages the International Standards Organization business process framework and the Resource-Events-Agents framework from the early 1980s to formally map elements of a firm's internal information system to external business relevant Big Data elements. Our paper provides a blueprint for firm's seeking to enhance the business value of their internal transaction data by linking them to related external Big Data activity.

Perspectives

I am very excited about this paper as my co-author Guido Geerts and I feel strongly that it can have a significant impact on business practice. Business school researchers are increasingly being called upon to produce "relevant research." The model we present in this paper does just that. I hope our paper cuts through the hype about Big Data and allows firms to realize significant business value by linking their internal data to external "Big Data."

Uday Murthy
University of South Florida

Read the Original

This page is a summary of: An REA Ontology-Based Model for Mapping Big Data to Accounting Information Systems Elements, Journal of Information Systems, September 2017, American Accounting Association,
DOI: 10.2308/isys-51803.
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