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XBRL and Business Intelligence

From Business Reporting to Advanced Analysis
  • Peter Chamoni

Abstract

There are several reasons to join the two concepts of business intelligence (BI) and eXtensible Business Reporting Language (XBRL). Both concepts have in common the support and automation of the management process of reporting and analyzing business information. Whereas XBRL tries to describe the meaning of business data and to standardize data exchange, BI seeks to analyze and report these decision-relevant data. Both come from different perspectives, XBRL from semantic description of data within an XML environment and BI from search of knowledge in data. In a naïve way we can understand XBRL as an automated process of business reporting and therefore as a part of BI. Otherwise BI provides a broad set of algorithms to explore the structure and meaning of data. All the data scrubbing and pre-processing (extract, transform and load: ETL) has to do with the mapping of meta data and can be neglected when we leverage clean and meaningful (XBRL-) data. So why not use the semantic layer and taxonomy of XBRL to go beyond reporting and do more in-depth analysis of financial transactions as can be found in a general ledger? Real-time control of business processes is currently hyped within the data warehousing industry. As every business process should possibly be traced in the accounts of a company a constant flow of financial data in XBRL-format into a BI-system will be necessary for a continuous control of operations, for early fraud detection and BI as a source of compliance systems. The intelligent real-time enterprise of the future will be based on these technologies. The objective of this paper is therefore to point out what future research must be done to develop analytical applications with a high degree of intelligence and very low reaction time based on XBRL and BI.

Keywords

Business Process Data Warehouse Business Intelligence Generally Accepted Accounting Principle Business Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden 2007

Authors and Affiliations

  • Peter Chamoni
    • 1
  1. 1.University of Duisburg-EssenGermany

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