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Principled Reference Data Management for Big Data and Business Intelligence

Principled Reference Data Management for Big Data and Business Intelligence

Sushain Pandit, Ivan Milman, Martin Oberhofer, Yinle Zhou
Copyright: © 2017 |Volume: 7 |Issue: 1 |Pages: 20
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513254|DOI: 10.4018/IJOCI.2017010104
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MLA

Pandit, Sushain, et al. "Principled Reference Data Management for Big Data and Business Intelligence." IJOCI vol.7, no.1 2017: pp.47-66. http://doi.org/10.4018/IJOCI.2017010104

APA

Pandit, S., Milman, I., Oberhofer, M., & Zhou, Y. (2017). Principled Reference Data Management for Big Data and Business Intelligence. International Journal of Organizational and Collective Intelligence (IJOCI), 7(1), 47-66. http://doi.org/10.4018/IJOCI.2017010104

Chicago

Pandit, Sushain, et al. "Principled Reference Data Management for Big Data and Business Intelligence," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.1: 47-66. http://doi.org/10.4018/IJOCI.2017010104

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Abstract

Most large enterprises requiring operational business processes utilize several thousand instances of legacy, upgraded, cloud-based, and/or acquired information management applications. With the advent of Big Data, Business Intelligence (BI) systems, receive unconsolidated data from a wide-range of data sources with no overarching governance procedures to ensure quality and consistency. Although different applications deal with their own flavor of data, reference data is found in all of them. Given the critical role that BI plays in ensuring business success, the fact that BI relies heavily on the quality of data to ensure that the intelligence being provided is trustworthy, and the prevalence of reference data in the information integration landscape, a principled approach towards management, stewardship and governance of reference data becomes necessary to ensure quality and operational excellence across BI systems. The authors discuss this approach in context of typical reference data management concepts and features, leading to a comprehensive solution architecture for BI integration.

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