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Principled Reference Data Management for Business Intelligence
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Author(s): Ivan Milman (IBM Corporation, USA), Martin Oberhofer (IBM Corporation, USA), Sushain Pandit (IBM Corporation, USA)and Yinle Zhou (IBM Corporation, USA)
Copyright: 2014
Pages: 17
Source title:
Information Quality and Governance for Business Intelligence
Source Author(s)/Editor(s): William Yeoh (Deakin University, Australia), John R. Talburt (University of Arkansas at Little Rock, USA)and Yinle Zhou (IBM Corporation, USA)
DOI: 10.4018/978-1-4666-4892-0.ch011
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Abstract
Most large enterprises requiring operational business processes (e.g., call center, human resources, order fulfillments, billing, etc.) utilize anywhere from a few hundred to several thousand instances of legacy, upgraded, cloud-based, and/or acquired information management applications. Due to this vastly heterogeneous information landscape, Business Intelligence (BI) systems (e.g., enterprise data warehouses) receive unconsolidated data from a wide-range of data sources with no overarching governance procedures to ensure quality, consistency, or appropriateness. Although different applications deal with their own flavor of data (e.g., master data, metadata, unstructured and structured data, etc.), reference data (residing in code tables) is found invariably 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. In this chapter, the authors discuss this approach in the domain of typical reference data management concepts and features, leading to a comprehensive solution architecture for BI integration.
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