Principled Reference Data Management for Big Data and Business Intelligence
Sushain Pandit, Ivan Milman, Martin Oberhofer, Yinle Zhou
Abstract
Sushain Pandit, Ivan Milman, Martin Oberhofer, Yinle Zhou
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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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.
Key concepts: Data governance, Computer science, Business intelligence, Data quality, Big data, Context (archaeology), Data science, Consistency (knowledge bases)