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Erschienen in: Information Systems Frontiers 2/2018

22.11.2016

Enabling self-service BI: A methodology and a case study for a model management warehouse

verfasst von: David Schuff, Karen Corral, Robert D. St. Louis, Greg Schymik

Erschienen in: Information Systems Frontiers | Ausgabe 2/2018

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Abstract

The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI, but prior attempts at developing modeling languages for non-technical audiences have not been widely implemented. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor. The paper concludes by outlining several areas for future research.

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Fußnoten
1
There are 53 UI jurisdictions. They include the 50 states plus the District of Columbia, the Virgin Islands, and Puerto Rico. State is used here to refer to a UI jurisdiction.
 
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Metadaten
Titel
Enabling self-service BI: A methodology and a case study for a model management warehouse
verfasst von
David Schuff
Karen Corral
Robert D. St. Louis
Greg Schymik
Publikationsdatum
22.11.2016
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 2/2018
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-016-9722-2

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