Data-driven indicator classification and selection for dynamic dashboards: The case of Spanish universities

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Title: Data-driven indicator classification and selection for dynamic dashboards: The case of Spanish universities
Authors: Molina-Carmona, Rafael | Llorens Largo, Faraón | Fernández Martínez, Antonio
Center, Department or Service: Cátedra Santander-UA de Transformación Digital
Keywords: KPI classification | KPI selection | Dashboard design | Data-driven design | Spanish universities KPI
Issue Date: Jun-2018
Abstract: In the context of business, dashboards are visual tools that display the most important information about the organization needed to help the top management to make decisions. Since it is important to just provide the relevant and objective-oriented information, the number of indicators included in the dashboard must be kept at minimum. Therefore, the crucial aspect when designing dashboards is the selection of the suitable Key Performance Indicators. To help to carry out this task, we propose a classification and selection methodology, based on the values of the own indicators. This methodology is performed in two main steps: the classification of the indicators in three categories (emergent, hot and consolidated indicators) and the selection of the suitable set of KPIs based on the organization strategies. To illustrate the application of this methodology, we also present a practical case of indicator classification and selection for Spanish universities based on the extensive UNIVERSITIC report.
Description: Paper submitted to EUNIS (European University Information Systems) 2018, Paris, France, 5-8 June 2018
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: Licencia Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0
Peer Review: si
Appears in Collections:Cátedra Santander-UA de Transformación Digital - Documentos de Trabajo

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