Please use this identifier to cite or link to this item:
http://repositorio.lnec.pt:8080/jspui/handle/123456789/1007491
Title: | How big data can enhance multi-utilities’ management? |
Authors: | Feliciano, J. Almeida, R. Santos, A. Ganhão, A. Coelho, J. Covas, D. Alegre, H. |
Keywords: | Dynamic platform;Performance assessment;Asset management |
Issue Date: | 21-Sep-2014 |
Publisher: | IWA WWC |
Abstract: | Water utilities sustainable management is nowadays a leading challenge in which data management and information analysis play a key role. Data generated is too many, moves too fast and is too diverse. As other cases of big-data, innovative information processing forms are needed to enhance organization’s management. AGS (Administração e Gestão de Sistemas de Salubridade, SA) is a multi-utility operator that manages 13 water utilities with long-term concession agreements. For water utilities, efficiency and effectiveness of systems’ management and its inherent quality of service require complex information that has been increasingly reinforced with legal obligations in developing infrastructure asset management (IAM) plans. These requirements added with reporting commitments with different entities led AGS to a step forward in data management. To support data supervision and advanced analytics AGS developed a technological tool – AGS platform. Based on big-data concepts and benchmarking principles this platform enables a complete vision of utilities and promotes different perspectives in systems’ performance and management. The present paper describes AGS approach to information analysis and the platform’s development process. Following IAM concerns and having the platform as support to evaluate Portuguese utilities’ data, a case study regarding the relation between rehabilitation investments and systems’ performance, in terms of non-revenue water, was analysed. |
URI: | https://repositorio.lnec.pt/jspui/handle/123456789/1007491 |
Appears in Collections: | DHA/NES - Comunicações a congressos e artigos de revista |
Files in This Item:
File | Description | Size | Format | |
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Paper_2478927_JoaoFeliciano_BigData.pdf | Documento principal | 745.86 kB | Adobe PDF | View/Open |
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