Data driven policy making: The Peruvian water resources observatory

Giuliana Barnuevo, Elsa Galarza, Maria Paz Herrera, Juan G. Lazo Lazo, Miguel Nunez-del-Prado

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nowadays, Big Data holds vast potential for improving decision-making in public policy due to the different methodologies for working with complex heterogeneous big data, which allows proposing policies based on real and measurable key performance indicators. This article aims to describe the water resource observatory of the Public Management School of Universidad del Pacífico. The idea behind the observatory is to handle data extracted from non-traditional sources to enhance efficient and responsive government solutions through evidence-based public policies for water regulation. We used Elastic Search stack to centralize and visualize data from different sources, which was standardized using river basins as basic units. Finally, we show a use case of the data gathered to optimize the water supply in new urban zones in Lima’s periphery.
Original languageEnglish
Title of host publicationInformation management and big data
Subtitle of host publication7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Place of PublicationCham
PublisherSpringer
Pages419-431
Number of pages13
Volume1410
DOIs
StatePublished - 12 May 2021
EventInternational Conference on Information Management and Big Data - Lima, Peru
Duration: 1 Oct 20203 Oct 2020
Conference number: 7
https://simbig.org/SIMBig2020/

Publication series

Name Communications in Computer and Information Science
PublisherSpringer
Number1410
ISSN (Print)1865-0929

Conference

ConferenceInternational Conference on Information Management and Big Data
Abbreviated titleSIMBig
Country/TerritoryPeru
CityLima
Period1/10/203/10/20
Internet address

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Big data
  • Data-driven decision
  • Public policy
  • Water management

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