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Revista Lasallista de Investigación
Print version ISSN 1794-4449
Abstract
RIVERO-RIQUEME, Daniela and ORTIZ-CLAVIJO, Luis Felipe. Data Flow Scheme for Public Sector Decision Making. Rev. Lasallista Investig. [online]. 2021, vol.18, n.2, pp.58-68. Epub Mar 11, 2022. ISSN 1794-4449. https://doi.org/10.22507/rli.v18n2a5.
Introduction:
The application scenarios of data science are wide, but the specialized literature reports few applications in the public sector, particularly as a decision-making tool. Interest in data science has increased in recent years, primarily motivated by the recurrent use of concepts such as the fourth industrial revolution or Big Data.
Objective:
Propose a data flow scheme for the public sector to aid decision-making.
Materials and methods:
The work followed a qualitative and descriptive approach methodology, with three stages: (1) documentary tracking, (2) targeting and analysis, and (3) dimension definition and schema design.
Results:
A data flow scheme with four dimensions is proposed: Big Data, data management, information management, and decision making.
Conclusions:
The proposed scheme is designed as a tool to help the public sector transition from an unstructured data flow to a sequential scheme that allows the generation of useful information, thereby facilitating decision-making processes.
Keywords : Data analysis; information; decision making; public sector.