Data Quality in the Money Direct to School Program (MDSP): Overcoming the Garbage In, Garbage Out (GIGO) Challenge
DOI:
https://doi.org/10.18225/ci.inf.v53i.6747Keywords:
money direct to school program, garbage in garbage out, data quality, dashboards, collaborative center for support in monitoring and management of educational programsAbstract
Introduction: The investigation presents and discusses the relevance of data quality in Data Science, introducing the concept of "Garbage In, Garbage Out" (GIGO) to the Money Direct to School Program (PDDE). Objectives: The objective was to describe the process of data extraction, transformation, and loading for the generation of information panels by the Collaborative Support Center for Monitoring and Management of Educational Programs (CECAMPE/NE). Methodology: The employed methodology was quantitative and used typical Data Science tools. The data related to the PDDE were collected from transactional systems and surveys with the involved populations. Results: The results showed a satisfactory reduction of GIGO, although challenges were identified such as data update discrepancies, lack of adequate documentation of data tables, and issues with free typing fields in survey forms. Conclusion: The analysis reiterates that the GIGO concept is a significant challenge for the effective use of PDDE data and emphasizes the need to develop better data management practices in the context of the program. It suggests that all professionals who use data as raw material for their professional activities should be aware of these challenges and work towards effective solutions.
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