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All Journal JURNAL SISTEM INFORMASI BISNIS Journal of Information System SEEIJ (Social Economics and Ecology International Journal) Jurnal ULTIMA InfoSys
Teguh Prasandy, Teguh
STMIK Provisi Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences Other

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Journal : Journal of Information System

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Peningkatan Kualitas Data dalam Konsolidasi Data Karyawan melalui Data Wrangling Joni, Joni; Prasandy, Teguh
JOINS (Journal of Information System) Vol. 9 No. 2 (2024): Edisi November 2024
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v9i2.9423

Abstract

Data wrangling is a critical process in data preparation that can significantly improve data quality. Effective data wrangling techniques, which consists of 6 steps i.e. Discovery, Structuring, Cleaning, Enriching, Validating, Publishing, can help Corporate Human Resource Division to ensure that their data is of high quality and ready for analysis. In this case study, we explore how effective data wrangling techniques can be used to improve data quality in employee data consolidation. We found employee data downloaded from various sources, captured incomplete, unreliable, or incorrect so that it could affect data analysis. Data wrangling seeks to remove that risk by ensuring data is in a reliable state before it’s analyzed and leveraged. We analyze a dataset from multiple sources of employee data and demonstrate how data wrangling techniques can be used to clean and transform the data to improve data quality and ready for analysis. Our study provides empirical evidence of the impact of data wrangling on data quality and highlights the importance of this process in employee data consolidation and provide workforce analytics.
Co-Authors Achmad Solechan Alam, Doni P. Joni Joni Murmanto, Imanuel Revelino Nugroho J. Setiadi