Peningkatan Kualitas Data dalam Konsolidasi Data Karyawan melalui Data Wrangling
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 the...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Dian Nuswantoro, Fakultas Ilmu Komputer
2025-02-01
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| Series: | JOINS (Journal of Information System) |
| Subjects: | |
| Online Access: | https://publikasi.dinus.ac.id/index.php/joins/article/view/9423 |
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| Summary: | 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. |
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| ISSN: | 2528-0228 2528-0236 |