University of Twente Student Theses
Optimizing Data Quality with a Scoring-Based Cleansing Framework
Pindya, Nabila (2024) Optimizing Data Quality with a Scoring-Based Cleansing Framework.
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| Abstract: | The effectiveness of business decisions heavily relies on the quality of the data used in the decision-making process. Poor-quality data misleads business decisions and leads to financial loss for a company. Therefore, the quality of data is of utmost importance. However, maintaining high-quality data is challenging, especially in the manufacturing domain, due to its inherent complexity and ever-changing nature. Thus, this research focuses on minimizing errors, enhancing efficiency, and guaranteeing high-quality data for decision-making. The research strives to develop a framework to enhance data quality. The research examined the past literature to reveal which attributes are significant to the data quality. The proposed framework primarily focuses on four dimensions of data quality: accuracy, completeness, consistency, and timeliness, and consists of four stages: Assess, Action, Enhancement, and Quality Scoring. Significant component of the framework is the scoring process, pre-scoring and post-scoring. During this stage, the data undergoes evaluation to determine overall quality. From the validation, accuracy was 95.75%, Consistency was 99.98% before the cleaning process, and scores for these two dimensions reached 100% after the cleansing process was completed. Consequently, this research significantly contributes by offering an effective method for enhancing data quality in the manufacturing sector, facilitating more accurate decision-making. |
| Item Type: | Essay (Master) |
| Clients: | Tembo Group, Kampen, Netherlands |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Programme: | Business Information Technology MSc (60025) |
| Link to this item: | https://purl.utwente.nl/essays/103281 |
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