Data Quality Quantized Framework: Ensuring Large-Scale Data Integration in Gig Economy Platforms

Integrating large volumes of data in gig economy platforms poses significant difficulties, especially regarding data integrity. Since these platforms frequently compile data from multiple sources, problems concerning accuracy, completeness, and consistency are common. To address these issues, we suggest a Data Quality Quantized Framework explicitly tailored for this setting. Our system utilizes automated data profiling methods that constantly oversee data in real-time, allowing for the detection and rectification of irregularities. Moreover, we utilize machine learning methods to predict possible data quality issues by examining past data, allowing for proactive actions. Customizing quality metrics to align with the unique attributes of gig economy data guarantees successful integration across different sources. Thorough assessments performed on datasets from prominent gig platforms show significant improvements in data quality indicators after implementing our framework, highlighting its promising ability to enhance operational workflows and facilitate informed decision-making.