Problem
Research questions and friction points this paper is trying to address.
data quality
evaluation metric
error-based metric
accuracy
data cleaning
Rethinking Accuracy: A Weighted Error-Based Metric for Data Quality
🤖 AI Summary
This work addresses the lack of a universal, tunable, and multi-scenario-compatible metric for data quality assessment, which hinders effective comparison of diverse data cleaning pipelines. To overcome this limitation, the authors propose TOMME—a general-purpose data quality measurement framework based on weighted errors—that extends traditional accuracy into a configurable, composite score. By producing a single quantitative metric, TOMME enables flexible adjustment of error weights according to specific use cases, thereby supporting both automated processing and optimization requirements. Experimental results demonstrate that TOMME exhibits strong adaptability, practicality, and comparability across a variety of scenarios, offering an efficient and unified solution for data quality evaluation and decision-making.
📝 Abstract
Real data often contains errors, which is why data engineers spend a lot of time creating data cleaning pipelines to ensure the best possible data quality. However, it is often difficult to compare the results of different pipelines and decide which pipeline leads to the best results. There are many different metrics that are designed for different use cases, but they often take only a portion of the data into account. There is a lack of universally applicable metrics for measuring data quality that can be used in many different scenarios. That is why in this paper we are presenting TOMME - an initial approach to a universally applicable weighted error-based metric for data quality. This allows the data quality of a dataset to be assessed based on a single score. While a detailed data quality evaluation remains important, the use of a single score enables rapid assessment and automated processing, for example, for optimization algorithms. By using different weights, the score can also be precisely adjusted to the specific use case. That is why we named it TOMME, which stands for "The One Metric Measuring Errors". As the name suggests, it measures errors in the data. It can thus be considered a generalized, weighted form of accuracy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
data quality
weighted error metric
TOMME
accuracy generalization
universal evaluation metric
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