A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

Problem
This paper addresses the gap in existing literature regarding the vulnerability of manual data entry systems to categorical misclassifications, particularly in small and medium-sized enterprises (SMEs). The authors highlight that current normalization tools inadequately handle the complexities of SME data, which often includes custom stock-keeping units (SKUs), abbreviations, and domain-specific jargon. The lack of effective automated input controls leads to irrecoverable errors that can distort Key Performance Indicators (KPIs) and misguide managerial decisions. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the introduction of the Categorical Error Sensitivity Index (ISEC), an ordinal composite score that ranks category pairs based on their susceptibility to confusion. ISEC combines three key components: (1) semantic distance, calculated using word embeddings; (2) custom weighted morphological transformation costs, derived from an adapted Damerau-Levenshtein algorithm; and (3) empirical frequency of category occurrences. This integration allows for a comprehensive assessment of categorical risk. The authors employ vector database architectures to enhance computational efficiency, achieving a performance improvement of approximately 195x over traditional brute-force methods. The training and validation of ISEC were conducted on three heterogeneous datasets: governmental judicial records, retail inventory, and a synthetic ISO-coded metalworking catalog.

Results
ISEC was validated across the aforementioned datasets, demonstrating its effectiveness in identifying latent structural risks in categorical data. While specific numerical results comparing ISEC to baseline methods are not detailed in the abstract, the authors assert that the index significantly outperforms existing normalization tools, which typically evaluate semantic and morphological dimensions in isolation. The performance metrics indicate that ISEC provides a scalable solution for SMEs, enabling proactive data governance.

Limitations
The authors acknowledge that ISEC’s effectiveness may be contingent on the quality and representativeness of the training datasets used for word embeddings and morphological transformations. Additionally, the reliance on empirical frequency may not account for all contextual nuances in categorical data. The paper does not address potential limitations related to the generalizability of ISEC across diverse industries or the computational overhead associated with maintaining vector databases.

Why it matters
The introduction of ISEC has significant implications for downstream work in data governance and error prevention in manual data entry systems. By providing a robust framework for assessing categorical risk, ISEC can enhance decision-making processes in SMEs, ultimately leading to improved data integrity and more reliable KPIs. This work opens avenues for further research into automated data entry systems and the development of more sophisticated normalization tools that can adapt to the unique challenges posed by SME data.

Authors: Ricardo Raúl Palma, Mauro Anibal Benetti, Fabricio Orlando Sanchez Varretti
Source: arXiv:2605.12328
URL: https://arxiv.org/abs/2605.12328v1