A Model-Driven Pipeline for Data Quality Specification and Operationalization: A No-Code Approach for Domain Experts

2026-07-27

AI Summary

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

Abstract

High-quality data is essential for reliable analysis, decision-making, and research across domains. This is especially relevant in areas such as cultural heritage, where data is collected and curated manually, making it prone to quality issues like inconsistencies. To improve data quality, the data must be analyzed regularly using systematic quality analyses. Quality analyses validate the conformance of data to domain-specific expectations. These expectations are best understood by domain experts, who can express them using natural language. However, they rarely possess the technical expertise to formalize these expectations into executable quality analyses. Consequently, this process requires domain experts and data engineers, making it time-consuming and technically demanding. The required technical expertise and the resulting dependencies pose a significant challenge. To address this challenge, we present a pipeline for formalizing and operationalizing data quality constraints. We support this pipeline using QPM, a metamodel for defining templates for reusable quality analyses. The web application Constrainify enables tailoring templates to specific conceptual requirements and translating them into executable quality analyses via a tool-chain based on model-driven engineering subpipelines. The result is a set of reusable, repeatable, and domain-specific quality analyses.

Problem

• data quality
• domain experts
• no-code
• quality constraints
• operationalization

Innovation

• no-code
• model-driven engineering
• data quality specification
• domain experts
• quality analysis automation