Trust the Data You Use: Scalability Assurance Forms (SAF) for a Holistic Quality Assessment of Data Assets in Data Ecosystems

Abstract

Companies generate terabytes of raw, unstructured data daily, which requires processing and organization to become valuable data assets. In the era of data-driven decision-making, evaluating these data assets' quality is crucial for various data services, users, and ecosystems. This paper introduces "Scalability Assurance Forms" (SAF), a novel framework to assess the quality of data assets, including raw data and semantic descriptions, with essential contextual information for cross-domain AI systems. The methodology includes a comprehensive literature review on quality models for linked data and knowledge graphs, and previous research findings on data quality. The SAF framework standardizes data asset quality assessments through 31 dimensions and 10 overarching groups derived from the literature. These dimensions enable a holistic assessment of data set quality by grouping them according to individual user requirements. The modular approach of the SAF framework ensures the maintenance of data asset quality across interconnected data sources, supporting reliable data-driven services and robust AI application development. The SAF framework addresses the need for trust in systems where participants may not know or historically trust each other, promoting the quality and reliability of data assets in diverse ecosystems.