Year: 2024
Type: conference-paper
Abstract: This paper discusses the importance of high-quality data required for the successful development and implementation of AI-based Industrial Analytics Business solutions.Advanced analytic systems use a variety of data, which include free-text unstructured, free-text structured, categorical, and numerical data.Data-centric artificial intelligence (data-centric AI) is an emerging concept that underscores the importance of developing and continuously enhancing the quality of data to build and maintain efficient AI-based systems.Data-centric AI nicely complements model-centric AI, which focuses on the development and improvement of AI-based systems based on changes and improvements in the AI models keeping the set of data fixed.This paper discusses the importance of developing capabilities to improve the data quality of AI-based systems within the context of data-centric AI.Special emphasis is placed in ensuring the data fed to the AI solution from original data sources meets the required and expected quality.Examples on inherent data quality issues discovered when building and maintaining AI-based Industrial Analytics Business systems are provided and discussed within the concept of data-centric-AI.Moreover, a discussion on how data quality improvements are made within these systems is presented.
Author: Aldo Dagnino
Institutions: North Carolina State University, ABB (United States)
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4406255324