Russian researchers have proposed a system for evaluating the quality of industrial data
Russian researchers have unveiled a unified framework for evaluating the quality of industrial data, a step that could significantly accelerate the rollout of AI‑driven smart manufacturing across the country
Data Quality as the Foundation for Industrial AI
Researchers at the Applied Artificial Intelligence Institute of Plekhanov Russian University of Economics, working jointly with colleagues from the Polytechnic University of Madrid, have introduced a new framework designed to bring artificial intelligence deeper into industrial operations. Their approach establishes a universal, end‑to‑end method for managing the quality of production data.
The core principle is continuous oversight. The system evaluates data at every stage—collection, processing, application, and even disposal. Earlier solutions were fragmented and lacked a unified standard.
This matters because data quality remains one of the most critical challenges in modern industry. Inaccurate or incomplete information can undermine even the most sophisticated algorithms.
Without Good Data, You Have No Results
This development paves the way for fully automated smart factories. Reliable data is the backbone of digital twins and autonomous control systems.
A Boost for Competitiveness and Digital Transformation
The methodology is designed to help organizations of all sizes—from major industrial holdings to midsize enterprises—overcome the persistent barriers to digital transformation. For Russia’s manufacturing sector, the approach offers a direct path toward higher efficiency and global competitiveness.
