Product data quality excellence platform with predictive machine learning module for quality data control and automation in FMCG - Kellermann Slotemaker, Petra (2024)
The aim of this thesis is to develop and implement the Product Data Quality Excellence (DQX) platform, designed to address critical needs in Nestlé CEE's product data management by revising current processes, enhancing data quality transparency, and reducing manual workload through automation of data quality control process, and ultimately achieving long-term improvement in product data quality. The research employs both secondary and primary methodologies. Secondary research involves a thorough review of existing literature, while primary research includes data analysis and iterative feedback from users. The project leverages Product DQX platform based on various programming skills and software tools, including Python, SQL, Microsoft Power BI and predictive models based on neural networks, anomaly detection, or natural language processing (NLP). The developed Product DQX platform integrates data management, ETL modules, data quality dashboards, and predictive machine learning techniques. The project achieved its objectives, by recommending several changes in data management, providing much needed transparency by data quality dashboard linked to the key KPIs and data stewards, providing insights for further data quality improvements as well as integrating automated data quality control for key attributes. The Product DQX platform provides a comprehensive, cost-effective solution for product data management matching company maturity and needs, offering tool with direct impact on data quality KPIs. Provided prototype is paving the way for further robust deployment.