Webinar: Scale Data Integrity: Leveraging AI in Master Data Management for Large Organizations

Summary 

Katrina Youdale introduced the webinar on scaling data with integrity, featuring speakers Simon Ellis and Dave Curtis. Simon emphasized the importance of data quality for AI effectiveness, highlighting the need for consistent data governance policies and accurate training data. David discussed the role of AI in supporting data management processes and shared a case study of a global agriscience business that used AI to enhance their material master record set, resulting in a significant return on investment. 

Simon Ellis:  Data Quality and AI in Procurement 

Simon discussed the exponential growth of data generated by organizations and the hype surrounding AI. He highlighted that despite significant digital investment, there is still scope for greater digitization in procurement, with a focus on spend analysis. Simon emphasized the importance of data quality for AI to be effective, stating that for every $1 spent on AI, $5 should be spent on data quality. He also pointed out that poor data input by humans is a significant challenge, and without a data governance framework, data quality will suffer. Simon concluded by stressing the need for accurate training data for AI systems to classify and process spend transactions effectively. 

Dave Curtis : Data Governance and AI Implementation Discussion 

David emphasized the importance of data governance in AI implementation, stating that AI is not omnipotent and cannot fix all issues. He highlighted the need for consistent data governance policies for long-term success. Dave then took over, discussing the importance of data quality and the role of AI in supporting data management processes. He stressed that AI is not a replacement for these processes but a supporting technology. Dave also discussed the use of AI in data handling scenarios such as ERP migrations, auto-classification, mergers and acquisitions, compliance and regulatory, and data quality. He concluded with a case study of a global agriscience business that used AI to enhance and fix their material master record set, resulting in a 110% return on investment. 

 
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