Leveraging AI/ML to Transform Data Quality Reviews at Scale

This closing session for the Analytics track explores Siemens' innovative use of machine learning to revolutionise data quality management. Faced with the overwhelming task of manually reviewing large data sets, Siemens developed a virtual assistant that not only detects errors but also significantly enhances data accuracy and engineer productivity. Discover how this tool reduces time spent on data checks by 75%, performing what used to take hours in just minutes.

Efficient data management is critical for business success, particularly in industries reliant on big data. Siemens' solution exemplifies how integrating machine learning with existing data systems can drive substantial business value, cutting costs and dramatically improving operational efficiency.

This session will explore:

  • Introduction to the challenges of big data management.
  • The development and integration of Siemens' virtual assistant.
  • Key features of the machine learning model used.
  • Impact analysis: time savings, error reduction, and cost efficiency.
  • Scalability and future applications in data quality management.

Learning outcomes:

  • Understand the necessity and benefits of automating data quality checks.
  • Gain insights into the process of developing a machine learning-powered virtual assistant.
  • Learn about the scalability and potential future applications of machine learning in data management.
  • Analyse the business impact of enhanced data accuracy and efficiency.