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Data integration & quality software
The Next Wave of technology & innovation


Your AI and ML Projects Are Failing – Key Steps to Get Them Back on Track

With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.

View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:

  • Framing the business problem
  • Identifying the “right” data to collect and work with
  • Establishing baselines of data quality through data profiling and business rules
  • Assessing fitness for purpose for training and evaluating the subsequent models and algorithms