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