AI and Machine Learning is hungry for data. However, data for training models is often hard to come by. Often only 15-20% of customers consent to having their data used for analytics. The rest of the data and the insights contained are locked away. Due to privacy reasons, sensitive data is often off-limits both for in-house data science teams and for external AI or analytics vendors.
Even when some data is available, data quality often is an issue. Missing relevant data complicate AI/ML development and negatively impact performance of models. Machine learning accuracy suffers when training data quality is insufficient. Training models on easy to access, fresh, balanced training data is not possible for most of the data scientists today.