The Business Analysis Benefits -- and Limitation -- Of AI and Synthetic Data

Enterprises increasingly deploy machine learning models to analyze corporate data that informs vital business issues. Identifying and gathering high quality data to develop these models is hugely challenging, however, as some information is protected by regulatory or proprietary reasons. Quality data collection is also expensive and time-consuming, leaving huge gaps in AI and machine learning model development. The development of synthetic data and explainable AI is helping fill these gaps by helping train machine learning models or develop new algorithms with insights that are readily available for data scientists. In this fireside chat with machine learning scientist Vincent Granville, he'll discuss: o Synthetic data design techniques, and how to identify business processes where most useful; o How to test the quality of synthetic data; o The benefits, and potential detriments, of explainable AI; o Common modern enterprise data issues, such as managing unbalanced, inconsistent, small, outdated, and unstructured data; o Ways to address data leakage, as well as small, wide and unobserved data; o How synthetic data, explainable AI and other modern technologies are used to overcome these issues.