Poorly developed data models are enemy #1 to the widespread, profitable use of machine learning and advanced analytics.  Even at the earliest stages of working with a data set, establishing quality checks and ensuring that you are collecting appropriate data to inform future decisions are critical elements of any product or project roadmap.

In this session join Clark Leininger in discussing how to capture and access relevant data to build the lab of the future. 

The session will discuss: 

·  The move from functionally designed software to strategically designed software products 

·  The importance of thinking beyond immediate concerns when evaluating data 

·  Exploring mechanisms for reducing or eliminating data extraction, transformation, and cleansing