Most analytical journeys start the same way – with a question. When did we have the highest profit? Which customers are driving growth? What segment should we look at next? In traditional analytic workflows, turning those questions into answers often requires navigating menus, configuring visuals, writing calculations and interpreting results
For years in enterprise analytics, the focus was on capability. More data. Faster computing. Stronger models. Organizations have invested heavily in modern data platforms to close the sophistication gap. That gap is largely closed. What hasn’t closed is the execution gap. Platform teams are discovering something: even in highly mature
Building a machine learning pipeline in SAS® Model Studio is rarely a straight line. You start with a goal in mind but quickly find yourself navigating many decisions: understanding the nuances of your data, identifying and fixing data quality issues, choosing models for the task and evaluating training results to
