As baby boomer reserving actuaries start to retire, new actuaries are inheriting reserving processes and don't have the benefit of 20-30 years of observations. Expectations are also higher than ever as auditors tend to question 'actuarial judgment' as acceptable justification. In the current age of data science, with access to state of the art data warehouses and statistical software, these knowledge gaps can be filled with new reserving techniques and solutions. Data analytics can help answer key reserving questions like which lag method to select, how to blend PMPM forecasts with lag estimates, estimation of trend, and much more. This session will introduce applicable examples and set up a forum to discuss solutions for some of the key challenges health reserving actuaries face today.