Our collaborative research team is focused on generating impact in cancer control for Canadians using novel data approaches and analytics. This presentation will highlight several of the data-driven research initiatives underway to advance cancer control efforts in Canada. Over the past several years we have been developing the Canadian Cancer Statistics publications and data dashboard. This work has highlighted emerging trends in early-onset cancers in Canada. Our team has been using various data platforms to model and examine the impacts of these trends for breast and colorectal cancer. To evaluate the impact of early detection, we have been using microsimulation approaches to model these trends at the population-level and examine whether screening guideline modifications should be considered. This modeling work is being utilized by the provincial screening programs to make evidence-based decisions. Given that these trends have resulted in higher numbers of patients being diagnosed, our team has also been using machine learning approaches with large administrative health data to model the uncertainty around clinical management in these patient populations. We have been applying causal inference approaches to identify optimal treatment strategies in real-world clinical populations where these populations have been under-represented in classic randomized controlled trials. To transform these analytical approaches to impact, we are developing provider and patient-facing tools based on efforts to reduce uncertainty and improve outcomes and experiences. These tools are being developed in collaboration with patients and family members as well as clinicians. Results of these analyses will be presented in the context of national efforts to guide cancer control strategies using data for these emerging populations in Canada. Despite progress, the myriad challenges to and opportunities for implementation to increase impact will be discussed.