Utilizing big data to guide decision-making for environmental health outcomes can provide the next level of health outcome improvements on a population basis.

Historical shifts in overall health and longevity came with environmental health interventions such as safe food and water supplies, the treatment of waste and the establishment of standards that have reduced acute illnesses in the population.

Big data analysis approaches have the potential to have a similar impact on quality and length of life by analyzing the factors leading to chronic illness in the population, and improving outcomes. Through the use of big data and machine learning, we can learn more about the environmental factors affecting population health. This article presents an opportunity to utilize pre-existing data to explore a novel way of assessing the impact of known health hazards. This is demonstrated by using drinking water test results as a case example. We demonstrate how big data analytics can be used in such a scenario to identify environmental public health risk. This approach is beginning to be used to collect new and better organized data with the intent of improving population health outcomes.