While advancements in health sciences, pharmaceuticals, and molecular biology continue to move at rapid speed, the key to the next major breakthrough will require standardization and integration of disparate data in order to see a more complete picture. For example, ability to integrate proteomics (study of proteins) data with metabolomics (study of metabolites) data provides deeper understanding of the biological pathways of disease, getting us closer to a cure. On a broader scale, data related to drug discovery examined in conjunction with large-scale genomics data sheds light on the ways specific drugs affect different groups of people at the genetic level, as opposed to a “one size fits all” approach. Highly proprietary life sciences fields are becoming more accepting of open source technology, as they move away from data silos and seek interdisciplinary collaboration. Various open source technologies such as Python, Hadoop, Spark, Jupyter (big data), NoSQL, R, and Galaxy (bioinformatics) are already paving the way to the standardization and integration of data that is so vital to the advancement of “multi-omics” and personalized healthcare.