How To Use Big Data
Data can be used in biomedical research to understand the inception and development of disease, detect new treatments, and develop new medical products to improve health. Utilizing big data in health and biomedicine is important because it allows for the global exchange of diverse data to supply the best information and improve decision-making. Ultimately big data in health can be used in the development of personalized analyses and treatments that are customized for each individual.
Deriving meaning from health data is one of the main curriculum themes of BD4P and requires an understanding of data types and sources, as well as data science terminology. Use issues are outlined in the training module "Making Big Data Useable." Working with data from different sources presents challenges when data aren't organized in the same way or don't use the same units of measurement. Combining data for analysis can be problematic because of issues of integration between devices or software and interoperability between different information technologies. Structured data, organized into pre-defined models, is often easier to work with than data that is unstructured or collected from new sources like wearable medical devices or personal genonmics information. For patients to use big data, it's important to understand the differences between standalone, federated and distributed databases (an example of which is the Foundation's IMEDS program, which uses a common data model to ensure interoperability and data standardization)