NewWave’s data wrangling includes the ingestion of raw data from a multitude of sources, ensuring quality and format compatibility of the data received, and categorizing and mapping the data to the correct environment for its intended use. For our healthcare data that is utilized by authorized researchers, our data wrangling includes not only ingestion of raw data, quality control, and categorization, but also the depersonalization of data for general research use and careful access control and sensitive information security measures. Once the data has been wrangled and structured, NewWave mines the data for the knowledge and insights that our client is seeking. For some of our clients this looks like compiling and continuously updating data sets to be used in outside analytics conducted by researchers or client leads. For other clients, we take the data management process a step further, conducting data analytics targeted at producing insights and performance information specified by the client.
For our data environment building, management, and analytics tooling, we often utilize Python based coding. Python provides the flexibility that allows us to easily customize our customer’s data environments to match their unique needs. Python is a widely adopted programming language, this allows for enhanced data analytics tooling compatibility, increasing the versatility of our multifaceted data management solutions, and enabling the growth of your data management ecosystem with your needs.
rts. The cloud offers native tools that scale storage space in use to match system needs, ensuring our clients are only paying for the space actively in use.