Typically, raw data cannot be used directly in data analytics applications. This may be because the application requires the input data to be in a certain format, or have numerical data type, or the data may contain noise and errors, and various other statistical constraints. For these reasons, data needs to be preprocessed to meet the constraints of the analytics application. Data preparation, also referred to as “data prep,” is essentially about transforming data into a format that’s usable for analytics. It’s the same role as transformation (the "T" in ETL, extract-transform-load), but whereas ETL is about formatting data in a systematic process, for example loading data into a data warehouse, data prep is often done by an individual, such as a data scientist for their individual requirements.

Bodo can help with large data preparation jobs in a fraction of time compared to traditional approaches, and can therefore cut 90% of both time and hardware costs.