"cleanse data" is a correct phrase that is often used in written English.
It is often used in the context of data analysis and computer programming. For example, "We need to cleanse the data before running the analysis."
Preprocessing I will load all the raw data, removing duplication data, cleansing data, and select the most important data that have been defined.
In order to implement this idea, several key techniques are illustrated in detail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big data analytic algorithms.
Beyond buying a way to let brands purchase social ads to pair with their campaigns, Shoutlet is eying "companies that are really good at manipulating and cleansing data to make it useful for marketers".
Preprocessing In this step, we refer to an ETL (extraction transformation loading) tool for preparing and cleansing data by transforming the data to a proper format and selecting only certain columns to load.
However, one limitation of the quantitative analysis is that we rely solely on self-reported intravaginal cleansing data from the administered questionnaires.
The terms data cleaning (or data cleansing), data standardisation, data scrubbing, data pre-processing and ETL (extraction, transformation and loading) are used synonymously to refer to the general tasks of transforming source data into clean and consistent sets of records suitable for loading into a data warehouse, or for linking with other data sets.
To cleanse the data of the possibility of the same physician responding multiple times, data with identical answers for physician age, gender, field of practice, and employing institution and having a 75% or greater concordance in responses to the other questions were treated as responses from the same physician.
That company would "cleanse" the data of personal identifiers before returning it to DoubleClick.
