For example, this step may involve creating a new field in the data file that aggregates counts from preexisting fields, or applying a statistical formula -- such as a linear or logistic regression model -- to the data.

After going through the workflow, data is output into a finalized file that can be loaded into a database or other data store, where it is available to be analyzed.

Data is often created with missing values, inaccuracies or other errors.

Additionally, data sets stored in separate files or databases often have different formats that need to be reconciled.

Many of the tokens issued by the v2.0 endpoint are implemented as JSON Web Tokens (JWTs).

A JWT is a compact, URL-safe way to transfer information between two parties. It's an assertion of information about the bearer and subject of the token.

With a formal data preparation process in place, repetitive analyses can be fed data automatically, rather than requiring users to locate and cleanse their data each time.

The Azure Active Directory (Azure AD) v2.0 endpoint emits several types of security tokens in each authentication flow.Machine learning algorithms can speed things up by examining data fields and automatically filling in blank values or renaming certain fields to ensure consistency when data files are being joined.After data has been validated and reconciled, data preparation software runs files through a workflow, during which specific operations are applied to files.Even though data preparation methods have become highly automated, it can still take up significant amounts of time -- especially as the volume of data used in analyses continues to grow.Data scientists often complain that they spend a majority of their time locating and cleansing data rather than actually analyzing it.Partly for that reason, there has been an increase in the number of software vendors attempting to tackle the data preparation problem, and many organizations are putting more resources toward automating data preparation.