dataarvot

Dataarvot is a term used in Finnish-language data science and information technology to refer to the values of the data fields in a data set. Each dataarvo represents a specific attribute or measurement for a given record, such as a person’s height, a timestamp, or a category label. Dataarvot are stored in data structures according to their data type and are subject to rules for validity and unit consistency.

Common dataarvot types include numeric values (integers and floating-point numbers), categorical values (nominal and ordinal), text strings, boolean values, and date/time values. Numeric data can be used in mathematical operations, while categorical data are typically encoded for analysis. Text and free-form data often require preprocessing and normalization to be usable in analyses.

Dataarvot quality is a central concern in data management. The reliability of analyses depends on accuracy, completeness, consistency, timeliness, and absence of duplicates. Practices such as data validation, schema enforcement, data provenance, and metadata documentation help ensure dataarvot are trustworthy.

During data processing, dataarvot may be transformed: scaling numeric values, encoding categories, handling missing values, and standardizing units. Proper handling of dataarvot is essential for analytical methods, from simple statistics to machine learning models.

See also data value, data quality, data governance.