Correctness
Synonym: accuracy
Description: Correctness describes how the data in the dataset correspond to reality. It also helps to identify systematic distortions in the dataset.
Example: The data leading to an operational decision represent the best understanding of the accurate data. For example, the data are considered accurate when the salary declared for tax purposes corresponds to the salary paid.
Indicators: methodically produced values, incorrect values, misclassification
Accuracy
Synonym: unbiasedness
Description: Accuracy describes how well the data in the dataset correspond to what is being sought. It describes how well the data hit the mark.
Examples: Accuracy describes the dispersion of indicator values, the proportion of outliers in the dataset, the accuracy of the classification and the scale of measurement (e.g. decimals, time, coordinates).
Indicators: standard deviation, outliers
Consistency
Synonyms: regularity, logical integrity of data
Description: Consistency indicates that the data are consistent and non-contradictory. The indicator can also be used to describe the consistency between different datasets.
Examples: For example, there is an inconsistency when there are no dwellings in a residential building, or a person’s date of marriage is earlier than their date of birth. Data consistency can be checked by means of validation/qualification rules.
Indicators: logic of data reviewed
Currentness
Description: Currentness describes the timeframe of the data in the dataset. The closer the data baseline period is to the present, the more current the data are. The baseline period is the point in time to which the data apply.
Examples: The baseline period associated with the dataset is provided with the data. It can be used to determine the freshness of the data. The baseline period can be the period between the beginning and the end of the year or a particular day, for example. In data production, it is also important to check the data review and change periods.
Indicators: baseline period, creation period, review period, change period,
Completeness
Synonym: coverage
Description: Completeness describes the temporal and regional target coverage of the data, as well as the target units and characteristics data. It also indicates the degree to which the dataset contains the desired data.
Examples: The dataset covers all units in a defined area, e.g. all enterprises in Finland. Regional coverage indicates whether all the target regions are included in the dataset (e.g. all Finnish municipalities), and if the dataset also covers Åland. Over-coverage indicates that the dataset includes units that do not belong to the dataset. Under-coverage indicates that units belonging to the dataset are missing. Non-response is also included in under-coverage. On the other hand, completeness also indicates whether the dataset contains all the characteristics specified for the target units in the dataset, for example, the details of the population and area of the Finnish municipalities in the dataset, or whether address or turnover data have been provided for all enterprises in the dataset.
Indicators: temporal target coverage, regional target coverage, target units, shortcomings in characteristics, missing units, additional units, incomplete units, incomplete characteristics