Data qualities

This is an overview of the DaMa data qualities. It can be extended with the implications for a specific project

Version 1.0
Created date 02-05-2021

Accurateness

Accurateness refers to the degree of which a data entity displays reality. Accurateness can be decided by comparing a data entity with the entity in reality. An example is a a difference between a mailing list of clients and the true clients of an organis

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Accurateness

Actuality

Degree of which a data entity display the current situation of reality. Good examples are deceased people that received a letter from an older data set. Replication of data is often a source of low actuality

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Actuality

Completeness

This refers to the degree in which certain attributes are present within a data entity. In addition to that the completeness also counts for a certain set of entities (rows) within a data set always being present. For example a person could only have the

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Completeness

Consistancy

This refers to the fact that the one data set of a certain entity is equal to another data set. In other words a data entity is always the same regardless of the source. An example of a low consistency is when there are differences between data sets of th

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Consistancy

Precision

Degree of detail in which a data entity displays reality. For example this refers to the precision of numbers and such. Storage of numbers and dates can be insufficiently accurate because rounding is needed in storage. Domains in features can also have in

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Precision

Privacy

For some data entities access control (authorisation and authentication) or monitoring of use is needed. Take for example requirements that are placed on the access of confidential data. In the GBA there are multiple levels of confidentiality. As such que

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Privacy

Reasonableness

Mainly refers to expectations within a certain operational context. Take for example the accepting of a lower performance during peak loads or having to wait a long time on a result set of archived data entities.

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Reasonableness

Referential integrity

This is the situation where referrals from one data entity always correctly refer to the related data entities. Examples are double keys in a data set which makes the connected entities unable to decide which entity is older. Also dangling references or f

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Referential integrity

Timeliness

Is a data set available on time within the set expectations. It is the difference between the moment of need and availability. For example requesting data in a Call Center. In this situation waiting five minutes for a response is not acceptable.

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Timeliness

Uniqueness

Uniqueness of a data entity is focused on the fact that there are no other data entities with the same data. An example from practice was a twin with the same initials, surname and birth date. A distinction could not be made due to the completeness being

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Uniqueness

Validity

This is the degree in which a data entity meets the desired format in storage and exchange. Take for example the domain yet also the datatype of the attributes of a data entity. Within chain exchange for example this is of the highest importance. Nobody w

Author Bert Dingemans
Alias --
Stereotypes Requirement
Details of Validity