| Definition | Source |
| Data accuracy refers to the degree with which data values agree with an identified source of correct information. There are different sources of correct information: database of record, a similar, corroborative set of data values from another table, dynamically computed values, the result of a manual workflow, or irate customers. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Accuracy to reference source
| Definition | Source |
| The set S should be sufficiently precise to distinguish among elements in the domain that must be distinguished by users. This dimension makes clear why icons and colors are of limited use when domains are large. But problems can and do arise for the other formats as well, because many formats are not one-to-one functions. For example, if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. The trick is to provide the precision to meet user needs. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Precision
| Definition | Source |
| Completeness refers to the expectation that certain attributes are expected to have assigned values in a data set. Completeness rules can be assigned to a data set in three levels of constraints: 1. Mandatory attributes that require a value 3. Inapplicable attributes (such as maiden name for a single male), which may not have a value.2. Optional attributes, which may have a value. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Completeness of mandatory attributes
| Definition | Source |
| 1) A null value is a missing value. However, a value that is missing may provide more information than one might think because there may be different reason that it is missing. A null value might actually represent an unavailable value, an attribute that is not applicable for this entity, or no value in the attribute’s domain that correctly classifies this entity. Of course, the value may actually be missing.
2) When the null value (or absence of a value) is required for an attribute, there should be a recognizable form for presenting that null value that does not conflict with any valid values. |
LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Completeness of optional attributes
| Definition | Source |
| Consistency can be curiously simple or dangerously complex. In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. Two data values drawn from separate data sets may be consistent with each other, yet both can be incorrect. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Value consistency
| Definition | Source |
| Currency refers to the degree to which information is current with the world that it models. Currency can measure how up to date information is and whether is it correct despite possible time-related changes. Timeliness refers to the time. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Data freshness
| Definition | Source |
| The notion of abstracting information into a data domain implies that there are enough users of the same set of data that it makes sense to manage their own versions. The dimension of enterprise agreement of usage measures the degree to which different organizations conform to the usage of the enterprise data domain of record instead of relying on their own data set. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Source quality
| Definition | Source |
| A good presentation provides the user with everything required for the correct interpretation of information. When there is any possibility of ambiguity, a key or legend should be included. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Understandability
| Definition | Source |
| 1) Appropriateness is the dimension we use to categorize how well the format and presentation of the data match the user needs. In our example, there is a difference between a high-level monthly sales report that is supplied to senior management and the daily product manifests that are handed to the shipping department for product packaging.
2) Flexibility in presentation describes the ability of the system to adapt to changes in both the represented information and in user requirements for presentation of information. For example, a system that display different counties; currencies may need to have the screen presentation change to allow for more significant digits for prices to be displayed when there is a steep devaluation in one county’s currency. 3) In an environment that makes use of different kinds of systems and applications, a portable interface is important so that as applications are migrated from one platform to another, the presentation of data is familiar to the users. Also, when dealing with a system designed for international use, the user of international standards as well as universally recognized icons is a sign of system designed with presentation portability in mind. |
LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Appropriate presentation
| Definition | Source |
| As a data quality-oriented organization matures, the agreement of usage will move from a small set of “early adopters” to gradually encompass more and more of the enterprise, Ubiquity measures the degree to which different departments in an organization use shared reference data. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Information value
| Definition | Source |
| This dimension refers to whether instances of data are represented in a format that is consistent with the domain of values and with other similar attribute values. For example, the display of time in a non-military (12-hour) format may be confusing if all other instances of times in the system are displayed in the 24-hour military format. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Related Characteristic: Meta-data compliance