| Definition | Source |
| Timeliness refers to the time expectation for accessibility and availability of information. Timeliness can be measured as the time between when information is expected and when it is readily available for use. For example, in the financial industry, investment product pricing data is often provided by third-party vendors. As the success of the business depends on accessibility to that pricing data, service levels specifying how quickly the data must be provided can be defined and compliance with those timeliness constraints can be measured. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Data punctuality
| Definition | Source |
| Data accuracy refers to the degree with which data correctly represents the “real-life” objects they are intended to model. In many cases, accuracy is measured by how the values agree with an identified source of correct information (such as reference data). There are different sources of correct information: a database of record, a similar corroborative set of data values from another table, dynamically computed values, or perhaps the result of a manual process. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Accuracy to reference source
| Definition | Source |
| Is the information to the point, void of unnecessary elements? | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Precision
| Definition | Source |
| An expectation of completeness indicates that certain attributes should be 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, 2. Optional attributes, which may have a value based on some set of conditions, and 3. Inapplicable attributes, (such as maiden name for a single male), which may not have a value. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Completeness of mandatory attributes
| Definition | Source |
| Asserting uniqueness of the entities within a data set implies that no entity exists more than once within the data set and that there is a key that can be used to uniquely access each entity. For example, in a master product table, each product must appear once and be assigned a unique identifier that represents that product across the client applications. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Uniqueness
| Definition | Source |
| In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. A strict definition of consistency specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Value consistency
| Definition | Source |
| Assigning unique identifiers to objects (customers, products, etc.) within your environment simplifies the management of your data, but introduces new expectations that any time an object identifier is used as foreign keys within a data set to refer to the core representation, that core representation actually exists. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Referential integrity
| 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 it is correct despite possible time-related changes. Data currency may be measured as a function of the expected frequency rate at which different data elements are expected to be refreshed, as well as verifying that the data is up to date. For example, one might assert that the contact information for each customer must be current, indicating a requirement to maintain the most recent values associated with the individual’s contact data. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Data freshness
| Definition | Source |
| Is the information understandable or comprehensible to the target group? | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Understandability
| Definition | Source |
| This dimension refers to whether instances of data are either store, exchanged, or presented in a format that is consistent with the domain of values, as well as consistent with other similar attribute values. Each column has numerous metadata attributes associated with it: its data type, precision, format patterns, use of a predefined enumeration of values, domain ranges, underlying storage formats, etc. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
Related Characteristic: Meta-data compliance