Semantic consistency
| Characteristic Name: | Semantic consistency |
| Dimension: | Consistency |
| Description: | Data is semantically consistent |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of semantically inconsistent data reported per thousand records |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Ensure that semantics of data is consistent within/across applications | (1) All orders placed by the customers are called “Sales order” in all tables/databases. (2) Anti-example: Payment type ( Check) Payment Details (Card type, Card number) |
| Maintenance of data dictionary or standard vocabularies of data semantics | (1) Data dictionary provides technical data as well as semantics of data |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain semantic consistency |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database. | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| A company has a color field that only records red, blue, and yellow. A new requirement makes them decide to break each of these colors down to multiple shadings and thus institute a scheme of recording up to 30 different colors, all of which are variations of red, blue, and yellow. None of the old records are updated to the new scheme, as only new records use it. This data- base will have inconsistency of representation of color that crosses a point in time. | J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Data about an object or event in one data store is semantically Equivalent to data about the same object or event in another data store. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Data is consistent if it doesn’t convey heterogeneity, neither in contents nor in form – anti examples: Order.Payment. Type = ‘Check’; Order. Payment. CreditCard_Nr = 4252… (inconsistency in contents); Order.requested_by: ‘European Central Bank’;Order.delivered_to: ‘ECB’ (inconsistency in form,because in the first case the customer is identified by the full name, while in the second case the customer’s acronym is used). | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| The extent of consistency in using the same values (vocabulary control) and elements to convey the same concepts and meanings in an information object. This also includes the extent of semantic consistency among the same or different components of the object. | STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733. |
Standards and regulatory compliance
| Characteristic Name: | Standards and regulatory compliance |
| Dimension: | Validity |
| Description: | All data processing activities should comply with the policies, procedures, standards, industry benchmark practices and all regulatory requirements that the organization is bound by |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due non adherence of standards and regulations |
| The number of complaints received due to non adherence to standards and regulations |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify the policies, procedures, standards, benchmark practices and any regulatory requirements that an Information object is bound by | (1) Each person's compensation criteria must be determined in accordance with the Annuities Based on Retired or Retainer Pay law. |
| Ensure that all data processing activities are well defined and documented based on the policies, procedures, standards, benchmarks and regulatory requirements. | (1) Process of making a damage estimate is well defined based on industry benchmarks |
| Ensure that the application programs cater for standards and regulatory compliance | (1) A software program to make damage estimates which includes all benchmark data |
| Regularly monitor the data processing activities and identify the problems and inefficiencies so that the corrective and preventive actions can be taken. | (1) Frequent delays in time sheet approvals results in delayed payments |
| Signs should be standardised and universally used | (1) In the line efficiency report, low efficiency lines are indicated using a RED light while a green light indicates high efficiency |
| Relevant standard, procedures, policies and regulations should be communicated to the users effectively | (1) Providing a guidelines for signs |
| Ensure that proper conversion tables are maintained and used in converting attribute vales to different measurement bases. | (1) Metric conversion tables are used to convert lbs to kgs. |
Validation Metric:
| How mature is the process maintain the adherence to standards and regulations |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid. | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the existence, completeness, quality, and documentation of data standards, data models, business rules, metadata, and reference data. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The data element has a commonly agreed upon enterprise business definition and calculations. | B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation. |
| SIGNS AND OTHER Information-Bearing Mechanisms like Traffic Signals should be standardized and universally used across the broadest audience possible. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Validity of data refers to data that has been collected in accordance with any rules or definitions that are applicable for that data. This will enable benchmarking between organisations and over time. | HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality. |