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. |
Data awareness
| Characteristic Name: | Data awareness |
| Dimension: | Availability and Accessability |
| Description: | Data users should be aware of all available data and its location |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data awareness |
| The number of complaints received due to lack of continuity in data awareness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Educate users on the data landscape of the organisation and how to access the information when needed. | (1) Training programs are conducted to educate on using the systems and retrieve data when necessary. |
| Provide appropriate searching tools, manuals to find the required information. | (1) Data catalogs (2)Data Searching facility provided in application programs |
| Educate users on how data is collected and what are the procedures used in data collection so that they can decide on the appropriateness of the data for use. | (1) Opinion pole data during festival season is not appropriate to analyse drinking habits. |
Validation Metric:
| How mature is the process of creating the awareness of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly. | B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Accessibility of data refers to how easily it can be accessed; the awareness of data users of what data is being collected and knowing where it is located. | 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. |