Format consistency
| Characteristic Name: | Format consistency |
| Dimension: | Consistency |
| Description: | Data formats are consistently used |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of inconsistent data formats reported in an attribute per thousand records |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Maintain consistent formats for data values across different data bases and different tables in the same database. | (1) Telephone number : Country code/Area code/number (2) Address : House number, Street, Suburb, Sate, Country |
| Maintain structural similarity or compatibility of entities and attributes across systems (databases/data sets) and across time. | (1) Customer record has the same structure in all systems which it is being used. |
| Maintain consistent and compatible encoding /decoding standards across different applications. | (1) ASCII, UTF-8, XML |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain format consistency |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters).
2) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers. 3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed. |
N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue. | 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: |
|---|---|
| A measure of the equivalence of information stored or used in various data stores, applications, and systems, and the processes for making data equivalent | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The extent to which similar attributes or elements of an information object are consistently represented using the same structure, format, and precision. | 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. |
Redundancy
| Characteristic Name: | Redundancy |
| Dimension: | Consistency |
| Description: | The data is recorded in exactly one place |
| Granularity: | Record |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The volume of redundant data as a percentage to total data |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Maintain the database schema eliminating the causes for redundancies of entities and attributes | (1) All customers are in customer table |
| Ensure that there are no redundant records across distributed databases | (1) Organisation has different customer bases maintained in different databases. But one customer is available only in one database |
| Ensure that same entity is not originally captured more than once in the systems | (1) Medical Insurance system refers employee bank details from the payroll. |
| Ensure that there are no temporary table backups are available in the database | (1) Created a backup for employees as employee_temp for a specific purpose and it is still in the database |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to eliminate the occurrence of redundant data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2% | 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 unwanted duplication existing within or across systems for a particular field, record, or data set. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| There is only one record in a given data store that represents a Single Real-World Object or Event. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Determines the extent to which the columns are not repeated. | G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc. |