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

GuidelinesExamplesDefinitons

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.

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.

Referential integrity

Characteristic Name:	Referential integrity
Dimension:	Consistency
Description:	Data relationships are represented through referential integrity rules
Granularity:	Record
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of referential integrity violations 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:
Implement and maintain foreign keys across tables (Data sets)	(1) Implementation of foreign keys
Implement proper validation rules/Automated suggestions of values based on popular value combinations, to prevent incorrect references of foreign keys	(1) The attribute Customer_Zip_Code of the Customer relation contains the value 4415, instead of 4445; both zip codes exist in the Zip_Code relation
Implement validation rules for foreign keys of relevant tables in case of data migrations	(1) Error logs are generated for foreign key violations.
Implement proper synchronising mechanisms to handle data updates when there are concurrent operations or distributed databases.	(1) Locking mechanisms to data objects while being updated
Ensure the consistency of the data model when changes are done to process model (software)	(1) Data dictionary provides the FDs and CFDs

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain referential integrity

These are examples of how the characteristic might occur in a database.

Example:	Source:
the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
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.

Example:

Source:

the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table

Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.

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:
The Information Float or Lag Time is acceptable between (a) when data is knowable (create or changed) in one data store to (b) when it is also knowable in a redundant or distributed data store, and concurrent queries to each data store produce the same result.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
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.
i.e. integrity rules. Data follows specified database integrity rules.	PRICE, R. J. & SHANKS, G. Empirical refinement of a semiotic information quality framework. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005. IEEE, 216a-216a.