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.

Data access control

Characteristic Name:	Data access control
Dimension:	Availability and Accessability
Description:	The access to the data should be controlled to ensure it is secure against damage or unauthorised access.
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 access control

The number of complaints received due to lack of data access control

GuidelinesExamplesDefinitons

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines:	Scenario:
Periodically evaluate the security needs considering the criticality of data (Value, confidentiality, privacy needs etc.) and accessibility requirements of data and then update the information security policy consistently.	(1) Employee salary is a confidential data and hence need security against unauthorised access. (2) Master data has a high economic value to the organisation and hence need security against unauthorised access and change
Continuously evaluate the risks threats and identify the vulnerabilities for data and update the information security policy	(1) The frequency of security assessment for data associated with online transactions was increased due to the high volume of online transactions.
Implementation of access controls for each critical information as prescribed by the information security policy.	(1) An Employee’s salary data can be viewed only by his or her superiors. (2) Master data can be created and updated only by the authorised executives. (3) Login credentials are required for system access
Data is stored in secured locations and appropriate backups are taken	(1) Databases are stored in a special server and backups are taken regularly (2) Documents are saved using a content management system in a file server
Restrict the accessibility of information using software based mechanism	(1) Data encryption (2) Firewalls
Restrict the accessibility of information using hardware based mechanism	(1) Security tokens

Validation Metric:

How mature is the process of ensuring data access control

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

Example:	Source:
if the ofﬁcial version of the minutes of a meeting is ﬁled by the records manager and thus protected from change, the unauthorised version will not form part of the ofﬁcial record.	K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

The Definitions are examples of the characteristic that appear in the sources provided.

Definition:	Source:
Is the information protected against loss or unauthorized access?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Data is appropriately protected from damage or abuse (including unauthorized access, use, or distribution).	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.
The extent to which information is protected from harm in the context of a particular activity.	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.
Access to data can be restricted and hence kept secure.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.