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.

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 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.