Precision

Characteristic Name:	Precision
Dimension:	Accuracy
Description:	Attribute values should be accurate as per linguistics and granularity
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of tasks failed or under performed due to lack of data precision

The number of complaints received due to lack of data precision

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Ensure the data values are correct to the right level of detail or granularity	(1) Price to the penny or weight to the nearest tenth of a gram. (2) precision of the values of an attribute according to some general-purpose IS-A ontology such as WordNet
Ensure that data is legitimate or valid according to some stable reference source like dictionary/thesaurus/code.	(1) Spellings and syntax of a description is correct as per the dictionary/thesaurus/Code (e.g. NYSIIS Code) (2) Address is consistent with global address book
Ensure that the user interfaces provide the precision required by the task	(1) if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values.
Ensure the data values are lexically, syntactically and semantically correct	(1) “Germany is an African country” (semantically wrong); Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong)

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain data precesion

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

Example:	Source:
if v = Jack,even if v = John, v is considered syntactically correct, as Jack is an admissible value in the domain of persons’ names	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.

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

Definition:	Source:
Data values are correct to the right level of detail or granularity, such as price to the penny or weight to the nearest tenth of a gram.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Data is correct if it conveys a lexically, syntactically and semantically correct statement – e.g.,the following pieces of information are not correct:“Germany is an African country” (semantically wrong);Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong).	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
The set S should be sufficiently precise to distinguish among elements in the domain that must be distinguished by users. This dimension makes clear why icons and colors are of limited use when domains are large. But problems can and do arise for the other formats as well, because many formats are not one-to-one functions. For example, if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. The trick is to provide the precision to meet user needs.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Is the information to the point, void of unnecessary elements?	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
The degree of precision of the presentation of an attribute’s value should reasonably match the degree of precision of the value being displayed. The user should be able to see any value the attributer may take and also be able to distinguish different values.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.
The granularity or precision of the model or content values of an information object according to some general-purpose IS-A ontology such as WordNet.	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.