Completeness of optional attributes

Characteristic Name:	Completeness of optional attributes
Dimension:	Completeness
Description:	Optional attributes should not contain invalid null values
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of invalid null values reported in an optional 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:
Provide default values for each valid case of null values for the attribute in concern so that null values occur only for actually missing values which are invalid cases for the attribute in concern.	Case1: Attribute values that are not defined for a particular entity instance (e.g.: Maiden name of unmarried women ) Such instances will get the default value “NOT DEFINED” Case2 : Attribute values that are defined for the entity instance whereas the real value for the attribute instance is null (eg: Vehicle number of a student who does not have a vehicle) Such instances will get the default value “NOT EXIST” Case3: Attribute values are defined for the entity instance and the attribute instance should have a value (Student’s date of birth).

Guidelines:

Scenario:

Provide default values for each valid case of null values for the attribute in concern so that null values occur only for actually missing values which are invalid cases for the attribute in concern.

Case1: Attribute values that are not defined for a particular entity instance (e.g.: Maiden name of unmarried women ) Such instances will get the default value “NOT DEFINED”

Case2 : Attribute values that are defined for the entity instance whereas the real value for the attribute instance is null (eg: Vehicle number of a student who does not have a vehicle) Such instances will get the default value “NOT EXIST”

Case3: Attribute values are defined for the entity instance and the attribute instance should have a value (Student’s date of birth).

Validation Metric:

How mature is the creation and implementation of the DQ rules to define valid null cases

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

Example:	Source:
Let us consider a Person relation with the attributes Name, Surname, BirthDate,and Email. The relation is shown in Figure 2.2. For the tuples with Id equalto2,3,and 4, the Email value is NULL. Let us suppose that the person represented by tuple 2 has no e-mail: no incompleteness case occurs. If the person represented by tuple 3 has an e-mail, but its value is not known then tuple 3 presents an incompleteness. Finally, if it is not known whether the person represented by tuple 4 has an e-mail or not, incompleteness may not be the case. ID 1 2 3 4 Name John Edward Anthony Marianne Surname Smith Monroe White Collins BirthDate 03/17/1974 02/03/1967 01/01/1936 11/20/1955 Email smith@abc.it NULL NULL NULL not existing existing but unknown not known if existing Fig. 2.2. The Person relation, with diﬀerent null value meanings for the e-mail attribute	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
1) A database contains information on repairs done to capital equipment. How- ever, it is a known fact that sometimes the repairs are done and the informa- tion about the repair is just not entered into the database. This is the result of lack of concern on the part of the repair people and a lack of enforcement on the part of their supervisors. It is estimated that the amount of missing information is about 5%. This database is probably a good-quality database for assessing the genral health of capital equipment. Equipment that required a great deal of expense to maintain can be identiﬁed from the data. Unless the missing data is disproportionately skewed, the records are usable for all ordinary decisions. However, trying to use it as a base for evaluating information makes it a low-quality database. The missing transactions could easily tag an important piece of equipment as satisfying a warranty when in fact it does not. 2) A blank for COLLEGE_LAST_ATTENDED may be accurate or inaccurate. If the person it applied to had attended college, it would be inaccurate. This is another case of valid but not accurate.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

Let us consider a Person relation with the attributes Name, Surname,

BirthDate,and Email. The relation is shown in Figure 2.2. For the tuples with Id equalto2,3,and 4, the Email value is NULL. Let us suppose that the person represented by tuple 2 has no e-mail: no incompleteness case occurs. If the person represented by tuple 3 has an e-mail, but its value is not known then tuple 3 presents an incompleteness. Finally, if it is not known whether the person represented by tuple 4 has an e-mail or not, incompleteness may not be the case.

ID 1

2 3 4

Name John

Edward Anthony Marianne

Surname Smith

Monroe White Collins

BirthDate 03/17/1974 02/03/1967 01/01/1936 11/20/1955

smith@abc.it NULL NULL NULL

not existing existing but unknown not known if existing

Fig. 2.2. The Person relation, with diﬀerent null value meanings for the e-mail attribute

C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.

1) A database contains information on repairs done to capital equipment. How- ever, it is a known fact that sometimes the repairs are done and the informa- tion about the repair is just not entered into the database. This is the result of lack of concern on the part of the repair people and a lack of enforcement on the part of their supervisors. It is estimated that the amount of missing information is about 5%. This database is probably a good-quality database for assessing the genral health of capital equipment. Equipment that required a great deal of expense to maintain can be identiﬁed from the data. Unless the missing data is disproportionately skewed, the records are usable for all ordinary decisions. However, trying to use it as a base for evaluating information makes it a low-quality database. The missing transactions could easily tag an important piece of equipment as satisfying a warranty when in fact it does not.

2) A blank for COLLEGE_LAST_ATTENDED may be accurate or inaccurate. If the person it applied to had attended college, it would be inaccurate. This is another case of valid but not accurate.

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:
1) A null value is a missing value. However, a value that is missing may provide more information than one might think because there may be different reason that it is missing. A null value might actually represent an unavailable value, an attribute that is not applicable for this entity, or no value in the attribute’s domain that correctly classifies this entity. Of course, the value may actually be missing. 2) When the null value (or absence of a value) is required for an attribute, there should be a recognizable form for presenting that null value that does not conflict with any valid values.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
1) Ability to distinguish neatly (without ambiguities) null and default values from applicable values of the domain. 2) Completeness refers to the degree to which values are present in a data collection, as for as an individual datum is concerned, only two situations are possible: Either a value is assigned to the attribute in question or not. In the latter case, null, a special element of an attribute’s domain can be assigned as the attribute’s value. Depending on whether the attribute is mandatory, optional, or inapplicable, null can mean different things.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.

Definition:

Source:

1) A null value is a missing value. However, a value that is missing may provide more information than one might think because there may be different reason that it is missing. A null value might actually represent an unavailable value, an attribute that is not applicable for this entity, or no value in the attribute’s domain that correctly classifies this entity. Of course, the value may actually be missing.

2) When the null value (or absence of a value) is required for an attribute, there should be a recognizable form for presenting that null value that does not conflict with any valid values.

LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.

1) Ability to distinguish neatly (without ambiguities) null and default values from applicable values of the domain.

2) Completeness refers to the degree to which values are present in a data collection, as for as an individual datum is concerned, only two situations are possible: Either a value is assigned to the attribute in question or not. In the latter case, null, a special element of an attribute’s domain can be assigned as the attribute’s value. Depending on whether the attribute is mandatory, optional, or inapplicable, null can mean different things.

REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.

Appropriate presentation

Characteristic Name:	Appropriate presentation
Dimension:	Usability and Interpretability
Description:	The data presentation is aligned with its use
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to the lack of appropriate presentation of data

The number of complaints received due to the lack of appropriate presentation of data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Ensure that Universally accepted standard formats are used to maintain the compatibility of information across organisations and across time	(1) A patients diagnostic card generated in one hospital is compatible with another hospital.
Ensure that information can be aggregated or combined through the use of compatible formats	(1) Product wise monthly sales report can be generated by combining the sales reports of three subsidiaries
Ensure that the data presentations are familiar to the users even if the application platform is changed.	(1) A quotation created in one system is sent to another system through an EDI message and displayed in the same presentation format
Ensure the media of presentation is appropriate for the target group	(1) A step by step written instruction list in a documents appropriate for a software engineer. (2) A video display is appropriate for a mechanic
Ensure that the presentation formats are flexible to accommodate changes easily	(1) An invoice document may require additional space to mansion authorisation evidence

Validation Metric:

How mature is the process to maintain appropriate presentation of data

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

Example:	Source:
my birth date is December 13, 1941. If a personnel database has a BIRTH_DATE data element that expects dates in USA format, a date of 12/13/1941 would be correct. A date of 12/14/1941 would be inaccurate because it is the wrong value. A date of 13/12/1941 would be wrong because it is a European representation instead of a USA representation.	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 how information is presented to and collected from those who utilize it. Format and appearance support appropriate use of information.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
1) The Characteristic in which formatted data is presented consistently in a standardized or consistent way across different media, such as in computer screens, reports, or manually prepared reports. 2) The Characteristic of Information being presented in the right technology Media, such as online, hardcopy report, audio, or video. 3) The degree to which Information is presented in a way Intuitive and appropriate for the task at hand. The Presentation Quality of Information will vary by the individual purposes for which it is required. Some users require concise presentation, whereas others require a complete, detailed presentation, and yet others require graphic, color, or other highlighting techniques.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
1) Appropriateness is the dimension we use to categorize how well the format and presentation of the data match the user needs. In our example, there is a difference between a high-level monthly sales report that is supplied to senior management and the daily product manifests that are handed to the shipping department for product packaging. 2) Flexibility in presentation describes the ability of the system to adapt to changes in both the represented information and in user requirements for presentation of information. For example, a system that display different counties; currencies may need to have the screen presentation change to allow for more significant digits for prices to be displayed when there is a steep devaluation in one county’s currency. 3) In an environment that makes use of different kinds of systems and applications, a portable interface is important so that as applications are migrated from one platform to another, the presentation of data is familiar to the users. Also, when dealing with a system designed for international use, the user of international standards as well as universally recognized icons is a sign of system designed with presentation portability in mind.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
1) Data is presented in an intelligible manner. 2) Data is presented in a manner appropriate for its use, with respect to format, precision, and units.	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.
Good format, like good views, are flexible so that changes in user need and recording medium can be accommodated.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.
Data are always presented in the same format and are compatible with the previous data.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.