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.

Data volume

Characteristic Name:	Data volume
Dimension:	Completeness
Description:	The volume of data is neither deficient nor overwhelming to perform an intended task
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to not meeting the right volume of data

The number of complaints received due to volume related issues

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Define the scope of data in terms of organisational coverage to perform a business activity	(1) At least 70% of the production units should submit data to calculate total production efficiency of the company
Define the scope of data in terms of activities relates to any business task	(1) Pages with more than thousand hits per day and above are considered for the analysis
Define the scope of data in terms of the population of data which is under concern	(1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth
Define an appropriate amount of records in terms of lower limit and upper limit for any task	(1) At least six responses should be available to evaluate a tutor's skills and competency.

Validation Metric:

How mature is the process of defining and maintaining appropriate data volumes of data

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

Example:	Source:
At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

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

Definition:	Source:
A measure of the availability and comprehensiveness of data compared to the total data universe or population of interest.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Is the scope of information adequate? (not too much nor too little).	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Degree of presence of data in a given collection.	SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.
The quantity or volume of available data is appropriate	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.