Understandability

Characteristic Name:	Understandability
Dimension:	Usability and Interpretability
Description:	The data is understandable
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 understandability of data

The number of complaints received due to the lack of understandability 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 appropriate signs/language is used to strenthen the readers understanding about the information object	(1) Poor, good, excellent is more suitable than 1, 2,3 as ratings to compare two factors
Avoid any possibility of ambiguity in understanding data with the inclusion of footnotes, legend etc.	(1) Footnote : Total price includes GST.
Provide supplements to understand the content of non-text and non-numeral information (e.g.. Images)	(1) A location in a plan can be identified by the coordinates
Ensure that data are concisely represented without being overwhelmed	(1) Focussed on one topic
Convenient and user friendly (more natural) formats are used for structured attributes like dates, time, telephone number, tax ID number, product code, and currency amounts	(1) U.S. phone number formats [+1(555)999-1234]
Appropriate fonts and styles are used to improve the clarity of the content	(1) Headings are marked in bold letters, Totals figures are are marked with bold numbers

Validation Metric:

How mature is the process to maintain the understandability of data

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

Example:	Source:
a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.

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:
The data element is used only for its intended purpose, that is, the degree to which the data characteristics are well understood and correctly utilized.	B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation.
1) Periodic Reports, such as Financial Statements, Annual Reports, and Policy and Procedure Manuals should have a standard format with a style sheet that presents the information in a consistent and easily read and understood format. 2) The Characteristic in which Information is presented in a way that clearly communicates the truth of the data. Information is presented with clear labels, footnotes, and/or other explanatory notes, with references or links to definitions or documentation the clearly communicates the meaning and any anomalies in the Information.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Usability of data refers to the extent to which data can be accessed and understood.	HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality.
A good presentation provides the user with everything required for the correct interpretation of information. When there is any possibility of ambiguity, a key or legend should be included.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Is the information understandable or comprehensible to the target group?	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
1) The extent to which the content of an object is focused on one topic. 2) The extent of cognitive complexity of an information object measured by some index or indices. 3) The extent to which the model or schema and content of an information object are expressed by conventional, typified terms and forms according to some general-purpose reference source.	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.
1) Data are compactly represented without being overwhelmed. 2) Data are clear without ambiguity and easily comprehended.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

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.