Completeness of mandatory attributes

Characteristic Name:	Completeness of mandatory attributes
Dimension:	Completeness
Description:	The attributes which are mandatory for a complete representation of a real world entity must contain values and cannot be null .
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of null values reported in a mandatory 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:
Specify which attributes are required to maintain a meaningful representation of an entity.	1) A sales order should at least have values for order number, Quantity, Price and Total (Sales order is the record)
Specify the states of an entity where the above identified attributes become mandatory values	(1)Order number quantity and total should be available as mandatory by the time order is created whereas price will become mandatory when the order is approved. (States :"Order created" "Order approved") (2) Product is retired and now has a product-last-available-date
Specify the dependencies of entities in operational context to identify the mandatory values	(1)Invoice number should exist to create a gate pass
Specify default values where possible	(1) Default country is Australia for those who fill the application from Australian IP addresses

Validation Metric:

How mature is the creation and implementation of the DQ rules to handle mandatory values

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

Example:	Source:
1) 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 2) if Dept is a relation representing the employees of a given department, and one speciﬁc employee of the department is not represented as a tuple of Dept, then the tuple corresponding to the missing employee is in ref(Dept),and ref(Dept) diﬀers from Dept in exactly that tuple.	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete.	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 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 information 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 general 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 BIRTH_DATE value left blank would not be accurate because all of us have birth dates.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

1) 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

2) if Dept is a relation representing the employees of a given department, and one speciﬁc employee of the department is not represented as a tuple of Dept, then the tuple corresponding to the missing employee is in ref(Dept),and ref(Dept) diﬀers from Dept in exactly that tuple.

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

if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete.

Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 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 information 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 general 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 BIRTH_DATE value left blank would not be accurate because all of us have birth dates.

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:
Domain Level: Data element is 1. Always required be populating and not defaulting; or 2. Required based on the condition of another data element. Entity Level: The required domains that comprise an entity exist and are not defaulted in aggregate.	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.
A given data element (fact) has a full value stored for all records that should have a value.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Determined the extent to which data is not missing. For example, an order is not complete without a price and quantity.	G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.
Completeness refers to the expectation that certain attributes are expected to have assigned values in a data set. Completeness rules can be assigned to a data set in three levels of constraints: 1. Mandatory attributes that require a value 3. Inapplicable attributes (such as maiden name for a single male), which may not have a value.2. Optional attributes, which may have a value.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
An expectation of completeness indicates that certain attributes should be assigned values in a data set. Completeness rules can be assigned to a data set in three levels of constraints:1. Mandatory attributes that require a value, 2. Optional attributes, which may have a value based on some set of conditions, and 3. Inapplicable attributes, (such as maiden name for a single male), which may not have a value.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.

Referential integrity

Characteristic Name:	Referential integrity
Dimension:	Consistency
Description:	Data relationships are represented through referential integrity rules
Granularity:	Record
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of referential integrity violations 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:
Implement and maintain foreign keys across tables (Data sets)	(1) Implementation of foreign keys
Implement proper validation rules/Automated suggestions of values based on popular value combinations, to prevent incorrect references of foreign keys	(1) The attribute Customer_Zip_Code of the Customer relation contains the value 4415, instead of 4445; both zip codes exist in the Zip_Code relation
Implement validation rules for foreign keys of relevant tables in case of data migrations	(1) Error logs are generated for foreign key violations.
Implement proper synchronising mechanisms to handle data updates when there are concurrent operations or distributed databases.	(1) Locking mechanisms to data objects while being updated
Ensure the consistency of the data model when changes are done to process model (software)	(1) Data dictionary provides the FDs and CFDs

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain referential integrity

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

Example:	Source:
the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
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:

the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table

Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.

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:
The Information Float or Lag Time is acceptable between (a) when data is knowable (create or changed) in one data store to (b) when it is also knowable in a redundant or distributed data store, and concurrent queries to each data store produce the same result.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Assigning unique identifiers to objects (customers, products, etc.) within your environment simplifies the management of your data, but introduces new expectations that any time an object identifier is used as foreign keys within a data set to refer to the core representation, that core representation actually exists.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
i.e. integrity rules. Data follows specified database integrity rules.	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.