Meta-data compliance

Characteristic Name:	Meta-data compliance
Dimension:	Validity
Description:	Data should comply with its metadata
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of meta-data violations reported in an 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:
Domains should be specified by considering all possible value patterns, cases and usage needs which is applicable for a data attribute.	(1) Eg: It is easy to maintain the order number as a numeric value since it can be easily incremented (10000, 10001, and 10002). But it can be also defined as alphanumeric in order to distinguish special cases (10000R is a return order of sales order 10000)
Maintain valid values/value ranges/Value lists for attributes.	(1) Gender can be M or F (2) Country is taken from the existing list of countries (3) Birth date cannot be a future value. (4) Salary range for level 4 employees must be between $40000-60000
Usage of number ranges for critical data elements	(1) Sales orders 10000001 to 1999999 (2) Purchase orders 50000001 to 59999999
Maintain the possible synonyms and abbreviations which could be accepted as valid values	(1) Post Box , PO BOX, BOX etc.
Explicitly mention what values, characters are not permitted in the attribute	(1) User Name can contain only A-Z (2) No blank spaces are allowed for credit card number
Explicitly mention the minimum /maximum number of characters, or any other requirements such as case sensitivity, that an attribute value should meet	(1) Password should contain minimum of 8 characters including one numeric and one capital
Maintain values based on specific formats as defined by the stakeholders, standards, best practices or agreements.	(1) Time should be in 24 hour clock (2) Date should be in DD/MM/YYYY
Appropriate measurement scale should be maintained against quantities and volumes	(1) Currency for Price values Kg/g/mg for weights litres for volumes (2) Data Dictionary , Data catalog
Documentation for Meta-Data is available online for the users	(1) Data Dictionary , Data catalog

Validation Metric:

How mature is the creation and implementation of the DQ rules to define meta-data

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

Example:	Source:
1) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers. 2) The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
the answer to the query “What is Mr. Wat- son% telephone number?” can bl: validated against the format for telephone numbers. Additionally, Wat- son’s address might be used to vallidate the area code and exchange	M. Brodie, “Data Quality in Information Systems”, North-Holland Publishing Company Information and Management 3, 1980, pp. 245-258.

Example:

Source:

1) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers.

2) The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid.

N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

the answer to the query “What is Mr. Wat- son% telephone number?” can bl: validated against the format for telephone numbers. Additionally, Wat- son’s address might be used to vallidate the area code and exchange

M. Brodie, “Data Quality in Information Systems”, North-Holland Publishing Company Information and Management 3, 1980, pp. 245-258.

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

Definition:	Source:
Determines the extent to which data conforms to a specified format. For example, the order date must be in the format YYYY/MM/DD.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
1) Data element passes all edits for acceptability and is free from variation and contradiction based on the condition of another data element (a valid value combination). 2) The metadata of the data element clearly states or defines the purpose of the data element, or the values used in the data element can be understood by metadata or data inspection. The metadata of the entity clearly states or defines the purpose of the entity and its required attributes/domains.	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) A data value is a Valid Value or within a specified range of valid values for this data element. 2) Data values are consistent with the Attribute (Fact) definition.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
This dimension refers to whether instances of data are represented in a format that is consistent with the domain of values and with other similar attribute values. For example, the display of time in a non-military (12-hour) format may be confusing if all other instances of times in the system are displayed in the 24-hour military format.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
This dimension refers to whether instances of data are either store, exchanged, or presented in a format that is consistent with the domain of values, as well as consistent with other similar attribute values. Each column has numerous metadata attributes associated with it: its data type, precision, format patterns, use of a predefined enumeration of values, domain ranges, underlying storage formats, etc.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Appropriate metadata is available to define, constrain, and document data.	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.
Representation consistency refers to whether physical instances of data are in record with their formats. For example, an EMPLOYEE’s salary cannot be represented “$AXT,” as there is (or should be) no such element in S. One would often like to know whether a physical instance is the proper representation for the intended (correct) value. But in practice this is rarely possible, as the intended value is conceptual and not known. So one is left with the issue of whether the representation conflicts with S.	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.