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.

Standards and regulatory compliance

Characteristic Name:	Standards and regulatory compliance
Dimension:	Validity
Description:	All data processing activities should comply with the policies, procedures, standards, industry benchmark practices and all regulatory requirements that the organization is bound by
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due non adherence of standards and regulations

The number of complaints received due to non adherence to standards and regulations

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Identify the policies, procedures, standards, benchmark practices and any regulatory requirements that an Information object is bound by	(1) Each person's compensation criteria must be determined in accordance with the Annuities Based on Retired or Retainer Pay law.
Ensure that all data processing activities are well defined and documented based on the policies, procedures, standards, benchmarks and regulatory requirements.	(1) Process of making a damage estimate is well defined based on industry benchmarks
Ensure that the application programs cater for standards and regulatory compliance	(1) A software program to make damage estimates which includes all benchmark data
Regularly monitor the data processing activities and identify the problems and inefficiencies so that the corrective and preventive actions can be taken.	(1) Frequent delays in time sheet approvals results in delayed payments
Signs should be standardised and universally used	(1) In the line efficiency report, low efficiency lines are indicated using a RED light while a green light indicates high efficiency
Relevant standard, procedures, policies and regulations should be communicated to the users effectively	(1) Providing a guidelines for signs
Ensure that proper conversion tables are maintained and used in converting attribute vales to different measurement bases.	(1) Metric conversion tables are used to convert lbs to kgs.

Validation Metric:

How mature is the process maintain the adherence to standards and regulations

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

Example:	Source:
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 Definitions are examples of the characteristic that appear in the sources provided.

Definition:	Source:
A measure of the existence, completeness, quality, and documentation of data standards, data models, business rules, metadata, and reference data.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The data element has a commonly agreed upon enterprise business definition and calculations.	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.
SIGNS AND OTHER Information-Bearing Mechanisms like Traffic Signals should be standardized and universally used across the broadest audience possible.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Validity of data refers to data that has been collected in accordance with any rules or definitions that are applicable for that data. This will enable benchmarking between organisations and over time.	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.