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.

Appropriate presentation

Characteristic Name:	Appropriate presentation
Dimension:	Usability and Interpretability
Description:	The data presentation is aligned with its use
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 appropriate presentation of data

The number of complaints received due to the lack of appropriate presentation 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 Universally accepted standard formats are used to maintain the compatibility of information across organisations and across time	(1) A patients diagnostic card generated in one hospital is compatible with another hospital.
Ensure that information can be aggregated or combined through the use of compatible formats	(1) Product wise monthly sales report can be generated by combining the sales reports of three subsidiaries
Ensure that the data presentations are familiar to the users even if the application platform is changed.	(1) A quotation created in one system is sent to another system through an EDI message and displayed in the same presentation format
Ensure the media of presentation is appropriate for the target group	(1) A step by step written instruction list in a documents appropriate for a software engineer. (2) A video display is appropriate for a mechanic
Ensure that the presentation formats are flexible to accommodate changes easily	(1) An invoice document may require additional space to mansion authorisation evidence

Validation Metric:

How mature is the process to maintain appropriate presentation of data

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

Example:	Source:
my birth date is December 13, 1941. If a personnel database has a BIRTH_DATE data element that expects dates in USA format, a date of 12/13/1941 would be correct. A date of 12/14/1941 would be inaccurate because it is the wrong value. A date of 13/12/1941 would be wrong because it is a European representation instead of a USA representation.	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:
A measure of how information is presented to and collected from those who utilize it. Format and appearance support appropriate use of information.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
1) The Characteristic in which formatted data is presented consistently in a standardized or consistent way across different media, such as in computer screens, reports, or manually prepared reports. 2) The Characteristic of Information being presented in the right technology Media, such as online, hardcopy report, audio, or video. 3) The degree to which Information is presented in a way Intuitive and appropriate for the task at hand. The Presentation Quality of Information will vary by the individual purposes for which it is required. Some users require concise presentation, whereas others require a complete, detailed presentation, and yet others require graphic, color, or other highlighting techniques.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
1) Appropriateness is the dimension we use to categorize how well the format and presentation of the data match the user needs. In our example, there is a difference between a high-level monthly sales report that is supplied to senior management and the daily product manifests that are handed to the shipping department for product packaging. 2) Flexibility in presentation describes the ability of the system to adapt to changes in both the represented information and in user requirements for presentation of information. For example, a system that display different counties; currencies may need to have the screen presentation change to allow for more significant digits for prices to be displayed when there is a steep devaluation in one county’s currency. 3) In an environment that makes use of different kinds of systems and applications, a portable interface is important so that as applications are migrated from one platform to another, the presentation of data is familiar to the users. Also, when dealing with a system designed for international use, the user of international standards as well as universally recognized icons is a sign of system designed with presentation portability in mind.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
1) Data is presented in an intelligible manner. 2) Data is presented in a manner appropriate for its use, with respect to format, precision, and units.	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.
Good format, like good views, are flexible so that changes in user need and recording medium can be accommodated.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.
Data are always presented in the same format and are compatible with the previous data.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.