Meta-data compliance

Meta-data compliance

Compare with other Characteristic

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

Verification Metric:

The number of meta-data violations reported in an attribute per thousand records

Validation Metric:

To what extent required rules have been identified and implemented to maintain the declarative characteristic in concern.

BackgroundGuidelines

The original definitions given below formed the basis of the consolidated definition of the characteristic.

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. More from this source
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. More from this source
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. More from this source
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. More from this source
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. More from this source
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. More from this source
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. More from this source

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

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