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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.

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.

 

Value consistency

Characteristic Name: Value consistency
Dimension: Consistency
Description: Data values are consistent and do not provide conflicting or heterogeneous instances
Granularity: Element
Implementation Type: Rule-based approach
Characteristic Type: Declarative

Verification Metric:

The number of inconsistent data values 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:
For critical data elements provide standard classifications (values lists) for data entry interfaces and restrict arbitrary values across the system (1) Country, city are taken from a standard list.
(2) Generally accepted industry classifications are used to analyse customers industry wise (Education, Banking & Finance, Medical, Manufacturing…….
When data elements are combined for specific identification/management/accounting purposes, standardise such combinations and use them across the system. (1) Customer and sales order are combined for identification purposes
(2) Costs of wastage are associated with individual orders they are incurred and managed.
Define data attributes in such a way that data values are atomic and hence consistency can be maintained for any form of aggregation or consolidation Name is divided into first name Middle name and Last Name
Maintain consistency in using unit of measures across different tables and different data bases Sales price is in $ in Sales table and Accounts receivable ledger

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain value consistency

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

Example: Source:
School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database. N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
For example, data are inconsistent when it is documented that a male patient has had a hysterectomy. B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).
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.
the data values ST Louis and Saint Louis may both refer to the same city. However, the recordings are inconsistent, and thus at least one of them is inaccurate. 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: The data values persist from a particular data element of the data source to another data element in a second data source. Consistency can also reflect the regular use of standardized values, articularly in descriptive elements. Entity Level: The entity’s domains and domain values either persist intact or can be logically linked from one data source to another data source. Consistency can also reflect the regular use of standardized values particularly in descriptive 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.
Determines the extent to which distinct data instances provide nonconflicting information about the same underlying data object. For example, the salary range for level 4 employees must be between $40,000 and $65,000. G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.
Data is consistent if it doesn’t convey heterogeneity, neither in contents nor in form – anti examples: Order.Payment. Type = ‘Check’; Order. Payment. CreditCard_Nr = 4252… (inconsistency in contents); Order.requested_by: ‘European Central Bank’;Order.delivered_to: ‘ECB’ (inconsistency in form,because in the first case the customer is identified by the full name, while in the second case the customer’s acronym is used). KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Consistency can be curiously simple or dangerously complex. In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. Two data values drawn from separate data sets may be consistent with each other, yet both can be incorrect. LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. A strict definition of consistency specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness. LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Consistency, in popular usage, means that two or more things do not conflict with one another. This usage extends reasonably well to data values, although a bit of added discipline is desired. REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.