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Traceability

Characteristic Name: Traceability
Dimension: Reliability and Credibility
Description: The lineage of the data is verifiable
Granularity: Record
Implementation Type: Process-based approach
Characteristic Type: Usage

Verification Metric:

The number of tasks failed or under performed due to lack of traceability in data
The number of complaints received due to lack of traceability in data

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
Maintain provenance records for the events such as creation, update,transcription, abstraction, validation and transforming ownership, if the data are dynamic. (1) Inventory system shows the current stocks and keep records for all the transactions that the stocks are subjected to
In case of multiple sources are available for same data/information, implement a traceability mechanism to view all versions from multiple sources (1) Content management systems
Maintain proper protocols/standards/policy to archive data (1) Every invoice is archived after 120 days of payments.
Maintain versions of data records where necessary (1) Customer versions

Validation Metric:

How mature is the process to maintain traceability in data

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

Example: Source:
minutes of a meeting will be produced in draft form and reviewed by the members of the committee before being approved. Once this process of creation is finished the record must be fixed and must not be susceptible to change. If a record is changed or manipulated in some way, it no longer provides evidence of the transaction it originally documented. For example, if someone alters the minutes of a meeting after they have been approved, the minutes can no longer be considered an accurate record of the meeting. This is another issue that becomes more important in an electronic context. K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

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

Definition: Source:
Is the background of the information visible (author, date etc.)? EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
A data provanance record can include information about creation, update, transcription, abstraction, validation and transforming ownership of data. ISO 2012. ISO 8000-2 Data Quality-Part 2-Vocabulary. ISO.
The extent to which the correctness of information is verifiable or provable in the context of a particular activity. STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733.

 

Redundancy

Characteristic Name: Redundancy
Dimension: Consistency
Description: The data is recorded in exactly one place
Granularity: Record
Implementation Type: Rule-based approach
Characteristic Type: Declarative

Verification Metric:

The volume of redundant data as a percentage to total data

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
Maintain the database schema eliminating the causes for redundancies of entities and attributes (1) All customers are in customer table
Ensure that there are no redundant records across distributed databases (1) Organisation has different customer bases maintained in different databases. But one customer is available only in one database
Ensure that same entity is not originally captured more than once in the systems (1) Medical Insurance system refers employee bank details from the payroll.
Ensure that there are no temporary table backups are available in the database (1) Created a backup for employees as employee_temp for a specific purpose and it is still in the database

Validation Metric:

How mature is the creation and implementation of the DQ rules to eliminate the occurrence of redundant data

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

Example: Source:
A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2% 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 unwanted duplication existing within or across systems for a particular field, record, or data set. D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
There is only one record in a given data store that represents a Single Real-World Object or Event. ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Determines the extent to which the columns are not repeated. G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.