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: |
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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: |
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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: |
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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. |
Referential integrity
Characteristic Name: | Referential integrity |
Dimension: | Consistency |
Description: | Data relationships are represented through referential integrity rules |
Granularity: | Record |
Implementation Type: | Rule-based approach |
Characteristic Type: | Declarative |
Verification Metric:
The number of referential integrity violations 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: |
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Implement and maintain foreign keys across tables (Data sets) | (1) Implementation of foreign keys |
Implement proper validation rules/Automated suggestions of values based on popular value combinations, to prevent incorrect references of foreign keys | (1) The attribute Customer_Zip_Code of the Customer relation contains the value 4415, instead of 4445; both zip codes exist in the Zip_Code relation |
Implement validation rules for foreign keys of relevant tables in case of data migrations | (1) Error logs are generated for foreign key violations. |
Implement proper synchronising mechanisms to handle data updates when there are concurrent operations or distributed databases. | (1) Locking mechanisms to data objects while being updated |
Ensure the consistency of the data model when changes are done to process model (software) | (1) Data dictionary provides the FDs and CFDs |
Validation Metric:
How mature is the creation and implementation of the DQ rules to maintain referential integrity |
These are examples of how the characteristic might occur in a database.
Example: | Source: |
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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. |
A company has a color field that only records red, blue, and yellow. A new requirement makes them decide to break each of these colors down to multiple shadings and thus institute a scheme of recording up to 30 different colors, all of which are variations of red, blue, and yellow. None of the old records are updated to the new scheme, as only new records use it. This data- base will have inconsistency of representation of color that crosses a point in time. | 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: |
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The Information Float or Lag Time is acceptable between (a) when data is knowable (create or changed) in one data store to (b) when it is also knowable in a redundant or distributed data store, and concurrent queries to each data store produce the same result. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
Assigning unique identifiers to objects (customers, products, etc.) within your environment simplifies the management of your data, but introduces new expectations that any time an object identifier is used as foreign keys within a data set to refer to the core representation, that core representation actually exists. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
i.e. integrity rules. Data follows specified database integrity rules. | 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. |