Source quality
Characteristic Name: | Source quality |
Dimension: | Reliability and Credibility |
Description: | Data used is from trusted and credible sources |
Granularity: | Information object |
Implementation Type: | Process-based approach |
Characteristic Type: | Usage |
Verification Metric:
The number of tasks failed or under performed due to lack of source quality |
The number of complaints received due to lack of source quality |
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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Asses the reputation of data sources | (1) Central Bank is the best source to get daily exchange rates |
Evaluate the remedies for non-compliance of data | (1) Any remedies given by the source organisation to mitigate the losses in case if the information is of low quality |
Rely on shared information sources created\recommended\used by the organisations operating in the industry | (1) In performing portfolios analysis most organisations use the risk factors produced by a central body of the economy (Central bank) |
Validation Metric:
How mature is the process to maintain quality of data sources |
These are examples of how the characteristic might occur in a database.
Example: | Source: |
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Consider an inventory database that contains part numbers, warehouse locations, quantity on hand, and other information. However, it does not contain source information (where the parts came from). If a part is supplied by multiple suppliers, once the parts are received and put on the shelf there is no indication of which supplier the parts came from. The information in the database is always accurate and current. For normal inventory transactions and deci- sion making, the database is certainly of high quality. If a supplier reports that one of their shipments contained defective parts, this database is of no help in identifying whether they have any of those parts or not. The database is of poor quality because it does not contain a relevant element of information. Without that information, the database is poor data quality for the intended use. | 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 source of information (1) guarantees the quality of information it provides with remedies for non-compliance; (2) documents its certification in its Information Quality Management capabilities to capture, maintain, and deliver Quality Information; (3) provides objective and verifiable measures of the Quality of Information it provides in agreed-upon Quality Characteristics; and (4) guarantees that the Information has been protected from unauthorized access or modification. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
The notion of abstracting information into a data domain implies that there are enough users of the same set of data that it makes sense to manage their own versions. The dimension of enterprise agreement of usage measures the degree to which different organizations conform to the usage of the enterprise data domain of record instead of relying on their own data set. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
Reputation is the extent to which data are trusted or highly regarded in terms of their source or content. | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
The degree of reputation of an information object in a given community or culture. | 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. |
Data are trusted or highly regarded in terms of their source and content. | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |
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. |