Usefulness and relevance

Characteristic Name:	Usefulness and relevance
Dimension:	Usability and Interpretability
Description:	The data is useful and relevant for the task at hand
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to the lack of usefulness and relevance of data

The number of complaints received due to the lack of usefulness and relevance of data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Define the content of the information object based on the user requirements (as required by the task at hand) and also considering all other compliance requirements so that the information is relevant and legitimate	(1) Customer invoice should contain information for the customer to understand his liability and for the delivery person to understand the point of delivery and the tax department to verify the applicable tax amount.
Regularly monitor the changes to the internal operational environment ( business process changes etc) and find out what are the new information requirements emerge due to the changes, and provide for them by amending the information structures	(1) Time stamp became an important attribute for GRNs (goods receipts notes) when Lean manufacturing started as all raw materials are expected to receive by six hours before production (GRN-record, and the time stamp -attribute)
Regularly monitor the changes in the external environment find out the new information requirements emerge due to such changes and provide for such data needs	(1) Competitors' rates have become important to price the existing products during the recession period since the traditional costing method does not give a competitive price.
Regularly check with knowledge workers to find out how their operations/decisions can be performed better with new data available to them and provide for such data in the information system	(1) An hourly working progress report is useful in identifying the bottlenecks in production lines and balance the lines
Monitor and measure the user satisfaction about the information provided	(1) User satisfaction survey

Validation Metric:

How mature is the process to maintain usefulness and relevance of data

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

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

Definition:	Source:
1) The Characteristic in which the Information is the right kind of Information that adds value to the task at hand, such as to perform a process or make a decision. 2) Knowledge Workers have all the Facts they need to perform their processes or make their decisions.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
1) Can the information process be adapted by the information consumer? 2)Can the information be directly applied? Is it useful? 3) Does the information provision correspond to the user’s needs and habits?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Relevance of data refers to the extent to which the data meets the needs of users. Information needs may change and is important that reviews take place to ensure data collected is still relevant for decision makers.	HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality.
Relevance is the degree to which statistics meet current and potential users’ needs. It refers to whether all statistics that are needed are produced and the extent to which concepts used (definitions, classifications etc.)	LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf.
The data includes all of the types of information important for its use.	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.
1) Intrinsic: The extent to which the information is new or informative in the context of a particular activity or community. 2) Relational Contextual:The amount of information contained in an information object. At the content level, it is measured as a ratio of the size of the informative content (measured in word terms that are stemmed and stopped) to the overall size of an information object. At the schema number of elements in the object level it is measured as a ratio of the number of unique elements over the total. 3) The extent to which information is applicable in a given activity. 4) The extent to which the model or schema and content of an information object are expressed by conventional, typified terms and forms according to some general-purpose reference source.	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.
1) Data are applicable and useful for the task at hand. 2) The quantity or volume of available data is appropriate. 3) Data are of sufficient depth, breath and scope for the task at hand.	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:
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:
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.

Example:

Source:

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:
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.