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.

Format consistency

Characteristic Name:	Format consistency
Dimension:	Consistency
Description:	Data formats are consistently used
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of inconsistent data formats 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:
Maintain consistent formats for data values across different data bases and different tables in the same database.	(1) Telephone number : Country code/Area code/number (2) Address : House number, Street, Suburb, Sate, Country
Maintain structural similarity or compatibility of entities and attributes across systems (databases/data sets) and across time.	(1) Customer record has the same structure in all systems which it is being used.
Maintain consistent and compatible encoding /decoding standards across different applications.	(1) ASCII, UTF-8, XML

Validation Metric:

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

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

Example:	Source:
1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters). 2) 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. 3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters).

2) 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.

3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed.

N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue.

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:
A measure of the equivalence of information stored or used in various data stores, applications, and systems, and the processes for making data equivalent	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The extent to which similar attributes or elements of an information object are consistently represented using the same structure, format, and precision.	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.