Understandability

Characteristic Name:	Understandability
Dimension:	Usability and Interpretability
Description:	The data is understandable
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 understandability of data

The number of complaints received due to the lack of understandability of data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Ensure that appropriate signs/language is used to strenthen the readers understanding about the information object	(1) Poor, good, excellent is more suitable than 1, 2,3 as ratings to compare two factors
Avoid any possibility of ambiguity in understanding data with the inclusion of footnotes, legend etc.	(1) Footnote : Total price includes GST.
Provide supplements to understand the content of non-text and non-numeral information (e.g.. Images)	(1) A location in a plan can be identified by the coordinates
Ensure that data are concisely represented without being overwhelmed	(1) Focussed on one topic
Convenient and user friendly (more natural) formats are used for structured attributes like dates, time, telephone number, tax ID number, product code, and currency amounts	(1) U.S. phone number formats [+1(555)999-1234]
Appropriate fonts and styles are used to improve the clarity of the content	(1) Headings are marked in bold letters, Totals figures are are marked with bold numbers

Validation Metric:

How mature is the process to maintain the understandability of data

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

Example:	Source:
a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

a Social Security number must consist of nine numeric digits. If this is your only deﬁnition, you will ﬁnd that all values that are blank, contain characters other than numeric or contain less than or more than nine digits. However, you can go further in your deﬁnition. The government employs a scheme of assigning numbers that allows you to examine the value in more detail to determine if it is valid or not. Using the larger rule has the potential for ﬁnding more inaccurate values.

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 data element is used only for its intended purpose, that is, the degree to which the data characteristics are well understood and correctly utilized.	B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation.
1) Periodic Reports, such as Financial Statements, Annual Reports, and Policy and Procedure Manuals should have a standard format with a style sheet that presents the information in a consistent and easily read and understood format. 2) The Characteristic in which Information is presented in a way that clearly communicates the truth of the data. Information is presented with clear labels, footnotes, and/or other explanatory notes, with references or links to definitions or documentation the clearly communicates the meaning and any anomalies in the Information.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Usability of data refers to the extent to which data can be accessed and understood.	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.
A good presentation provides the user with everything required for the correct interpretation of information. When there is any possibility of ambiguity, a key or legend should be included.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Is the information understandable or comprehensible to the target group?	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
1) The extent to which the content of an object is focused on one topic. 2) The extent of cognitive complexity of an information object measured by some index or indices. 3) 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 compactly represented without being overwhelmed. 2) Data are clear without ambiguity and easily comprehended.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

Completeness of records

Characteristic Name:	Completeness of records
Dimension:	Completeness
Description:	Every real world entity instance, that is relevant for the organization can be found in the data
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to missing records

The number of complaints received due to missing records

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Implement a process level validation mechanism to avoid occurrence of missing records	(1) A buyer must record/verify an expense or asset in accordance with accepting/receiving any purchased items. (2)New application are stored in a temporary cabinet after entering into the system and they will be transferred to the file cabinet at the end of every week after the property manager cross check them with the system
Execute database commits upon transaction sequences in application programs and make sure all the transactions in the sequence successfully commit and generate the required records at the end of the sequence.	(1) In generating the MRP, the database operations will not be committed unless all materials in BOM is successfully executed for MRP
When distributed databases are used or online data collection devices are used, ensure the synchronisation/replication of records happen successfully without distortions and omissions.	(1) EFTPOS transactions are replicated with bank database and create the new balance B/F in the account
Implement periodic audit process for critical tangible objects that are recorded as data in database	(1) Annual audit for tangible assets in the organisation
Implement a validation mechanism in data transfers considering the business rules to monitor and ensure all records relevant to a event/transaction is transferred successfully.	(1) Rules to verify the number of records in the source file and destination file (2) All records relevant to a customer trip is transferred to the central database from online data stores
Maintain error logs for system transactions and regularly monitor them and perform relevant forensic activities to find missing records.	(1) A failed sales order creation

Validation Metric:

How mature is the process to prevent missing records

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

Example:	Source:
if Dept is a relation representing the employees of a given department, and one speciﬁc employee of the department is not represented as a tuple of Dept, then the tuple corresponding to the missing employee is in ref(Dept),and ref(Dept) diﬀers from Dept in exactly that tuple.	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete.	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
the database should contain all customers in North and South America, but it is known that the database reflects only a portion of the company’s customers. Coverage in this example is the percent- age of customers actually captured in the database compared to the population of all customers that should be in it.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.

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

Definition:	Source:
A record exists for every Real-World Object or Event the Enterprise needs to know about.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Completeness of data refers to the extent to which the data collected matches the data set that was developed to describe a specific entity. Monitoring for incomplete lists of eligible records or missing data items will identify data quality problems.	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.
Quality of having all data that existed in the possession of the sender at time the data message was created.	ISO 2012. ISO 8000-2 Data Quality-Part 2-Vocabulary. ISO.
Data is complete if no piece of information is missing – anti-example: "The Beatles were John Lennon, George Harrison and Ringo Starr"	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Every real-world phenomenon is represented.	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.