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.

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:
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:
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.

Example:

Source:

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:
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.