Traceability

Characteristic Name:	Traceability
Dimension:	Reliability and Credibility
Description:	The lineage of the data is verifiable
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of traceability in data

The number of complaints received due to lack of traceability in data

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Maintain provenance records for the events such as creation, update,transcription, abstraction, validation and transforming ownership, if the data are dynamic.	(1) Inventory system shows the current stocks and keep records for all the transactions that the stocks are subjected to
In case of multiple sources are available for same data/information, implement a traceability mechanism to view all versions from multiple sources	(1) Content management systems
Maintain proper protocols/standards/policy to archive data	(1) Every invoice is archived after 120 days of payments.
Maintain versions of data records where necessary	(1) Customer versions

Validation Metric:

How mature is the process to maintain traceability in data

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

Example:	Source:
minutes of a meeting will be produced in draft form and reviewed by the members of the committee before being approved. Once this process of creation is ﬁnished the record must be ﬁxed and must not be susceptible to change. If a record is changed or manipulated in some way, it no longer provides evidence of the transaction it originally documented. For example, if someone alters the minutes of a meeting after they have been approved, the minutes can no longer be considered an accurate record of the meeting. This is another issue that becomes more important in an electronic context.	K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

Example:

Source:

minutes of a meeting will be produced in draft form and reviewed by the members of the committee before being approved. Once this process of creation is ﬁnished the record must be ﬁxed and must not be susceptible to change. If a record is changed or manipulated in some way, it no longer provides evidence of the transaction it originally documented. For example, if someone alters the minutes of a meeting after they have been approved, the minutes can no longer be considered an accurate record of the meeting. This is another issue that becomes more important in an electronic context.

K. Smith, “Public Sector Records Management: A Practical Guide”, Ashgate, 2007.

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

Definition:	Source:
Is the background of the information visible (author, date etc.)?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
A data provanance record can include information about creation, update, transcription, abstraction, validation and transforming ownership of data.	ISO 2012. ISO 8000-2 Data Quality-Part 2-Vocabulary. ISO.
The extent to which the correctness of information is verifiable or provable in the context of a particular activity.	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.

Standards and regulatory compliance

Characteristic Name:	Standards and regulatory compliance
Dimension:	Validity
Description:	All data processing activities should comply with the policies, procedures, standards, industry benchmark practices and all regulatory requirements that the organization is bound by
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due non adherence of standards and regulations

The number of complaints received due to non adherence to standards and regulations

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Identify the policies, procedures, standards, benchmark practices and any regulatory requirements that an Information object is bound by	(1) Each person's compensation criteria must be determined in accordance with the Annuities Based on Retired or Retainer Pay law.
Ensure that all data processing activities are well defined and documented based on the policies, procedures, standards, benchmarks and regulatory requirements.	(1) Process of making a damage estimate is well defined based on industry benchmarks
Ensure that the application programs cater for standards and regulatory compliance	(1) A software program to make damage estimates which includes all benchmark data
Regularly monitor the data processing activities and identify the problems and inefficiencies so that the corrective and preventive actions can be taken.	(1) Frequent delays in time sheet approvals results in delayed payments
Signs should be standardised and universally used	(1) In the line efficiency report, low efficiency lines are indicated using a RED light while a green light indicates high efficiency
Relevant standard, procedures, policies and regulations should be communicated to the users effectively	(1) Providing a guidelines for signs
Ensure that proper conversion tables are maintained and used in converting attribute vales to different measurement bases.	(1) Metric conversion tables are used to convert lbs to kgs.

Validation Metric:

How mature is the process maintain the adherence to standards and regulations

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

Example:	Source:
The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

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

Definition:	Source:
A measure of the existence, completeness, quality, and documentation of data standards, data models, business rules, metadata, and reference data.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The data element has a commonly agreed upon enterprise business definition and calculations.	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.
SIGNS AND OTHER Information-Bearing Mechanisms like Traffic Signals should be standardized and universally used across the broadest audience possible.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Validity of data refers to data that has been collected in accordance with any rules or definitions that are applicable for that data. This will enable benchmarking between organisations and over time.	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.