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.

Data freshness

Characteristic Name:	Data freshness
Dimension:	Currency
Description:	Data which is subjected to changes over the time should be fresh and up-to-date with respect to its intended use.
Granularity:	Element
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

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

The number of complaints received due to lack of data freshness

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Identify the natural factors which creates a particular data obsolete	(1) A seasonal change may impact the customer's food preferences. (2) Customers who are students may change their addresses frequently.
Considering the above factors plan for data refreshing activities by specify the frequency of refreshing the data elements and adhere to the plan.	(1) Customer contact information should be refreshed annually.
Identify the master data that may change over the time but may be used in longitudinal analysis.	(1) Name of customer in 2001 is ABC (PLC) Ltd, after a merger in 2006 its name is XYZ (PLC). This customer is an ongoing customer in the customer master file
For such master data maintain longitudinal versions with time a stamp in such a way they can be linked in longitudinal analysis	(1) 2001-2005: ABC (PLC) (2) 2006-20012: XYZ (PLC)

Validation Metric:

How mature is the process for ensuring data freshness

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

Example:	Source:
let us consider two databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments	C. Cappiello, C. Francalanci, and B. Pernici, “Time-Related Factors of Data Quality in Multichannel Information System” in Journal of Management Information Systems, Vol. 20, No. 3, M.E. Sharpe, Inc., 2004, pp.71-91.
currency indicates how stale is the account balance presented to the user with respect to the real balance at the bank database.	V. Peralta, “Data Freshness and Data Accuracy: A State of The Art”, Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Uruguay, Tech. Rep. TR0613, 2006.
Consider an air traﬃc control center which receives data from several controller stations. To regulate air traﬃc, the traﬃc control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“eﬀective” decision must be made to regulate traﬃc;	B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010.

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

Definition:	Source:
A measure of the rate of negative change to the data.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Is the information upto-date and not obsolete?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Data is accurate if it is up to date – anti example: “Current president of the USA: Bill Clinton”.	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Currency refers to the degree to which information is current with the world that it models. Currency can measure how up to date information is and whether is it correct despite possible time-related changes. Timeliness refers to the time.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Currency refers to the degree to which information is current with the world that it models. Currency can measure how “up-to-date” information is, and whether it is correct despite possible time-related changes. Data currency may be measured as a function of the expected frequency rate at which different data elements are expected to be refreshed, as well as verifying that the data is up to date. For example, one might assert that the contact information for each customer must be current, indicating a requirement to maintain the most recent values associated with the individual’s contact data.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
A datum value is up-to-date if it is correct in spite of a possible discrepancy caused by time related change to the correct values; a datum is outdate at time t if it is incorrect at t but was correct at some time preceding t. currency refers to a degree to which a datum in question is up-to-date.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.