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.

Referential integrity

Characteristic Name:	Referential integrity
Dimension:	Consistency
Description:	Data relationships are represented through referential integrity rules
Granularity:	Record
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of referential integrity violations 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:
Implement and maintain foreign keys across tables (Data sets)	(1) Implementation of foreign keys
Implement proper validation rules/Automated suggestions of values based on popular value combinations, to prevent incorrect references of foreign keys	(1) The attribute Customer_Zip_Code of the Customer relation contains the value 4415, instead of 4445; both zip codes exist in the Zip_Code relation
Implement validation rules for foreign keys of relevant tables in case of data migrations	(1) Error logs are generated for foreign key violations.
Implement proper synchronising mechanisms to handle data updates when there are concurrent operations or distributed databases.	(1) Locking mechanisms to data objects while being updated
Ensure the consistency of the data model when changes are done to process model (software)	(1) Data dictionary provides the FDs and CFDs

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain referential integrity

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

Example:	Source:
the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
A company has a color ﬁeld that only records red, blue, and yellow. A new requirement makes them decide to break each of these colors down to multiple shadings and thus institute a scheme of recording up to 30 different colors, all of which are variations of red, blue, and yellow. None of the old records are updated to the new scheme, as only new records use it. This data- base will have inconsistency of representation of color that crosses a point in time.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table

Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.

A company has a color ﬁeld that only records red, blue, and yellow. A new requirement makes them decide to break each of these colors down to multiple shadings and thus institute a scheme of recording up to 30 different colors, all of which are variations of red, blue, and yellow. None of the old records are updated to the new scheme, as only new records use it. This data- base will have inconsistency of representation of color that crosses a point in time.

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 Information Float or Lag Time is acceptable between (a) when data is knowable (create or changed) in one data store to (b) when it is also knowable in a redundant or distributed data store, and concurrent queries to each data store produce the same result.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Assigning unique identifiers to objects (customers, products, etc.) within your environment simplifies the management of your data, but introduces new expectations that any time an object identifier is used as foreign keys within a data set to refer to the core representation, that core representation actually exists.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
i.e. integrity rules. Data follows specified database integrity rules.	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.