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.

Data access control

Characteristic Name:	Data access control
Dimension:	Availability and Accessability
Description:	The access to the data should be controlled to ensure it is secure against damage or unauthorised access.
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 data access control

The number of complaints received due to lack of data access control

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Periodically evaluate the security needs considering the criticality of data (Value, confidentiality, privacy needs etc.) and accessibility requirements of data and then update the information security policy consistently.	(1) Employee salary is a confidential data and hence need security against unauthorised access. (2) Master data has a high economic value to the organisation and hence need security against unauthorised access and change
Continuously evaluate the risks threats and identify the vulnerabilities for data and update the information security policy	(1) The frequency of security assessment for data associated with online transactions was increased due to the high volume of online transactions.
Implementation of access controls for each critical information as prescribed by the information security policy.	(1) An Employee’s salary data can be viewed only by his or her superiors. (2) Master data can be created and updated only by the authorised executives. (3) Login credentials are required for system access
Data is stored in secured locations and appropriate backups are taken	(1) Databases are stored in a special server and backups are taken regularly (2) Documents are saved using a content management system in a file server
Restrict the accessibility of information using software based mechanism	(1) Data encryption (2) Firewalls
Restrict the accessibility of information using hardware based mechanism	(1) Security tokens

Validation Metric:

How mature is the process of ensuring data access control

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

Example:	Source:
if the ofﬁcial version of the minutes of a meeting is ﬁled by the records manager and thus protected from change, the unauthorised version will not form part of the ofﬁcial record.	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 information protected against loss or unauthorized access?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Data is appropriately protected from damage or abuse (including unauthorized access, use, or distribution).	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.
The extent to which information is protected from harm 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.
Access to data can be restricted and hence kept secure.	WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.