Continuity of data access

Characteristic Name:	Continuity of data access
Dimension:	Availability and Accessability
Description:	The technology infrastructure should not prohibit the speed and continuity of access to the data for the users
Granularity:	Information object
Implementation Type:	Process-bases approacd
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to the lack of continuity in data access

The number of complaints received due to lack of continuity in data access

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Convenient and efficient platform should be made available to access data depending on the task at hand	(1) For a sales person, a web based interface run on a smart device is more suitable to quickly access data
Speed of the data retrieval should be acceptable for users working pace	(1) For an online customer care executive, speedy retrieval of information is necessary since the customer cannot be kept waiting (2) With the growth of the database reports become slower (Anti example)
Continuous and unobstructed connectivity should be ensured for data retrievals	(1) Connection lost while accessing reports (Anti example)
Proper concurrency control has been implemented	(1) Controlling access to data by locks
Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible	(1) New version of the software does not provide access to " X out orders" since the new version does not allow the function "X out"

Validation Metric:

How mature is the process of maintaining an infrastructure for data access

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

Example:	Source:
1) For example, recording the age and race in medical records may be appropriate. However, it may be illegal to collect this information in human resources departments. 2) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly.	B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).

Example:

Source:

1) For example, recording the age and race in medical records may be appropriate.

However, it may be illegal to collect this information in human resources departments.

2) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly.

B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).

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

Definition:	Source:
1) Is there a continuous and unobstructed way to get to the information? 2) Can the infrastructure match the user’s working pace?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Data is easy and quick to retrieve.	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.
1) availability of a data source or a system. 2) Accessibility expresses how much data are available or quickly retrievable. 3) The frequency of failures of a system, its fault tolerance.	SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.

Definition:

Source:

1) Is there a continuous and unobstructed way to get to the information?

2) Can the infrastructure match the user’s working pace?

EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.

Data is easy and quick to retrieve.

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.

1) availability of a data source or a system.

2) Accessibility expresses how much data are available or quickly retrievable.

3) The frequency of failures of a system, its fault tolerance.

SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15.