Data maintenance

Characteristic Name:	Data maintenance
Dimension:	Availability and Accessability
Description:	Data should be accessible to perform necessary updates and maintenance operations in it’s entirely
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

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

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:
Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible	(1) Sales order is created once a customer signs a contract. Then it is updated in three instances 1)Delivery date and shipment date is updated once the production plan is created. 2) Actual quantity is updated once the manufacturing is complete 3) Total cost is updated once the freight changes are incurred. A sales order is achieved after one years from delivery.
A maintenance policy for mission critical data should be developed and implemented to handle on going systematic updates (Create, read, update, delete, archive and cleanse)	(1) Customer data : Created when a customer enters into a contract, updated once the customer details change or contact change, archived once the contact end
When multiple versions of the same data is available through different datasets\databases create a master record and make it available across the systems	(1) Master data management
Leverage application and storage technology in such a way that the maintenance policies can be applied on data	(1)Addresses which were not updated during the last 24 months are prompted for validations
Create a responsibility structure/Authorisation structure and a communication structure to manage the process of information generation maintenance and utilisation	(1) It is the responsibility of the work study team to provide SMV (standard minute values) for a garment. (2) Approved SMVs should be sent to the planning department for planning purposes.

Validation Metric:

How mature is the data maintenance process

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:
A measure of the degree to which data can be accessed and used and the degree to which data can be updated, maintained, and managed.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Can all of the information be organized and updated on an on-going basis?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.

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.