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.

Semantic consistency

Characteristic Name:	Semantic consistency
Dimension:	Consistency
Description:	Data is semantically consistent
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of semantically inconsistent data reported 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:
Ensure that semantics of data is consistent within/across applications	(1) All orders placed by the customers are called “Sales order” in all tables/databases. (2) Anti-example: Payment type ( Check) Payment Details (Card type, Card number)
Maintenance of data dictionary or standard vocabularies of data semantics	(1) Data dictionary provides technical data as well as semantics of data

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain semantic consistency

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

Example:	Source:
School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
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:

School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database.

N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.

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:
Data about an object or event in one data store is semantically Equivalent to data about the same object or event in another data store.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Data is consistent if it doesn’t convey heterogeneity, neither in contents nor in form – anti examples: Order.Payment. Type = ‘Check’; Order. Payment. CreditCard_Nr = 4252… (inconsistency in contents); Order.requested_by: ‘European Central Bank’;Order.delivered_to: ‘ECB’ (inconsistency in form,because in the first case the customer is identified by the full name, while in the second case the customer’s acronym is used).	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
The extent of consistency in using the same values (vocabulary control) and elements to convey the same concepts and meanings in an information object. This also includes the extent of semantic consistency among the same or different components of the object.	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.