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 |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
Guidelines: | Scenario: |
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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: |
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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: |
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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. |
Business rules compliance
Characteristic Name: | Business rules compliance |
Dimension: | Validity |
Description: | Data should comply with business rules |
Granularity: | Element |
Implementation Type: | Rule-based approach |
Characteristic Type: | Declarative |
Verification Metric:
The number of tasks failed or under performed due to lack of data precision |
The number of complaints received due to lack of data precision |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
Guidelines: | Scenario: |
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Identify data related business rules separately (business rules that determines the value of data elements and business rules that get executed depending on the values of data elements) and organise them into a separate executable data rules engine | (1) The system maintains price routines to handle price related data Element :Discount rate A price routine (procedure) can be maintained to calculate the discount rate considering the rules R1:All registered customers get a discount of 6% |
Implement a stewardship structure for business rules (parallel to stewardship structure for data) and manage the changes to the rules properly | (1) Sales director is responsible for discounts and his approval is needed to change a discount rate. Only the sales manager can change the rules related to discounts. |
Maintain an error log to identify the problems resulted in the data rules repository where the problematic data records can be identified precisely | (1) Rules engines |
Continuously monitor the root causes for the errors recorded in the log and take preventive actions by amending the rules, fixing the technical defects in the system etc. | (1) Some trip data is missing for a particular journey in the go card system and as a result an unacceptable journey duration was resulted. New rules were implemented to process such data using a different criteria |
Validation Metric:
How mature is the creation and implementation of the data related business rules |
These are examples of how the characteristic might occur in a database.
Example: | Source: |
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One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases. | J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009. |
The Definitions are examples of the characteristic that appear in the sources provided.
Definition: | Source: |
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Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
1) Data values conform to the Specified Business Rules.
2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |