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 |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| 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: |
|---|---|
| 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: |
|---|---|
| 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. |
Data volume
| Characteristic Name: | Data volume |
| Dimension: | Completeness |
| Description: | The volume of data is neither deficient nor overwhelming to perform an intended task |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to not meeting the right volume of data |
| The number of complaints received due to volume related issues |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Define the scope of data in terms of organisational coverage to perform a business activity | (1) At least 70% of the production units should submit data to calculate total production efficiency of the company |
| Define the scope of data in terms of activities relates to any business task | (1) Pages with more than thousand hits per day and above are considered for the analysis |
| Define the scope of data in terms of the population of data which is under concern | (1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth |
| Define an appropriate amount of records in terms of lower limit and upper limit for any task | (1) At least six responses should be available to evaluate a tutor's skills and competency. |
Validation Metric:
| How mature is the process of defining and maintaining appropriate data volumes of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table. | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the availability and comprehensiveness of data compared to the total data universe or population of interest. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Is the scope of information adequate? (not too much nor too little). | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Degree of presence of data in a given collection. | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
| The quantity or volume of available data is appropriate | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |