Completeness of records
| Characteristic Name: | Completeness of records |
| Dimension: | Completeness |
| Description: | Every real world entity instance, that is relevant for the organization can be found in the data |
| Granularity: | Record |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to missing records |
| The number of complaints received due to missing records |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Implement a process level validation mechanism to avoid occurrence of missing records | (1) A buyer must record/verify an expense or asset in accordance with accepting/receiving any purchased items. (2)New application are stored in a temporary cabinet after entering into the system and they will be transferred to the file cabinet at the end of every week after the property manager cross check them with the system |
| Execute database commits upon transaction sequences in application programs and make sure all the transactions in the sequence successfully commit and generate the required records at the end of the sequence. | (1) In generating the MRP, the database operations will not be committed unless all materials in BOM is successfully executed for MRP |
| When distributed databases are used or online data collection devices are used, ensure the synchronisation/replication of records happen successfully without distortions and omissions. | (1) EFTPOS transactions are replicated with bank database and create the new balance B/F in the account |
| Implement periodic audit process for critical tangible objects that are recorded as data in database | (1) Annual audit for tangible assets in the organisation |
| Implement a validation mechanism in data transfers considering the business rules to monitor and ensure all records relevant to a event/transaction is transferred successfully. | (1) Rules to verify the number of records in the source file and destination file (2) All records relevant to a customer trip is transferred to the central database from online data stores |
| Maintain error logs for system transactions and regularly monitor them and perform relevant forensic activities to find missing records. | (1) A failed sales order creation |
Validation Metric:
| How mature is the process to prevent missing records |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if Dept is a relation representing the employees of a given department, and one specific employee of the department is not represented as a tuple of Dept, then the tuple corresponding to the missing employee is in ref(Dept),and ref(Dept) differs from Dept in exactly that tuple. | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
| if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete. | Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006. |
| the database should contain all customers in North and South America, but it is known that the database reflects only a portion of the company’s customers. Coverage in this example is the percent- age of customers actually captured in the database compared to the population of all customers that should be in it. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A record exists for every Real-World Object or Event the Enterprise needs to know about. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Completeness of data refers to the extent to which the data collected matches the data set that was developed to describe a specific entity. Monitoring for incomplete lists of eligible records or missing data items will identify data quality problems. | HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality. |
| Quality of having all data that existed in the possession of the sender at time the data message was created. | ISO 2012. ISO 8000-2 Data Quality-Part 2-Vocabulary. ISO. |
| Data is complete if no piece of information is missing – anti-example: "The Beatles were John Lennon, George Harrison and Ringo Starr" | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| Every real-world phenomenon is represented. | 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. |
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. |