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. |
Referential integrity
| Characteristic Name: | Referential integrity |
| Dimension: | Consistency |
| Description: | Data relationships are represented through referential integrity rules |
| Granularity: | Record |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of referential integrity violations 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: |
|---|---|
| Implement and maintain foreign keys across tables (Data sets) | (1) Implementation of foreign keys |
| Implement proper validation rules/Automated suggestions of values based on popular value combinations, to prevent incorrect references of foreign keys | (1) The attribute Customer_Zip_Code of the Customer relation contains the value 4415, instead of 4445; both zip codes exist in the Zip_Code relation |
| Implement validation rules for foreign keys of relevant tables in case of data migrations | (1) Error logs are generated for foreign key violations. |
| Implement proper synchronising mechanisms to handle data updates when there are concurrent operations or distributed databases. | (1) Locking mechanisms to data objects while being updated |
| Ensure the consistency of the data model when changes are done to process model (software) | (1) Data dictionary provides the FDs and CFDs |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain referential integrity |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and filling in the name of the city systematically through the use of referential integrity with a postal code table | Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006. |
| 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: |
|---|---|
| The Information Float or Lag Time is acceptable between (a) when data is knowable (create or changed) in one data store to (b) when it is also knowable in a redundant or distributed data store, and concurrent queries to each data store produce the same result. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Assigning unique identifiers to objects (customers, products, etc.) within your environment simplifies the management of your data, but introduces new expectations that any time an object identifier is used as foreign keys within a data set to refer to the core representation, that core representation actually exists. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| i.e. integrity rules. Data follows specified database integrity rules. | 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. |