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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.

 

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.

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.