Redundancy
| Characteristic Name: | Redundancy |
| Dimension: | Consistency |
| Description: | The data is recorded in exactly one place |
| Granularity: | Record |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The volume of redundant data as a percentage to total data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Maintain the database schema eliminating the causes for redundancies of entities and attributes | (1) All customers are in customer table |
| Ensure that there are no redundant records across distributed databases | (1) Organisation has different customer bases maintained in different databases. But one customer is available only in one database |
| Ensure that same entity is not originally captured more than once in the systems | (1) Medical Insurance system refers employee bank details from the payroll. |
| Ensure that there are no temporary table backups are available in the database | (1) Created a backup for employees as employee_temp for a specific purpose and it is still in the database |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to eliminate the occurrence of redundant data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| A school has 120 current students and 380 former students (i.e. 500 in total) however; the Student database shows 520 different student records. This could include Fred Smith and Freddy Smith as separate records, despite there only being one student at the school named Fred Smith. This indicates a uniqueness of 500/520 x 100 = 96.2% | 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 unwanted duplication existing within or across systems for a particular field, record, or data set. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| There is only one record in a given data store that represents a Single Real-World Object or Event. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Determines the extent to which the columns are not repeated. | G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc. |
Data access control
| Characteristic Name: | Data access control |
| Dimension: | Availability and Accessability |
| Description: | The access to the data should be controlled to ensure it is secure against damage or unauthorised access. |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data access control |
| The number of complaints received due to lack of data access control |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Periodically evaluate the security needs considering the criticality of data (Value, confidentiality, privacy needs etc.) and accessibility requirements of data and then update the information security policy consistently. | (1) Employee salary is a confidential data and hence need security against unauthorised access. (2) Master data has a high economic value to the organisation and hence need security against unauthorised access and change |
| Continuously evaluate the risks threats and identify the vulnerabilities for data and update the information security policy | (1) The frequency of security assessment for data associated with online transactions was increased due to the high volume of online transactions. |
| Implementation of access controls for each critical information as prescribed by the information security policy. | (1) An Employee’s salary data can be viewed only by his or her superiors. (2) Master data can be created and updated only by the authorised executives. (3) Login credentials are required for system access |
| Data is stored in secured locations and appropriate backups are taken | (1) Databases are stored in a special server and backups are taken regularly (2) Documents are saved using a content management system in a file server |
| Restrict the accessibility of information using software based mechanism | (1) Data encryption (2) Firewalls |
| Restrict the accessibility of information using hardware based mechanism | (1) Security tokens |
Validation Metric:
| How mature is the process of ensuring data access control |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if the official version of the minutes of a meeting is filed by the records manager and thus protected from change, the unauthorised version will not form part of the official record. | 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: |
|---|---|
| Is the information protected against loss or unauthorized access? | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is appropriately protected from damage or abuse (including unauthorized access, use, or distribution). | 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. |
| The extent to which information is protected from harm in the context of a particular activity. | STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733. |
| Access to data can be restricted and hence kept secure. | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |