Format consistency
| Characteristic Name: | Format consistency |
| Dimension: | Consistency |
| Description: | Data formats are consistently used |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of inconsistent data formats reported in an attribute per thousand records |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Maintain consistent formats for data values across different data bases and different tables in the same database. | (1) Telephone number : Country code/Area code/number (2) Address : House number, Street, Suburb, Sate, Country |
| Maintain structural similarity or compatibility of entities and attributes across systems (databases/data sets) and across time. | (1) Customer record has the same structure in all systems which it is being used. |
| Maintain consistent and compatible encoding /decoding standards across different applications. | (1) ASCII, UTF-8, XML |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain format consistency |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) Each class in a UK secondary school is allocated a class identifier; this consists of the 3 initials of the teacher plus a two digit year group number of the class. It is declared as AAA99 (3 Alpha characters and two numeric characters).
2) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers. 3) In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed. |
N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| if a data element is used to store the color of a person’s eyes, a value of TRUCK is invalid. A value of BROWN for my eye color would be valid but inaccurate, in that my real eye color is blue. | 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: |
|---|---|
| A measure of the equivalence of information stored or used in various data stores, applications, and systems, and the processes for making data equivalent | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The extent to which similar attributes or elements of an information object are consistently represented using the same structure, format, and precision. | 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. |
Continuity of data access
| Characteristic Name: | Continuity of data access |
| Dimension: | Availability and Accessability |
| Description: | The technology infrastructure should not prohibit the speed and continuity of access to the data for the users |
| Granularity: | Information object |
| Implementation Type: | Process-bases approacd |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to the lack of continuity in data access |
| The number of complaints received due to lack of continuity in data access |
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Convenient and efficient platform should be made available to access data depending on the task at hand | (1) For a sales person, a web based interface run on a smart device is more suitable to quickly access data |
| Speed of the data retrieval should be acceptable for users working pace | (1) For an online customer care executive, speedy retrieval of information is necessary since the customer cannot be kept waiting (2) With the growth of the database reports become slower (Anti example) |
| Continuous and unobstructed connectivity should be ensured for data retrievals | (1) Connection lost while accessing reports (Anti example) |
| Proper concurrency control has been implemented | (1) Controlling access to data by locks |
| Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible | (1) New version of the software does not provide access to " X out orders" since the new version does not allow the function "X out" |
Validation Metric:
| How mature is the process of maintaining an infrastructure for data access |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) For example, recording the age and race in medical records may be appropriate.
However, it may be illegal to collect this information in human resources departments. 2) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly. |
B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| 1) Is there a continuous and unobstructed way to get to the information?
2) Can the infrastructure match the user’s working pace? |
EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is easy and quick to retrieve. | 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. |
| 1) availability of a data source or a system.
2) Accessibility expresses how much data are available or quickly retrievable. 3) The frequency of failures of a system, its fault tolerance. |
SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |