Precision
| Characteristic Name: | Precision |
| Dimension: | Accuracy |
| Description: | Attribute values should be accurate as per linguistics and granularity |
| 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: |
|---|---|
| Ensure the data values are correct to the right level of detail or granularity | (1) Price to the penny or weight to the nearest tenth of a gram. (2) precision of the values of an attribute according to some general-purpose IS-A ontology such as WordNet |
| Ensure that data is legitimate or valid according to some stable reference source like dictionary/thesaurus/code. | (1) Spellings and syntax of a description is correct as per the dictionary/thesaurus/Code (e.g. NYSIIS Code) (2) Address is consistent with global address book |
| Ensure that the user interfaces provide the precision required by the task | (1) if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. |
| Ensure the data values are lexically, syntactically and semantically correct | (1) “Germany is an African country” (semantically wrong); Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong) |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain data precesion |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if v = Jack,even if v = John, v is considered syntactically correct, as Jack is an admissible value in the domain of persons’ names | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Data values are correct to the right level of detail or granularity, such as price to the penny or weight to the nearest tenth of a gram. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Data is correct if it conveys a lexically, syntactically and semantically correct statement – e.g.,the following pieces of information are not correct:“Germany is an African country” (semantically wrong);Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong). | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| The set S should be sufficiently precise to distinguish among elements in the domain that must be distinguished by users. This dimension makes clear why icons and colors are of limited use when domains are large. But problems can and do arise for the other formats as well, because many formats are not one-to-one functions. For example, if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. The trick is to provide the precision to meet user needs. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| Is the information to the point, void of unnecessary elements? | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| The degree of precision of the presentation of an attribute’s value should reasonably match the degree of precision of the value being displayed. The user should be able to see any value the attributer may take and also be able to distinguish different values. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |
| The granularity or precision of the model or content values of an information object according to some general-purpose IS-A ontology such as WordNet. | 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. |
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. |