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. |
Data awareness
| Characteristic Name: | Data awareness |
| Dimension: | Availability and Accessability |
| Description: | Data users should be aware of all available data and its location |
| 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 awareness |
| The number of complaints received due to lack of continuity in data awareness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Educate users on the data landscape of the organisation and how to access the information when needed. | (1) Training programs are conducted to educate on using the systems and retrieve data when necessary. |
| Provide appropriate searching tools, manuals to find the required information. | (1) Data catalogs (2)Data Searching facility provided in application programs |
| Educate users on how data is collected and what are the procedures used in data collection so that they can decide on the appropriateness of the data for use. | (1) Opinion pole data during festival season is not appropriate to analyse drinking habits. |
Validation Metric:
| How mature is the process of creating the awareness of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 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: |
|---|---|
| Accessibility of data refers to how easily it can be accessed; the awareness of data users of what data is being collected and knowing where it is located. | 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. |