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. |
Standards and regulatory compliance
| Characteristic Name: | Standards and regulatory compliance |
| Dimension: | Validity |
| Description: | All data processing activities should comply with the policies, procedures, standards, industry benchmark practices and all regulatory requirements that the organization is bound by |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due non adherence of standards and regulations |
| The number of complaints received due to non adherence to standards and regulations |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify the policies, procedures, standards, benchmark practices and any regulatory requirements that an Information object is bound by | (1) Each person's compensation criteria must be determined in accordance with the Annuities Based on Retired or Retainer Pay law. |
| Ensure that all data processing activities are well defined and documented based on the policies, procedures, standards, benchmarks and regulatory requirements. | (1) Process of making a damage estimate is well defined based on industry benchmarks |
| Ensure that the application programs cater for standards and regulatory compliance | (1) A software program to make damage estimates which includes all benchmark data |
| Regularly monitor the data processing activities and identify the problems and inefficiencies so that the corrective and preventive actions can be taken. | (1) Frequent delays in time sheet approvals results in delayed payments |
| Signs should be standardised and universally used | (1) In the line efficiency report, low efficiency lines are indicated using a RED light while a green light indicates high efficiency |
| Relevant standard, procedures, policies and regulations should be communicated to the users effectively | (1) Providing a guidelines for signs |
| Ensure that proper conversion tables are maintained and used in converting attribute vales to different measurement bases. | (1) Metric conversion tables are used to convert lbs to kgs. |
Validation Metric:
| How mature is the process maintain the adherence to standards and regulations |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid. | 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 the existence, completeness, quality, and documentation of data standards, data models, business rules, metadata, and reference data. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The data element has a commonly agreed upon enterprise business definition and calculations. | B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation. |
| SIGNS AND OTHER Information-Bearing Mechanisms like Traffic Signals should be standardized and universally used across the broadest audience possible. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Validity of data refers to data that has been collected in accordance with any rules or definitions that are applicable for that data. This will enable benchmarking between organisations and over time. | 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. |