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. |
Statistical validity
| Characteristic Name: | Statistical validity |
| Dimension: | Validity |
| Description: | Computed data must be statistically valid |
| 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 statistical validity in data |
| The number of complaints received due to lack of statistical validity of data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Establish the population of interest unambiguously with appropriate justification (maintain documentation) | (1) Both credit customers and cash customers are considered for a survey on customer satisfaction. |
| Establish an appropriate sampling method with appropriate justification | (1) Stratified sampling is used to investigate drug preference of the medical officers |
| Establish statistical validity of samples -avoid over coverage and under coverage (maintain documentation) | (1) Samples are taken from all income levels in a survey on vaccination |
| Maintain consistency of samples in case longitudinal analysis is performed. (Maintain documentation) | (1) Same population is used over the time to collect epidemic data for a longitudinal analysis |
| Ensure that valid statistical methods are used to enable valid inferences about data, valid comparisons of parameters and generalise the findings. | (1) Poisson distribution is used to make inferences since data generating events are occurred in a fixed interval of time and/or space |
| Ensure that the acceptable variations for estimated parameters are established with appropriate justifications | (1) 95% confidence interval is used in estimating the mean value |
| Ensure that appropriate imputation measures are taken to nullify the impact of problems relating to outliers, data collection and data collection procedures and the edit rules are defined and maintained. | (1) Incomplete responses are removed from the final data sample |
Validation Metric:
| How mature is the process to maintain statistical validity of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete. | Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Coherence of data refers to the internal consistency of the data. Coherence can be evaluated by determining if there is coherence between different data items for the same point in time, coherence between the same data items for different points in time or coherence between organisations or internationally. Coherence is promoted through the use of standard data concepts, classifications and target populations. | 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. |
| 1) Accuracy in the general statistical sense denotes the closeness of computations or estimates to the exact or true values.
2) Coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. |
LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf. |