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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.

 

Information value

Characteristic Name: Information value
Dimension: Usability and Interpretability
Description: Quality information should provide a business value to the organization
Granularity: Information object
Implementation Type: Process-based approach
Characteristic Type: Usage

Verification Metric:

The number of tasks failed or under performed due to the lack of business value delivered by the information
The number of complaints received due to the lack of business value delivered by the information

GuidelinesExamplesDefinitons

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines: Scenario:
Continuously asses the relevance and the usefulness of existing data to the organisational goals (Strategic level). (1)What is the cost of poor quality customer data to the organisation in concern?
(2) What revenue can be generated from data?
Continuously asses the usefulness of information based on the tasks at hand (Operational level) (1) Can we predict our future market share from the existing market information?
Monitor and Measure if the intended goal of the data presentation/Interpretation is achieved (1) Employee efficiency data is displayed in a dash board to motivate employees. The effectiveness of this display can be measured by examining the efficiency gain of each employee.
(2) Has the given sales forecast for the last three years been reasonably accurate compared to actuals.

Validation Metric:

How mature is the process to maintain the business value of information

These are examples of how the characteristic might occur in a database.

Example: Source:
Consider a database containing orders from customers. A practice for handling complaints and returns is to create an “adjustment” order for backing out the original order and then writing a new order for the corrected information if applicable. This procedure assigns new order numbers to the adjustment and replacement orders. For the accounting department, this is a high-quality database. All of the numbers come out in the wash. For a business analyst trying to determine trends in growth of orders by region, this is a poor-quality database. If the business analyst assumes that each order number represents a distinct order, his analysis will be all wrong. Someone needs to explain the practice and the methods necessary to unravel the data to get to the real numbers (if that is even possible after the fact). 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:
1) A measure of the degree to which data will produce the desired business transaction or outcome.

2) A measure of the perception of and confidence in the quality of the data; the importance, value, and relevance of the data to business needs.

 D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
As a data quality-oriented organization matures, the agreement of usage will move from a small set of “early adopters” to gradually encompass more and more of the enterprise, Ubiquity measures the degree to which different departments in an organization use shared reference data. LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Data are beneficial and provide advantages for their use. WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.