Compare Page

Objectivity

Characteristic Name: Objectivity
Dimension: Reliability and Credibility
Description: Data are unbiased and impartial
Granularity: Information object
Implementation Type: Process-based approach
Characteristic Type: Usage

Verification Metric:

The number of tasks failed or under performed due to biased and partial data
The number of complaints received due to biased or partial data

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
Identify all the factors that make a particular data/information biased for the intended use and take preventive actions to eliminate them (1) A written questionnaire is better than a face to face interviews in getting sensitive personal data
Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data collectors Perform a duel coder approach to code qualitative data.
Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data transmitters (1) After a survey is performed, each participant is contacted individually by a party (other than the person who conducted the survey) and randomly verify if the participants real responses have been marked properly.

Validation Metric:

How mature is the process to prevent biased and partial data

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

Example: Source:
Consider an inventory database that contains part numbers, warehouse locations, quantity on hand, and other information. However, it does not contain source information (where the parts came from). If a part is supplied by multiple suppliers, once the parts are received and put on the shelf there is no indication of which supplier the parts came from. The information in the database is always accurate and current. For normal inventory transactions and deci- sion making, the database is certainly of high quality. If a supplier reports that one of their shipments contained defective parts, this database is of no help in identifying whether they have any of those parts or not. The database is of poor quality because it does not contain a relevant element of information. Without that information, the database is poor data quality for the intended use. 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:
The degree to which Information is presented without bias, enabling the Knowledge Worker to understand the meaning and significance without misinterpretation. ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information free of distortion, bias, or error? EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
1) Data are unbiased and impartial

2) Objectivity is the extent to which data are unbiased (unprejudiced) and impartial.

 WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33.

 

Value consistency

Characteristic Name: Value consistency
Dimension: Consistency
Description: Data values are consistent and do not provide conflicting or heterogeneous instances
Granularity: Element
Implementation Type: Rule-based approach
Characteristic Type: Declarative

Verification Metric:

The number of inconsistent data values reported in an attribute per thousand records

GuidelinesExamplesDefinitons

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

Guidelines: Scenario:
For critical data elements provide standard classifications (values lists) for data entry interfaces and restrict arbitrary values across the system (1) Country, city are taken from a standard list.
(2) Generally accepted industry classifications are used to analyse customers industry wise (Education, Banking & Finance, Medical, Manufacturing…….
When data elements are combined for specific identification/management/accounting purposes, standardise such combinations and use them across the system. (1) Customer and sales order are combined for identification purposes
(2) Costs of wastage are associated with individual orders they are incurred and managed.
Define data attributes in such a way that data values are atomic and hence consistency can be maintained for any form of aggregation or consolidation Name is divided into first name Middle name and Last Name
Maintain consistency in using unit of measures across different tables and different data bases Sales price is in $ in Sales table and Accounts receivable ledger

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain value consistency

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

Example: Source:
School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database. N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
For example, data are inconsistent when it is documented that a male patient has had a hysterectomy. B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).
the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and filling in the name of the city systematically through the use of referential integrity with a postal code table Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
the data values ST Louis and Saint Louis may both refer to the same city. However, the recordings are inconsistent, and thus at least one of them is inaccurate. 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:
Domain Level: The data values persist from a particular data element of the data source to another data element in a second data source. Consistency can also reflect the regular use of standardized values, articularly in descriptive elements. Entity Level: The entity’s domains and domain values either persist intact or can be logically linked from one data source to another data source. Consistency can also reflect the regular use of standardized values particularly in descriptive domains. 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.
Determines the extent to which distinct data instances provide nonconflicting information about the same underlying data object. For example, the salary range for level 4 employees must be between $40,000 and $65,000. G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.
Data is consistent if it doesn’t convey heterogeneity, neither in contents nor in form – anti examples: Order.Payment. Type = ‘Check’; Order. Payment. CreditCard_Nr = 4252… (inconsistency in contents); Order.requested_by: ‘European Central Bank’;Order.delivered_to: ‘ECB’ (inconsistency in form,because in the first case the customer is identified by the full name, while in the second case the customer’s acronym is used). KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Consistency can be curiously simple or dangerously complex. In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. Two data values drawn from separate data sets may be consistent with each other, yet both can be incorrect. LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. A strict definition of consistency specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness. LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Consistency, in popular usage, means that two or more things do not conflict with one another. This usage extends reasonably well to data values, although a bit of added discipline is desired. REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.