Business rules compliance

Characteristic Name:	Business rules compliance
Dimension:	Validity
Description:	Data should comply with business rules
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:
Identify data related business rules separately (business rules that determines the value of data elements and business rules that get executed depending on the values of data elements) and organise them into a separate executable data rules engine	(1) The system maintains price routines to handle price related data Element :Discount rate A price routine (procedure) can be maintained to calculate the discount rate considering the rules R1:All registered customers get a discount of 6% R2:All gold customers get a discount of 12% R3:All purchases greater than $500 get a discount of 5%
Implement a stewardship structure for business rules (parallel to stewardship structure for data) and manage the changes to the rules properly	(1) Sales director is responsible for discounts and his approval is needed to change a discount rate. Only the sales manager can change the rules related to discounts.
Maintain an error log to identify the problems resulted in the data rules repository where the problematic data records can be identified precisely	(1) Rules engines
Continuously monitor the root causes for the errors recorded in the log and take preventive actions by amending the rules, fixing the technical defects in the system etc.	(1) Some trip data is missing for a particular journey in the go card system and as a result an unacceptable journey duration was resulted. New rules were implemented to process such data using a different criteria

Validation Metric:

How mature is the creation and implementation of the data related business rules

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

Example:	Source:
One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases.	J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009.

Example:

Source:

One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases.

J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009.

The Definitions are examples of the characteristic that appear in the sources provided.

Definition:	Source:
Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
1) Data values conform to the Specified Business Rules. 2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.

Definition:

Source:

Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter.

D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.

1) Data values conform to the Specified Business Rules.

2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules.

ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.

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.