Processes and Guidelines
Processes
Institutional Data Governance aims to create a library of repeatable processes concerning the management of data. The goal is to apply these processes across the institution to ensure data at the university is managed in a standardized and consistent manner. The process library is found in the .
Guidelines
1) Establishing Definitions for Data Elements
A good Definition should express the essential nature of a data element and permit its differentiation from all other data elements. As described in the ISO/IEC 11179 IT Metadata Registry standard, the following are required and recommended characteristics of a good definition:
A data definition shall (required):
a) be stated in the singular
b) state what the concept is, not only what it is not
c) be stated as a descriptive phrase or sentence(s)
d) contain only commonly understood abbreviations
e) be expressed without embedding definitions of other data or underlying concepts
A data definition should (recommended):
a) state the essential meaning of the concept
b) be precise and unambiguous
c) be concise
d) be able to stand alone
e) be expressed without embedding rationale, functional usage, or procedural information
f) avoid circular reasoning
g) use the same terminology and consistent logical structure for related definitions
h) be appropriate for the type of metadata item being defined
2) Establishing Business Rules for Data Elements
Business Rules are constraints governing the characteristic or behaviour of a data element, or the relationship between data elements (DAMA). Business rules can fall under several categories:
• Constraints: Student Name cannot be empty
• Calculations: Profit = Total Revenue - Total Expenses
• Relationships: If data element A is X, then data element B must equal Y
• Qualifications/Filtering: An Active Student must have (a,b,c…) characteristics