Page Comparison

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1. An identification of core campus systems that produce data of common interest and the respective data owning department(s), individuals, and data stewards.  Examples:  Student Enrollment Data - Registrar/ Elizabeth Bennett;  Student Admissions - Deborah Decker;  Employee Payroll Data - Accounting and Fiscal Services/Brenda Mathias...
2. An assessment of the data collection and management practices and data quality of the systems.
3. Entry of common data attribute names, descriptions, and business rules into a campus data dictionary and catalogue along with aliases.
4. A data flow diagram per subject or data context of where the data is transmitted to and how often.
5. An assessment of what it means to implement a "zero data defect" policy for this data set.

The data governance maturity modelrequirements, highly based on a White Paper by DataFlux is  about data governance is separated into three areas, each with the goals: 

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1. Data governance has executive-level sponsorship with direct CIO and Executive Leadership support.  Executive-level decision-makers view data as a strategic asset.  Management understands and appreciates the role of data governance – and commits personnel and resources.
2. Business users take an active role in data strategy and deliverycollecting and delivering quality data.
3. Data stewards emerge as the primary implementers of data management strategy and work directly with cross-functional teams to enact data quality standards
4. A data quality or data governance group works directly with data stewards, application developers and database administrators
5. Organization has "zero defect" policies for data collection and management

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1. New initiatives are only approved after careful consideration of how the initiatives will impact the existing data infrastructure
2. Automated policies are in place to ensure that data remains consistent, accurate and reliable throughout the enterprise
3. A service oriented architecture (SOA) encapsulates business rules for data quality and identity management
4. Real-time activities and preventive data quality rules and processes emergeare implemented
5. Data governance processes are built into the SDLCSystems Development or System Acquisition Life Cycle
6. Goals shift from problem correction to prevention

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1. Data quality and data integration tools are standardized across the organization
2. All aspects of the organization use standard business rules created and maintained by designated data stewards
3. Data is continuously inspected – and any deviations from standards are resolved immediately.  Ongoing data monitoring helps the campus maintain data integrity
4. Data models capture the business meaning and technical details of common data elements
5. A data stewardship group maintains common data definitions and business rules
6. Service-oriented architecture becomes the enterprise standardOngoing data monitoring helps the campus maintain data integrity
7. More real-time processing is available and data quality functionality is shared and reviewed across different operational solutions

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