
Transcript
Taking a Human Approach to Data Governance
Successful data governance requires organizations to address human factors such as cultural change, which is the greatest obstacle to implementation.
Data maturity: The additional uses of data depend on the organization’s data maturity as follows:
- None – documentation & physical databases
- Initial – conceptual, logical & physical design
- Managed – governance metadata
- Advanced – business glossaries
- Optimized – data modeling
Process maturity: The effects of process maturity on data governance include the following:
- None – documentation
- Initial – BPM (business process modeling)
- Managed – process improvement
- Advanced – process design
- Optimized – mature data processing methodologies
Human factors: The human factors that impede data governance include:
- Resistance to change
- Inadequate planning
- Poorly defined goals
Change management: The sources of change resistance include extra work, uncertainty, and ripple effect. The solutions to these resistances include:
- Securing the appropriate human resources and rewards for extra effort
- Creating a process for the change with simple steps and clear timeline
- Identifying affected parties of the change and considering their point of view
Summary: The information capabilities of most organizations is already poor and continuing to decline, which directly impacts data governance efforts. Organizations need a high level of data and process maturity to implement data governance successfully. They should also use quantifiable metrics to measure their success in data governance over the long term.
Topics : Data Modeling,Database Development,Enterprise Architecture,
Products : ER/Studio Data Architect,
Taking a Human Approach to Data Governance
Successful data governance requires organizations to address human factors such as cultural change, which is the greatest obstacle to implementation.
ER/Studio Data Architect enables you to efficiently catalog your current data assets and sources across different platforms and track end-to-end data lineage. Simplify your data architecture with a common language leveraging consistent naming standards and data definitions. Easily specify the sensitive data objects that need heightened protection, to withstand audit scrutiny. Learn more →.