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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.

For more information, please refer to Whitepaper : Taking a Human Approach to Data Governance.

Topics : Data Modeling,Database Development,Enterprise Architecture,

Products : ER/Studio Data Architect,

Infographic : ER/Studio Data Architect

Take 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.

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