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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.

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 →.

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