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Data Governance

Data Governance

Data governance is a control that ensures that the data entry by an operations team member or by an automated process meets precise standards, such as a business rule, a data definition and data integrity constraints in the data model. The data governor uses data quality monitoring against production data to communicate errors in data back to operational team members, or to the technical support team, for corrective action. Data governance is used by organizations to exercise control over processes and methods used by their data stewards and data custodians in order to improve data quality.

Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. It’s about using technology when necessary in many forms to help aid the process. When companies desire, or are required, to gain control of their data, they empower their people, set up processes and get help from technology to do it.

Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will use them.

Some goals include:

  • Increasing consistency and confidence in decision making
  • Decreasing the risk of regulatory fines
  • Improving data security, also defining and verifying the requirements for data distribution policies
  • Maximizing the income generation potential of data
  • Designating accountability for information quality
  • Enable better planning by supervisory staff
  • Minimizing or eliminating re-work
  • Optimize staff effectiveness
  • Establish process performance baselines to enable improvement efforts
  • Acknowledge and hold all gain

 

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