Data Governance

What is data governance?

  • The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets   (*DAMA International)                                 
  • Data governance is an integrated discipline for the identification of decision rights and accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information.

*The Data Management Association(DAMA International) is the Premiere organization for data professionals worldwide.DAMA International is a not-for-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management.

What is not data governance?

Understanding what data governance is not can help focus on what it is. The list below illustrates the types of projects where data governance is essential to be ultimately successful. In particular, data governance is not: 

  • Change management 
  • Data cleansing or extract, transform and load data (ETL)
  • Master Data Management (MDM) 
  • Data warehousing 
  • Database design 
  • Database management and administration 

What are Data governance Goals?

  • Define, approve and communicate data strategies, policies, standards, architecture, procedures, and metrics
  • Track and enforce conformance to data policies, standards, architecture, and procedures. 
  • Sponsor, track and oversee the delivery of data management projects and services. 
  • Manage and resolve data related issues. 
  • Understand and promote the value of data assets. 
Ultimately, poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point you either have to stop and clear the windshield or risk everything.
— Ken Orr, The Cutter Consortium

What are Data governance Inputs, Activities and Deliverables? (Click to expand Picture)

Examples 

Data Governance Metrics

Credit :IPL ( Chris Bradley)

Data Quality Metrics ( Click the picture for full view)

Credit: Christopher Bradley

Governance Framework( Click the picture for full view)

Credit :IPL ( Chris Bradley)

Overall Data Governance Maturity

Credit :IPL ( Chris Bradley)

I will be covering the various maturity model in another article.

 Benefits of Data Governance

  • Assurance and evidence that data is managed effectively reduces regulatory compliance risk and improves confidence in operational and management decisions
  • Known individuals, their responsibilities and escalation route reduces the time and effort to resolve data issues
  • Increased capability to respond to change and events faster through joint understanding across users and IT
  • Reduced system design and integration effort
  • Reduced risk of departmental silos and duplication leading to reconciliation effort and argument

 

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