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What is Data Governance? Definition, Framework, and Best Practices

A data governance program establishes standards, policies, and procedures to ensure that an organization uses information in its systems effectively and in alignment with its business objectives. Managing data quality, security, and access, it also delineates which parties can take action with data assets and enforces appropriate data protection. Leading tech research analyst Gartner defines data governance as the “specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics”.   

Data management vs. data governance

Data management refers to the collection of practices that enable an organization to gain control of its data resources and keep them organized in a safe, usable manner. The discipline of data management allows companies to build the data foundation they need to drive well-informed decision making and critical business insight.  

Data management comprises 11 core components, which include data governance. A well-developed enterprise strategy addresses each of these areas and covers every aspect of enterprise data, including policy execution and enforcement.   

Data governance framework and processes

A data governance framework is the architecture containing the data rules, role delegations, and procedures that make up an organization’s governance program. Creation of the framework includes data discovery, providing stakeholders with a unified view of enterprise data, data lineage, and metadata.

An organization’s data governance framework also provides a glossary of terms for its governance program and defines the program’s essential components, including processes to: 

  • Identify data owners
  • Enforce and monitor policy, including appropriate data access
  • Create reference data and master data
  • Establish data privacy and security guidelines
  • Store, provision, and deliver data
  • Establish program metrics
  • Create data audit trails 

Roles and responsibilities

Roles within a data governance program can include: 

  • A chief data officer (CDO) to oversee the initiative, secure funding, and advocate internally for the program
  • A data governance manager (in lieu of a CDO) to direct the program
  • A data governance committee to review and approve policies, rules, and procedures
  • Data stewards to ensure that policies and rules are implemented and that users adhere to them
  • An executive sponsor to coordinate data governance activities and serve as a liaison between senior stakeholders and the data governance lead
  • Data owners to make decisions related to data definitions and requirements

Data governance tools 

When leveraging an effective data governance tool to automate key governance processes, organizations benefit from:

  • Confidence that comes with knowing security and privacy measures are fully enforced in protecting data
  • Better decision making that results from access to high-quality, well-governed data
  • Clear insight into data lineage—how data was transformed, what changed, and why
  • Greater operational efficiency based on organizational insights (for example, inventory levels or customer satisfaction)
  • Improved production speed, product quality, or resource usage and the resulting revenue gains

Best practices

Let organizational priorities drive your operating model 

Operating models delineate how organizations define data governance roles, responsibilities, and data domains. Serving as the foundation for a data governance program, an operating model dictates what data is governed, how it’s governed, and who governs it. Best practices suggest that a company should allow current priorities to determine where to begin in developing a model; an organization’s maturity, structure, and data governance use cases can all come into play. 

Establish data domains early on

With a data governance structure in place, your organization will need to establish data domains, or logical groupings of interrelated information (e.g., customer, vendor, or product). Having a well-organized data domain system is helpful in empowering your data users and addressing issues proactively. Instead of waiting until problems arise, getting the system implemented early on is optimal.   

Focus on critical assets within your data domains

Data domains are far reaching, extending across countless systems and applications that contain information. Identify the data elements within your domains that are necessary for your business operations and focus on these assets instead of attempting to govern the full scope of your enterprise data.   

Implement control measurements

An effective data governance program requires well-thought-out controls to keep it in place. To implement control measurements, your organization will need to define workflows for key activities; align these workflows with your governance structure; establish processes for reporting on program progress; and create a mechanism for capturing feedback.   

Provide clear and thorough communication throughout the effort

Begin by getting buy-in from leaders across your organization; make sure to articulate how a data governance program will help them achieve strategic goals. While onboarding the program, clearly communicate expectations and procedures to data citizens. And finally, in the adoption phase, leading informational sessions and providing updates will encourage people across your organization to adhere to and participate in data governance practices.    

Use metrics to track and measure goals

This aspect of data governance can be challenging, but it’s important to measure your  program’s progress and impact. Commonly used metrics include the number of employees using governance technology, the volume of assets uploaded to the governance system, and financial return on investment; however, success for your organization depends on your specific objectives. 

Data governance challenges and benefits

In implementing data governance initiatives, enterprises report challenges that include limited resources, siloed data, a lack of data leadership, and data lacking business context. However, working through the difficulties to establish an effective data governance program can provide significant benefits: 

  • A holistic understanding of risk to your data and better alignment with data regulations
  • The ability to respond more quickly and easily to compliance audits
  • Well-informed decision making, more accurate analytics, and greater confidence in your organization’s data
  • Delivery of a more consistent and efficient customer experience

Data governance in the cloud

Like the data governance discipline for on-premises data environments, the practice for cloud data regulates access, integrity, use, and security. Best practices dictate:

  • Centralizing the control of your on-premises data and cloud data to consistently apply policies 
  • Adopting a scalable governance platform to easily extend as your data grows  
  • Empowering your users with self-service data access
  • Enforcing transparency with automated reporting capabilities 

Wondering how to build a data governance discipline that’s highly effective for your on-premises and cloud data environments? View our Modernizing Data Governance for the Cloud webinar now.

Andreu Pintado
Andreu Pintado