Most organizations by now recognize the importance of data governance in ensuring data value and achieving regulatory compliance. However, implementing an effective data governance strategy requires clear vision, cross-functional collaboration, a well-defined framework, and cultural adjustment. Ongoing evaluation and updates also prove essential.
1. Align Data Governance Goals with Business Priorities
Successful data governance begins with assessing the current state of data governance, then building a strategy that aligns with business goals and needs. Technologies and trends can and should support data governance. However, strategic planning should focus first on identifying existing risks and business needs and establishing priorities.
That is, begin by establishing why the organization needs data governance. For many companies, regulatory compliance provides initial motivation for improving management of data assets and practices. Additional motivations can include improving data quality and accessibility for decision making, as well as harnessing the power of data for innovation.
Then, identify the risks and practices that get in the way of reaching these business goals. For instance, insufficient data security inhibits regulatory compliance. Likewise, data silos reduce data accessibility and make it difficult to make informed decisions.
2. Define Data Governance Roles and Responsibilities
Data governance requires involvement from stakeholders throughout the organization, from executive sponsorship to end users. Some common roles and responsibilities include:
- Data governance sponsor – A senior executive who provides strategic direction, sponsorship, and oversight for the data governance program. With top management fully committed to data governance, it makes it easier to build engagement throughout the organization.
- Data governance council – This cross-functional committee sets the data governance vision, goals, policies, and priorities. They also approve the data governance framework and roadmap.
- Data owners – Data owners have authority and accountability for data within a specific domain or business area. In that role they define business requirements, rules, and standards for the data. They also approve data policies and procedures and have final say on who can access, modify, and share the data.
- Data custodians – This person or group provides technical support and maintenance related to data. They ensure that data is stored, backed up, archived, and disposed of according to data policies. They also have responsibility for protecting data from unauthorized access or use.
- Data stewards – These subject matter experts take responsibility for monitoring the quality, security, and usability of data. They also implement and enforce data policies and procedures and resolve any conflicts related to the data.
- Data users – These are the people who access and use data for a specific purpose or function. They have the responsibility to comply with data polices and procedures. They should also report any errors or breaches related to the data.
3. Determine Data Governance Framework
With roles and responsibilities defined, the organization can proceed with designing the data governance framework. This will involve defining data policies and processes that govern how data should be collected, stored, processed, shared, and utilized. These policies and processes help build data quality and ensure data remains secure and compliant with regulations.
Technology forms an important part of the data governance framework by providing the tools to support and automate data governance processes and tasks. While technology plays a critical role in data governance, organizations should take care to choose tools with the capability of delivering on defined objectives.
For example, common tools include data catalogs, archiving and eDiscovery solutions, compliance monitoring, automated sensitivity and retention labeling, and more. With the vast amounts of data companies must process, AI and automation prove essential to managing data at scale.
4. Data Culture Essential to Implementing an Effective Data Governance Strategy
Regardless of the policies and technology in place, any strategy will prove less than effective without a culture of data governance. Proactive companies focus on creating awareness throughout the organization around data governance benefits and best practices.
With a culture of collaboration and accountability, data governance ceases to be a burden or simply an issue of compliance. Rather, it becomes a strategic asset to drive competitive advantage.
5. Continually Review and Update
The data governance strategies that work today cannot possibly anticipate every aspect of next year’s data environment. Security and privacy regulations evolve constantly. And as business priorities change and technology advances, data governance programs will require adjustments.
Review and improve data governance programs on a regular basis. This will involve collecting feedback from stakeholders and users, as well as evaluating data governance performance against established metrics.
Get Started with Data Governance
Building effective data governance takes time. Resist the temptation to try and create the perfect governance model all at once. Instead, start with pilot projects that support business goals and build from there. The data governance experts at Messaging Architects can provide the guidance and tools to get you started.
Microsoft Copilot is NOW Available!
Your Everyday AI Companion