Governance is defined by Merriam-Webster as “the continuous exercise of authority over and the performance of functions for an organization,” so “data governance” would seem to mean the exercise of control over data. However, it is not that simple, data does not exist in a vacuum; business processes act on, change and manipulate data. Therefore, discussing data governance organizations must include business processes in the scope of items to be governed. Successful enterprise data governance requires identifying the root causes that impede business effectiveness, implementing governance over business processes as well as over data.
Include Business Processes in Data Governance
Current Focus Areas
- Meta Data
- Data Values
- Data Quality
- Data Security
Business Process Integration
Data does not exist in a vacuum – processes create and manage both data and metadata
“Include business processes”– how can data governance include business processes when the governance bodies (data governance council and data stewardship teams) have so much to focus on: meta data, data values, data quality, data security, etc…? Once again, data does not exist in a vacuum, since processes create and manage the data and metadata – they are inextricably linked together. By including a focus on process as well as data, the organization and the data governance effort can start to reduce the silo approach to interactions between business and technologies. Adopting this holistic view of data governance with attention to business processes as well as to data can give an organization an advantage in managing the totality of the environment it has created.
In a properly implemented data governance organization, there are intersection points across business operations. Data governance offers the potential to give the business an understanding of the data it needs and how that data is used, focusing on the business operations versus focusing on IT applications.
Challenges to Incorporating Business Processes in Data Governance
There are several challenges to incorporating business processes in a data governance effort. One is that the data governance formation team (data governance program team and the data governance council) must recognize that processes are part of the data governance landscape. Since many business processes have developed in an ad-hoc fashion, it may be difficult to include management and control of business processes at an enterprise level. The data governance council must accept this challenge and include all fundamental business processes in the data governance program. Why? Once again, data does not exist in a vacuum; processes create and manage that data!
Another challenge to including business processes is the need to identify stewards who are knowledgeable about both data and process for their subject areas. “Knowledgeable” means that they understand what the current state of the business processes and data are, what the data actually means and how it is used, and what processes are correct and which ones should be changed.
Identifying the right data stewards may lead to a significant elevation of influence and authority for those individuals who can answer the questions about the data and the processes. Discovering this set of knowledgeable people can extend knowledge sharing, continuous improvement and consensus across the organization – for both data and process. Frequently, poor data quality is the result of incorrect or broken business processes, and correcting / improving business processes often leads to improved data quality and to a deeper understanding of the “right” business process.
Establishing an Effective Data Governance Framework
A successful data governance strategy requires a well-defined organizational structure that supports both data and process management. The framework must encompass three essential pillars: people, processes, and tools. All should work in harmony to achieve organizational objectives.
Organizational Structure and Roles
The data governance team typically operates through a hierarchical structure like the following:
- Executive Sponsors: Senior management provides top-down support and strategic direction for data governance initiatives, ensuring company-wide adoption and resource allocation.
- Data Governance Committee: This group approves data governance policies and standards, handles escalated issues, and maintains alignment between business and technology teams.
- Data Owners: These stakeholders hold accountability for specific data domains within their business units, ensuring data assets meet quality standards and business requirements.
- Data Stewards: Operating as part of a central management team, stewards implement daily data standards, maintain data definitions, and oversee data quality within their domains.
Policy Framework and Compliance
Effective data governance requires clear policies that define:
- Accountability matrices across different organizational levels
- Data standards for quality, security, and usage
- Compliance requirements for regulatory frameworks like GDPR
- Procedures for data profiling and quality assessment
- Protocols for managing sensitive data and preventing data breaches
Quality Monitoring and Tools
Modern data governance frameworks leverage various tools and processes:
- Automated monitoring systems for tracking data quality metrics
- Regular auditing procedures for operational requirements
- Data profiling tools for ongoing quality assessment
- Centralized repositories for master data management
- Collaborative platforms for cross-functional team coordination
This structured approach ensures data governance activities align with both business processes and strategic objectives while maintaining regulatory compliance and data integrity.
One way to discover the alignment of data and the processes that create and manage it is to model the current and desired business processes and develop metadata (definitions, common terms, etc.) for the processes. A business process model facilitates the alignment of business specifications with the data the process needs. A shared model (not siloed) can help to keep the process and its data synchronized and reusable across the organization. Although this can be a major effort, similar to the development of an enterprise data model if taken to a significant detail, data governance programs find that the benefits to high-level business process modeling far outweigh the investment of time and resources.
A robust data governance initiative can establish common process architecture, modeling and implementation approaches for the fundamental business processes and the data needed for these processes, leading to the stewardship of both the data and processes important to the organization.
Conclusion
In high-performing organizations that value data and information as significant resources, good data governance extends beyond managing data to encompass the entire spectrum of governance processes. By implementing data governance best practices and establishing clear data governance principles, organizations can effectively bridge the gap between data silos while protecting critical data assets.
Maximizing Value Through Effective Data Governance Programs
A comprehensive data governance program delivers multiple benefits through its key capabilities: streamlined data flows, enhanced data capture processes, and improved handling of both structured and unstructured data. Modern data governance tools and software support proactive management of data volumes while enabling shared responsibility among data stakeholders, from data architects to data custodians. This coordinated approach helps organizations move away from reactive change management activities toward a more strategic orientation in making data-related decisions.
The success of data governance processes relies heavily on well-defined data governance roles and operational auditing requirements. When organizations embrace these principles, they create an environment where key metrics drive continuous improvement, and data governance features align with organizational objectives. This systematic approach ensures that data storage, data entity management, and data flows work in harmony to protect and optimize the organization’s data assets.
By integrating business processes with data governance principles, enterprises create a robust and holistic approach that maximizes the benefits of data governance while ensuring effective management of their critical data resources. This integration sets the foundation for sustainable growth, operational excellence, and strategic advantage in an increasingly data-driven business landscape.
