The exponential rise in enterprise data and regulatory requirements like the GDPR in the EU and CCPA in North America have made it imperative for organizations to prioritize adopting data governance. The market for data governance was valued at circa $2.73 billion in 2023, with projections up to $6.71 billion by 2028, according to Mordor Intelligence.
Data governance is not an isolated concept in big data management. Enterprises must consider data governance best practices, which revolve around various components, technologies, and implementation. In this article, we explore all these points.
What is a data governance framework, and why is it important for organizations?
A data governance framework lays the foundation for a solid data strategy and compliance. Beginning with the data model (comprising the data flows such as inputs, outputs, and storage parameters), the framework serves as the blueprint for how data governance will operate in an enterprise.
As organizations, particularly enterprises, undergo digital transformation and strive to achieve a higher level of data maturity, it is important to understand the importance of data governance frameworks and implement them. To summarize, a data governance framework comprises procedures and policies that:
- Ensure accurate handling of data and its storage
- Avoid manipulation of the data
- Define access and authorization controls for the data
Steps and best practices involved in creating a data governance framework
Identify goals and establish clear ownership
Define how your enterprise’s data can support strategic goals and help achieve desired outcomes. Once this is done, create a data governance team and assign clear ownership and accountability for different data domains. Keep documentation of the goals, roles, responsibilities, and the processes involved.
Roles and responsibilities in a data governance team include:
- Data administrators: These may include data modelers, architects, and quality analysts tasked with overseeing policy implementation, data repository management, and handling data-related conflicts.
- Data stewards: Crucial in data collaboration, data stewards define access policies, create data assets, and ensure the data quality and security are up to the mark.
- Data owners: Domain experts with deep domain knowledge who retain control over the datasets and draw insights from the data to drive market innovation and gain competitive advantage.
Implement robust data policies and procedures
The foundation of effective data governance lies in implementing robust policies and procedures. This includes standardizing data collection, storage, and usage practices across the organization, defining data classification and security protocols based on sensitivity levels, developing data quality management processes to safeguard accuracy and consistency, and enacting data lifecycle management policies to guide data throughout its lifespan.
Foster collaboration and communication
Requires establishing solid cross-functional teams that bring together business users, technologists, and legal experts to promote transparent communication about data assets and develop feedback mechanisms and training programs to encourage user involvement as well as continuous improvement.
Assemble the right tech stack and leverage the power of automation
Technology and automation play a pivotal role in streamlining data governance processes. Investing in the right technology stack can help enterprises automate tasks, enforce policies, and monitor data quality. Also, by integrating these technologies with existing IT infrastructures, enterprises can ensure seamless data flow while complying with the defined regulations.
Ensure flexibility for change and improvement
This involves regularly reviewing and adapting practices to align with evolving business needs and technological advancements, such as adopting artificial intelligence and machine learning (AI and ML), measuring the impact of data governance initiatives to demonstrate value, and fostering a culture of data awareness and responsible usage across the organization.
Importance and benefits of a data governance framework
Improvement in data quality
Data governance ensures the integrity and reliability of information, leading to higher-quality and more dependable datasets.
Reduced operational costs
Data governance reduces operational costs by streamlining processes, minimizing data errors, and optimizing resource utilization.
Comprehensive regulatory adherence
With robust data governance frameworks, enterprises can confidently navigate regulatory, legal, and industry compliance requirements, mitigating risks and ensuring adherence to standards.
Data-driven decision-making
Since data governance helps reduce errors and improves data quality, enterprise leaders can make data-driven decisions and deliver better organizational performance.
A single source of truth
Establishing a standardized data version by implementing data governance best practices fosters consistency and reduces discrepancies across the enterprise.
Wrapping up
Organizations, especially enterprises, should remember that implementing a data governance framework is not a one-time activity and requires continuous improvement with the emergence of new technologies, regulations, and increasing datasets. VentureDive has extensive experience providing data engineering services with a keen understanding of the best practices around data governance.