Is Data Governance Essential for Enterprise Cloud Migration?
Cloud-based data governance is essential for organizational success. The critical migration process requires an action plan involving several key components.
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Data governance is an integral part of a successful, comprehensive approach to building enterprise data applications in the cloud. Ensuring that data remains secure, compliant with relevant regulations, and well-managed is critical during and after the cloud migration process. In 2021, Gartner Data and Analytics reported that only 61 percent of leaders surveyed indicated an interest in optimizing their data processes. A data governance model helps mitigate risks, ensures data integrity, and builds trust with customers and stakeholders. To optimize the model’s effectiveness, it’s essential to work closely with legal and compliance teams to navigate the regulatory landscape and make informed decisions.
Many challenges come with cloud migration including service disruption, legacy system compatibility, scalability, and all aspects of data governance. Data is one of the most important assets businesses possess. As such, having a detailed data governance plan is critical. This involves knowing how data will be collected, used, stored, and disposed of, as well as creating privacy protocols and ensuring security. Benefits include cost control, regulatory compliance, managed risks, and improved customer and shareholder confidence.
To mitigate the challenges and make the most efficient migration, address these vital elements:
Data classification. Classify data based on sensitivity and importance while identifying critical data that requires special security and compliance measures.
Encryption. Implement encryption for data at rest and in transit while considering data masking or anonymization techniques to protect sensitive data in all environments.
Regulations. Understand data residency requirements imposed by industry-specific regulations or regional laws for compliance.
System accessibility. Institute robust access controls and identity management systems so only authorized individuals can access sensitive data, as noted by Industry Insider.
Recovery. Develop a data backup and recovery strategy to protect against data loss or corruption.
Data management. Establish data retention and deletion policies that comply with regulatory requirements and align with organizational business needs, along with data quality standards and metadata management practices, to ensure data is accurate, consistent, and well-documented. This includes data quality and profiling, having a data catalog, and data lineage.
Audits. Implement robust and regular auditing and monitoring capabilities to track data access and changes with cloud-native tools or third-party solutions to generate audit trails.
Roles and responsibilities. Assign and clearly identify roles and responsibilities for data stewards, custodians, and owners.
Assessment. Conduct a data protection impact assessment (DPIA) to assess the impact of migrating data to the cloud to address risks and take proactive measures to mitigate them.
Third-party data processors. Ensure third-party cloud services adhere to the same data governance and compliance standards.
Documentation. Maintain thorough records of data governance and compliance processes.
Incident responses. Develop an incident response plan that outlines how to handle data breaches or security incidents, then test the plan regularly for effectiveness.
Data mesh and fabric. Data mesh is an evolving approach, using complex API integrations across microservices to integrate data throughout the enterprise. Data fabric is an architecture layer and toolset that connects data across disparate systems and creates a unified view without coding and API integration. Adapt existing data governance practices that align with the principles of data mesh and fabric to help unify an enterprise’s systems and services.
Development plan and implementation
Before an organization creates an effective plan, it is essential to clearly define goals and policies for each step of the process. Then, develop a data governance framework that involves data ownership, stewardship, and accountability, including defined roles and responsibilities for managing data throughout its lifecycle.
Researching cloud migration options to determine what best suits a particular organization is critical to success. Once the decision is made and key elements are in place, then the plan can be put into motion. Staying flexible throughout the development and implementation process and having clearly defined processes are crucial. As the migration occurs, and once it is complete, regular monitoring and adjustment to make the best use of the cloud will boost chances for optimizing data governance.
Cloud migration options
Selecting a cloud provider with encryption services and tools to protect data from unauthorized access is crucial for privacy and security concerns. Because different regions and industries have their regulations and frameworks—such as GDPR, HIPAA, PCI DSS, and SOC 2—it is important to confirm that the data centers align with the applicable requirements. Depending on regulations and the company’s goals, automated data lifecycle management may save time and money, and many cloud services offer cloud-native tools that generate audit trails.
Cloud-based data governance is nothing new, but many organizations have not taken advantage of its benefits, significantly limiting the potential for success. According to Cloud Zero, over 60 percent of sensitive corporate data, including personal details about patients, customers, and employees, is already stored in the cloud. Without appropriate data governance and compliance, this information is at risk, compromising the organization’s long-term success. Putting a detailed plan in place with the right people and focusing on the key components is critical for effective cloud migration.
About the Author
Senthilkumar Thirunavukarasu is an IT professional with nearly 20 years of experience in data analytics, cloud computing, AI, and ML. He has successfully architected and built large-scale data integration solutions in the cloud for numerous enterprises across various industries, with a strong focus on setting up cloud-based infrastructure, building data engineering solutions, configuring security features, setting up data governance platforms and compliance standards. For more information, contact email@example.com.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.