Today, more and more organizations are adopting the cloud. This is primarily due to the benefits of cost savings and data sharing. The IT leaders overseeing the migration must not overlook the importance of data governance. Data governance is essential because it ensures people can access useful, high-quality data. Therefore, the question is not if a business should implement cloud data governance, but which data governance framework is best for them.
Cloud computing and analytics promise to enhance business agility and propel innovation by empowering organizations to leverage data in new ways. However, rapid increases in data privacy and security regulations have made it challenging for enterprises to access and leverage data in the cloud while remaining compliant.
This article highlights the definition of data governance and its importance as well as the different ways that companies can use data governance in cloud computing so you can better understand what’s data governance overall.
What is Cloud Data Governance?
Data governance is a collection that defines the processes, roles, policies, standards, and metrics for the end-to-end lifecycle of data that ensure the effective and efficient use of data and information for a company’s growth towards its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization and defines who can take what action, upon what data, in what situations, using what methods.
Cloud data governance takes on an additional dimension of complexity in multi-cloud or hybrid cloud computing environments, where data is found in multiple places and data governance protocols are inconsistent amongst databases.
The need for effective data governance increases when data gets stored in various locations and the cloud data governance procedures such as permissions, guidelines, and metadata is inconsistent across databases.
6 Ways Companies Can Use Data Governance in Cloud Computing
Automate the Discovery of Sensitive Data
Some data access governance platforms can automatically detect, classify and tag sensitive data across multiple platforms. Data teams are less likely to make mistakes by doing data classification by machine instead of by hand. When sensitive data is detected, it gets tagged so that any access control policies are triggered.
Sensitive Data Certification Workflow
Even when sensitive data discovery is automated, data governance teams must be able to certify that it has been detected, classified, and tagged appropriately. To meet these requirements, data architects and engineers should establish workflows for inspecting, reviewing, and approving the results of automated discovery and tagging.
Automated Reporting for Transparency
The combination of centralized data access and automated reporting ensures full transparency amongst data consumers, data architects, data engineers, and compliance teams when it comes to understanding the who, what, when, why, and how of data access. With the help of auditing and reporting capabilities, data teams can quickly generate automated reports that show who is accessing the data, why they are accessing the data, and how they are using the data.
When people need to access a data source or table, the authorization process should take seconds or minutes – not weeks or months, as is common when data owners, IT, security, and other stakeholders get involved. A centralized data governance platform allows data teams to review data requests and connect consumers with the resources they need.
Self-Service Access for Data Consumers
Modern data catalogs compile all data into a single, searchable platform, making it easier for data consumers to explore, discover and analyze it. Self-service access means data consumers can access any available data set they have the right permissions for, instead of having to manually request access from each separate data owner.
While all data consumers have access to the same catalog, data architects and engineers can restrict access to specific data sets based on user permissions, ensuring the protection of sensitive data.
Fine-Grained Data Access Controls
Data architects and engineers with fine-grained data access controls can create policies that restrict access to specific rows, columns, or cells within a table for unauthorized data consumers and allow organizations to remain compliant with data regulations and protect sensitive data that is contained in a table with other frequently used data or that must be accessed for a specific purpose.
The data teams would have to make a copy of the file and remove or anonymize the sensitive data before they could let anyone see it. So now, with dynamic data masking capabilities, sensitive data is hidden from unauthorized users without copying or moving data.
Monitor Data Usage for Auditing Purposes
Cloud data governance policies must be audited regularly to assess the effectiveness of the existing policies, identify any security risks or threats and enable ongoing compliance with regulatory requirements. To support the data audit trail, data architects and engineers should develop capabilities to monitor and log data usage.
Data-rich audit logs that include all data sources, when they were accessed, and all queries performed enable data teams to share data usage details with compliance and legal teams. These logs are essential for proving compliance and troubleshooting issues when needed.
For a more in-depth guide on data governance, you can learn from Satori’s Data Governance Guide and learn more about what data governance is in more advanced terms, the principles that go into it, along with its components, and a deeper look into the details of data governance.