Achieving Data Governance through Data Orchestration – Data Orchestration Techniques

Achieving Data Governance through Data Orchestration

While a detailed discussion on data governance with supported tools has been covered in a separate chapter, this section aims to provide a concise overview of its implementation using data orchestration. Achieving data governance through data orchestration requires the implementation of specific practices and the effective utilization of data orchestration tools to enforce governance principles. Let’s examine a step-by-step approach with an example:

•     Define Data Governance Policies: Start by establishing data governance policies that align with your organization’s goals and regulatory requirements. These policies may include data quality standards, data classification guidelines, access controls, and data retention policies.

•     Implement Data Orchestration Tools: Choose a data orchestration tool that supports data governance capabilities, such as data lineage tracking, access control mechanisms, metadata management, and data quality monitoring.

•     Define Data Workflows: Design data workflows within the data orchestration tool that adhere to the defined data governance policies. These workflows should include data integration, transformation, and validation steps, incorporating data governance controls at each stage.

•     Establish Data Lineage and Metadata Management: Leverage the data orchestration tool’s capabilities to capture and manage data lineage and metadata. This involves documenting the origin, transformations, and usage of data, as well as maintaining metadata descriptions and data dictionaries.

•     Enforce Access Controls: Utilize the access control mechanisms provided by the data orchestration tool to enforce data governance policies. This includes role-based access control, authentication mechanisms, and data masking techniques to protect sensitive data.

•     Monitor and Validate Data Quality: Implement data quality monitoring within the data orchestration tool to continuously assess the quality of data. Set up automated checks and validations to detect and address data quality issues in real-time.

•     Regular Auditing and Compliance: Conduct regular audits to ensure compliance with data governance policies and regulatory requirements. Use the data lineage, metadata, and access control information captured by the data orchestration tool to facilitate audits and demonstrate compliance.

By following these steps and leveraging the capabilities of a data orchestration tool, organizations can achieve effective data governance. The example provided illustrates how data governance policies can be implemented and enforced through data orchestration, promoting data quality, security, and compliance throughout the data lifecycle.

Example

For instance, let’s consider a generic organization implementing data governance through data orchestration. The organization establishes a policy that mandates the classification of all customer data as sensitive and restricts access to authorized personnel only. They do the following:

•    To support their data governance initiatives, the organization selects a data orchestration tool that seamlessly integrates with their existing data management infrastructure. The chosen tool offers robust features, such as data lineage tracking, access control mechanisms, metadata management, and data quality checks. While most of the cloud tools for data orchestration now support these features, some of the common cloud tools that an organization can select is Azure Data Factory, an AWS data pipeline, a Google cloud dataflow, a Snowflake data cloud, and so forth.

•    They create a comprehensive data workflow within the data orchestration tool that encompasses the integration of customer data from various sources, data transformations with a focus on maintaining data quality, and validation of access permissions before storing the data.

•    Utilizing the data orchestration tool, the organization automates the capture of vital information such as source systems, applied transformations, and destination storage for each customer data record. Additionally, they leverage the tool’s capabilities to assign metadata descriptions and data classifications to the data, providing valuable context and organization.

•    By leveraging the access control mechanisms inherent to the data orchestration tool, the organization ensures that only users with the appropriate roles and permissions can access and modify customer data. Furthermore, sensitive information is safeguarded through data masking techniques, anonymizing it for users with restricted access.

•    Data quality is a top priority, and the data orchestration tool incorporates data quality checks throughout the integration and transformation processes. These checks validate the accuracy, completeness, and consistency of customer data. Whenever data quality thresholds are not met, the tool generates alerts or notifications to address any issues promptly.

•    The organization conducts periodic reviews of the data lineage,metadata, and access logs generated by the data orchestration tool. These reviews serve to validate compliance with their data governance policies and enable the creation of audit reports that demonstrate adherence to regulatory requirements.

In summary, through the implementation of data orchestration, this generic organization successfully enforces data governance by classifying customer data as sensitive, selecting a suitable data orchestration tool, creating a comprehensive data workflow, capturing essential metadata, enforcing access controls, conducting data quality checks, and conducting regular reviews to ensure compliance and demonstrate adherence to regulatory requirements.