How to Implement Effective Data Governance with Snowflake

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The term “data governance” doesn’t exactly scream “market expansion.” However, beneath this bureaucratic misnomer, data governance is the driving force that enables enterprises to become data-driven—a term that actually does scream “market expansion.” Data governance enables businesses to improve operational efficiencies, personalize customer experiences, drive innovation, and enhance decision-making.

For a company to thrive, it must tackle the challenges of data governance head-on. Such challenges include protecting sensitive data, guaranteeing data quality, and mastering the art of effective data lifecycle management.

The impact of data governance

Enterprises with robust data governance solutions and policies foster trust and confidence in their data by:

  • Making data-driven decisions with a higher degree of accuracy and reliability, iImproving business outcomes 
  • Building a reputation for having trustworthy data, strengthening collaboration with partners, stakeholders, and vendors 
  • Retaining a reputation for effective data privacy and security, which enhances customer trust and loyalty

Enterprises that underestimate the value of data governance may struggle to protect sensitive information, ensure data quality, and adhere to regulatory requirements. There’s so much at stake: data breaches, compliance violations, and reputational damage. Governance is not a nice-to-have—it’s a must-have.  

Data governance policies have impact beyond security and sharing permissions. Data governance tools ensure data is accurate, consistent, and complete. They uncover data quality issues by tracking how data has changed over time, aka lineage tracking. Data catalog providers like Snowflake recognize these gaps and are actively adding governance capabilities to their products. 

Snowflake’s enterprise data governance tools simplify data discovery. Its data catalog and centralized metadata repository make it easy to find and understand data. With Snowflake, you can streamline data operations, maintain integrity, and build a strong reputation for data privacy and security.

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Understanding Snowflake data governance

Effective data governance ensures you know, understand, and document every aspect of data. It prevents data from disappearing into a black hole of bits and bytes. Snowflake automatically aligns data with established norms, expectations, and security policies. 

Top 4 data governance areas of focus

Snowflake’s data governance framework prioritizes four key areas: data access controls, permissions, lineage tracking, and data stewardship. 

  • Access controls: Promote data security and privacy with role-based access controls (RBAC). Precisely manage data access by assigning roles based on job function or department. Access controls enforce compliance with data privacy regulations such as GDPR and CCPA. 
  • Permissions: Build security and accountability. Define specific user actions such as deleting, modifying, and adding data, granting privileges, and transferring object ownership. Create a secure environment for data sharing and promote enterprise-wide data confidentiality.
  • Lineage tracking: Track errors and foster data integrity. Visualize the flow of data across various systems and identify data quality issues. Optimize data workflows and make informed decisions with a full understanding of data asset dependencies. 
  • Data stewardship: Maintain accurate, consistent, and complete data by enforcing data governance policies. Governing data by predefined rules helps businesses ensure data is accurate, consistent, and complete. Helps cultivate a culture of responsible data management.

💡Ready to learn more about data sharing in Snowflake? Check out our quick start guide to Snowflake data sharing.

Snowflake data governance features and functionality

Snowflake’s built-in data governance features include column-level security, row-level access policies and security, object tagging, object tag-based masking policies, data classification, object dependencies, and access history. Each feature is integral to one or more of the following data governance components:

Data classification and labeling

Snowflake’s automated data classification feature streamlines data identification and protection. It also facilitates automated analysis of structured data columns, providing users with valuable insights into sensitive data. Data protection capabilities such as these are immensely helpful when managing and protecting sensitive data.

Object tagging allows organizations to assign custom metadata tags to different data objects, including tables, views, and databases. Organizations can also use tagging to categorize and classify data objects based on their specific attributes or properties. By assigning metadata tags, organizations can easily search, filter, and group related data objects for more efficient data discovery and retrieval.     

Object-tag-based masking policies provide a powerful mechanism to enforce dynamic data masking for sensitive data, leveraging the predefined object tags. Masking policies apply predefined rules that automatically obfuscate sensitive data, ensuring its protection. Enterprises rely on masking policies to safeguard sensitive information and restrict access to unmasked data only to authorized users.

By leveraging these features, Snowflake enhances data management practices, safeguarding sensitive information and promoting data security within the organization.

Access controls and permissions management

In Snowflake, data access control privileges determine who can access and use data for performing operations (such as querying, modifying, inserting, and deleting metadata) on specific data objects. Snowflake offers granular visibility of access controls so administrators can oversee individual user privileges and ensure compliance with data governance policies. This level of granular control helps enterprises maintain data privacy, meet regulatory requirements, and enforce data governance policies effectively.

Snowflake’s access granularity extends to rows and columns. Row-level access policies define rules and conditions that determine which rows of data a user or role can access within a table or dataset. Similarly, column-level security allows administrators to define specific access controls and permissions for individual columns.

Role-based access controls (RBACs) simplify data governance by defining access privileges for roles so administrators only have to do it once. Individuals who are assigned specific roles automatically gain access to data according to data governance policies. Snowflake users can switch between multiple roles to perform different actions using separate privileges. 

Snowflake users with appropriate access can even create custom roles. Once access is defined, specific privileges dictate the operations users can perform on data within the Snowflake system. The flexibility to define custom roles and assign specific privileges in Snowflake helps organizations align access privileges with their business requirements and data governance policies.

Data lineage and tracking

Snowflake’s data lineage feature enables users to:

  • Trace data origins 
  • Understand data transformations 
  • Identify data destinations 
  • Track errors.

Snowflake’s data lineage feature lets users unravel a complex web of data movements, much like how a GPS reveals the interconnected roads, intersections, and turns that lead to a destination.

Snowflake captures and stores metadata throughout the data processing lifecycle. Upon ingestion, Snowflake automatically collects metadata, including details about data sources, transformations, queries, and outputs. It then stores this metadata in its built-in metadata management system.

Object dependencies, a built-in data governance component within Snowflake, play a crucial role in determining data lineage. They provide insight into how data flows within the system so teams can understand how changes affect downstream processes. It also promotes a clear understanding of the dependencies and relationships between different objects, such as tables, views, and columns.  

Auditing and compliance features

Snowflake’s auditing and compliance feature ensures data integrity. Access history records all user activity related to data access and modification within a Snowflake account. It offers comprehensive audit trails that capture and retain detailed information about user activities, including data access, modifications, and system changes. With this feature, you’ll never need to sweat the word “audit” again. Snowflake also supports encryption at rest and in transit so data remains secure throughout its lifecycle.

Collaboration and workflow management

Snowflake recently introduced the public preview of listing discovery controls. This new feature enables data providers to privately and selectively list data, then share listings within their org while maintaining control.

In addition, Snowflake has introduced auto-fulfillment so users can easily access and share data without manual intervention. Snowflake provides usage analytics that reveal programmatic and visualized insights to facilitate workflow management. Teams rely on analytics to track data usage, understand patterns, and make informed decisions.

Implementing Snowflake data governance

To begin implementing data governance in Snowflake, teams must begin how they always do: By clearly defining the objectives of their data governance initiative and aligning it to business strategy. This involves assessing the current data landscape to identify areas that require governance, such as data sources, data quality, and compliance requirements.

Next, teams must establish a data governance framework with defined roles, responsibilities, and processes. Data classification, access controls, data lineage, and auditing should all be part of this framework.

Involving organizational stakeholders and ensuring their buy-in is essential to foster collaboration. Additionally, you should plan to monitor, maintain, and continuously improve their data governance practices. Incorporate regular assessments, feedback loops, and performance metrics to measure the effectiveness and impact of a data governance strategy. As a famous business adage states, “You can’t manage what you can’t measure.”

Enterprises that follow a well-defined strategy will maximize the benefits of data governance, ensuring data integrity, security, and compliance.

Data governance best practices

Rather than attempting to tackle all obstacles simultaneously, start small and concentrate on existing opportunities to enhance data management. With an iterative process, enterprises can refine their data governance practices and steadily advance toward their goals and foster a culture of data-driven growth. 

As data volumes increase and new data streams and access points emerge, incorporate periodic reviews of the data governance structure to see how it’s working. You should also identify data domains, establish access controls, and develop a plan for automated workflow processes and approval thresholds.  

Finally, enterprises can benefit from measuring progress against data management objectives and including the results in regular reporting and evaluation. Incorporate metrics like adoption rate, data issues, and program costs into your reporting. Armed with this information, businesses can make data-driven decisions, identify areas for improvement, and optimize their data governance strategies to drive better outcomes and achieve their business objectives.

Effective data governance: a value driver

By implementing effective data governance, businesses establish a foundation for data-driven decision-making, and build data quality and reliability. As a result, organizations gain a competitive advantage, enabling them to leverage their data assets effectively and scream “market expansion.”

Implementing effective data governance eliminates pointless tasks like manual data searching and verification, increasing productivity and resulting in faster data discovery. Businesses that streamline workflows save time and lower the cost of poor data management and manual processes. Similarly, investing in solid data governance practices enables organizations to unlock the full potential of their data, enabling better decision-making, operational efficiency, and overall business success. 

Effective data governance promotes cross-functional alignment, facilitating efficient data sharing and collaboration among internal teams. The result is a data-driven culture where data becomes a strategic asset that drives innovation, identifies opportunities, and supports informed decision-making at all levels.

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