Data Governance Checklist for Snowflake Migration: 7 Steps to Reduce Failure Rates by 64%

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 1st, 2026 | 33 min read

Quick answer: 7-Step Governance Checklist for Snowflake Migration

Complete these 7 steps before migration:

  • Step 1: Establish governance framework with councils and stewards (2-3 months)
  • Step 2: Configure Snowflake RBAC and access policies (3-4 weeks)
  • Step 3: Implement data classification and sensitivity tagging (4-6 weeks)
  • Step 4: Set up automated lineage tracking across sources (2-3 weeks)
  • Step 5: Define and enforce data quality standards (3-4 weeks)
  • Step 6: Configure security controls and encryption (2-3 weeks)
  • Step 7: Validate compliance and audit readiness (2-4 weeks)

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Most Snowflake migrations fail without proper governance planning. Over 80% of data migration projects fail or exceed budgets, and the primary cause is treating governance as an afterthought rather than a migration prerequisite. You can dramatically reduce this failure risk by implementing governance before you migrate any data. This 7-step checklist covers the governance framework, RBAC configuration, data classification, lineage tracking, quality standards, security controls, and compliance validation you’ll need across 6-12 months. Complete these steps before migration, and you’ll have trusted data from day one instead of spending months fixing problems in production.

Why does governance matter before Snowflake migration?

Eighty-three percent of data migration projects fail or exceed their budgets, and the biggest driver is skipping governance until after you’ve moved data. When you treat governance as a parallel workstream rather than a prerequisite, you create data silos, access chaos, compliance gaps, and broken stakeholder trust that take months to fix in production.

Governance-first migrations flip this pattern. When you implement governance before moving data, you significantly lower the odds of failure or budget overruns. You get unified policies from day one, automated controls that prevent problems before they happen, and audit-ready systems that pass compliance reviews without scrambling. The choice is simple: invest 15-20% of your migration budget in upfront governance, or spend 3-5 times that amount fixing governance debt after go-live.

Metric Without Governance With Governance
Failure Rate 83% fail or exceed budget Far lower failure/overrun rate
Time to Trusted Data 12-18 months 6-9 months
Compliance Risk High (regulatory fines, audit failures) Low (audit-ready from day one)
Total Cost 3-5x more (post-migration remediation) Baseline + 15-20%
Stakeholder Trust Damaged (broken analytics, access issues) Strong (reliable data, clear ownership)

The cost comparison is stark. Organizations that implement governance after migration spend substantial resources revoking over-provisioned access, classifying unmarked sensitive data, and documenting undocumented policies—all while trying to keep production systems running. Teams that govern upfront avoid this governance debt entirely.

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Step 1: How do you establish a data governance framework? (2-3 months)


Start by forming a governance council that includes your CDO or senior data leader, data stewards from each business domain, domain owners who understand specific data contexts, and a compliance lead who knows regulatory requirements. This council makes policy decisions, resolves conflicts, and provides executive sponsorship. Plan for 2-3 months to secure stakeholders, align on decision rights, and document your initial governance charter.

Your first deliverable is a governance charter that defines who owns data decisions, how policies get created and approved, and what escalation paths exist when conflicts arise. Data leaders increasingly prioritize governance over pure infrastructure, recognizing that technology alone doesn’t solve data problems without clear accountability.

Break this work into three phases:

1.1 Form your governance council (3-4 weeks)


Identify specific individuals, not just roles. You’ll need executive sponsorship (typically your CDO, CDAO, or VP of Data), operational leadership (data stewards who enforce policies daily), domain expertise (business owners who understand data context), compliance knowledge (someone who tracks regulatory requirements), and technical implementation capability (engineering leads who configure systems). These people will meet bi-weekly initially to make policy decisions.

1.2 Define decision rights and escalation paths (3-4 weeks)


Document who decides what. Use a RACI matrix to clarify responsibility (who executes), accountability (who approves), consultation (who provides input), and information (who needs to know). For example, data stewards might be responsible for classifying data, domain owners accountable for approving classifications, the governance council consulted on edge cases, and the broader organization informed of new policies.

1.3 Document core policies (4-6 weeks)


Create written policies for data access (who can see what data under which conditions), classification (how you label sensitive data), retention (how long you keep different data types), and quality (what standards data must meet). These policies should reference specific regulations where applicable—GDPR for EU customer data, HIPAA for healthcare information, PCI-DSS for payment card data.

Role Responsibility Time Commitment Key Deliverables
CDO/CDAO Executive sponsorship, budget approval, conflict resolution 2-4 hours/week Governance charter, policy sign-off, budget allocation
Data Steward Policy enforcement, classification oversight, access reviews 10-15 hours/week Data catalogs, access certifications, quality monitoring
Domain Owner Business context, use case validation, data quality definition 5-8 hours/week Domain policies, quality rules, user requirements
Compliance Lead Regulatory interpretation, audit preparation, risk assessment 3-5 hours/week Compliance mapping, audit documentation, risk reports
Technical Lead System configuration, automation setup, tool integration 15-20 hours/week RBAC implementation, lineage configuration, policy enforcement

Without this foundation, your migration will lack clear decision rights. When conflicts arise (and they will—about access permissions, data ownership, or quality standards), you’ll have no process to resolve them. Teams will work around governance rather than with it, creating shadow systems and compliance gaps.

Step 2: How do you configure Snowflake RBAC and access policies? (3-4 weeks)


Configure role-based access control in Snowflake by connecting your identity provider (Okta, Azure AD, or Google Workspace) for SSO and SCIM provisioning, then designing a role hierarchy that grants permissions through functional roles rather than individual users. Proper RBAC reduces unauthorized access incidents by 85% according to Snowflake’s security best practices. Snowflake Horizon provides robust native RBAC—this works well for Snowflake-only environments where all your governance happens within one platform. For multi-platform estates, extend policies through catalog integration to keep access controls consistent across systems.

Before configuring technical controls, determine your governance scope:

Scenario Snowflake Horizon Native Add Universal Catalog
Single Snowflake workspace Sufficient Not needed initially
Multiple Snowflake workspaces Recommended Required for consistency
Multi-cloud data estate (AWS, Azure, GCP) Sufficient within Snowflake Required for cross-platform governance
Business user adoption priority Limited (technical audience only) Required for self-service discovery
BI tool governance needed (Tableau, Power BI) Sufficient for Snowflake data Required to extend to BI layer

If you operate a single Snowflake workspace with primarily technical users, Horizon’s native capabilities handle most governance needs. When your data spans multiple cloud platforms, transformation tools, and business intelligence systems, extend Snowflake’s governance through bi-directional integration to maintain consistent policies everywhere.

2.1 Connect your identity provider


Set up SSO through your corporate identity provider to centralize authentication. Enable SCIM provisioning so user additions, changes, and departures in your identity system automatically sync to Snowflake. This eliminates the manual work of managing Snowflake users separately and ensures terminated employees lose access immediately.

2.2 Design your role hierarchy


Create functional roles that represent job responsibilities, not individual people. Start with account-level roles (ACCOUNTADMIN, SECURITYADMIN, SYSADMIN), add domain-specific roles (DATA_ENGINEER_FINANCE, ANALYST_MARKETING), and finish with consumption roles (BUSINESS_USER_READONLY). Each role inherits permissions from roles below it in the hierarchy, letting you grant broad access through a single role assignment.

2.3 Configure future grants


Set up future grants so objects created in specific schemas automatically receive appropriate permissions. For example, when an engineer creates a new table in the FINANCE schema, your DATA_ENGINEER_FINANCE role should automatically get SELECT, INSERT, UPDATE, and DELETE privileges without manual intervention. This automation prevents permission gaps as your environment grows.

2.4 Implement managed access schemas


For sensitive data, create managed access schemas where only the schema owner can grant privileges. This prevents object owners from bypassing governance by granting permissions directly. All access requests go through your governance process instead of being handled ad-hoc by individual table owners.

Role Level Example Roles Permissions Scope Use Case
Account Admin ACCOUNTADMIN Full system control, user management, billing Reserved for platform administrators only
Security Admin SECURITYADMIN Grant/revoke access, manage roles, security policies Governance team, compliance lead
Data Engineer DATA_ENGINEER_FINANCE, DATA_ENGINEER_MARKETING Full access within domain (read, write, transform) Domain engineering teams
Data Analyst ANALYST_FINANCE, ANALYST_MARKETING Read access within domain, transformation capability Domain analytics teams
Business User BUSINESS_USER_READONLY Read-only access to certified data products Self-service consumers

Design your hierarchy with the principle of least privilege—grant the minimum access needed for each role to do its work. You can always expand permissions later, but revoking over-provisioned access is substantially more difficult once users depend on it.

Step 3: How do you implement data classification and tagging? (4-6 weeks)


Start with Snowflake Horizon’s automatic PII detection as your foundation—it identifies sensitive data patterns across your environment without manual scanning. Apply Object Tags to enforce policies programmatically based on classifications. For multi-system environments where data moves between Snowflake and other platforms, extend classification through bi-directional tag sync to keep policies consistent everywhere.

Snowflake Horizon Catalog provides strong classification capabilities within Snowflake workspaces. The platform automatically detects personally identifiable information (PII), applies Object Tags for policy enforcement, and links tags to masking policies. For single-platform environments where all your governed data lives in Snowflake, these native capabilities may be sufficient. Add universal catalog integration when your data estate spans multiple systems and you need tag propagation across platforms or business user discovery beyond technical teams.

3.1 Define your classification schema


Align classifications with specific regulations your organization must follow. For GDPR compliance, classify EU resident data. For HIPAA, identify protected health information. For PCI-DSS, tag payment card data. Your classification levels typically include Public (freely shareable), Internal (employee access only), Confidential (restricted to specific roles), and Restricted (minimal access with logging).

3.2 Enable automatic PII detection


Snowflake Horizon continuously scans tables to identify PII patterns like email addresses, phone numbers, social security numbers, and credit card numbers. Review the automatically detected classifications to verify accuracy, then approve them to create your baseline. This automated approach identifies most sensitive data versus minimal coverage through manual documentation.

3.3 Apply Object Tags for enforcement


Create Object Tags in Snowflake that represent classification levels: PII, PHI, PCI_DATA, CONFIDENTIAL, PUBLIC. Apply these tags to tables, views, and columns. Link tags to masking policies so accessing PII-tagged columns automatically applies masking for unauthorized users. This programmatic enforcement prevents human error in policy application.

3.4 Sync tags across platforms (multi-system environments)


For data that moves between Snowflake and other systems, implement bi-directional tag synchronization. Classifications applied in Snowflake propagate to connected catalogs, and tags created in external systems sync back to Snowflake Object Tags. This keeps governance policies consistent regardless of where users access data.

Classification Level Examples Access Requirements Regulatory Driver
Public Marketing content, published reports, public datasets No restrictions None
Internal Employee directories, project plans, internal dashboards Valid employee credentials Data protection policies
Confidential Salary data, customer contracts, strategic plans Role-based approval required Employment law, commercial sensitivity
Restricted PII (names, emails, addresses), PHI (medical records), PCI (card numbers) Minimal access with audit logging GDPR, HIPAA, PCI-DSS
Highly Restricted Full SSNs, unmasked credit cards, detailed medical diagnoses Exceptional approval only, time-limited GDPR, HIPAA, PCI-DSS

The key is connecting classifications to automated enforcement. Tags without linked policies are just labels that people can ignore. Tags with masking policies, access restrictions, and monitoring create real governance guardrails.

Step 4: How do you set up automated lineage tracking? (2-3 weeks)


Data lineage shows you exactly where your data comes from and where it goes. Configure automated lineage extraction from Snowflake Account Usage views to capture query history, then integrate transformation tool metadata from dbt, Airflow, and Fivetran to track data movement. When a source system changes, column-level lineage reduces impact analysis time from hours to seconds.

4.1 Extract lineage from Snowflake query history


Snowflake automatically logs every query in the QUERY_HISTORY view within Account Usage. Parse these queries to identify table and column relationships: which tables feed which transformations, which views depend on which base tables, which columns flow from source to destination. This gives you lineage coverage within Snowflake without additional instrumentation.

4.2 Integrate transformation tool metadata


Your transformation logic likely lives in tools like dbt (SQL transformations), Airflow (workflow orchestration), or Fivetran (data ingestion). Connect these tools’ metadata APIs to extend lineage upstream from Snowflake sources and downstream to consumption layers. For example, dbt’s manifest.json file documents every model’s dependencies—integrate this to show how source tables transform into analytics-ready views.

4.3 Map downstream BI dependencies


Track which Tableau dashboards, Looker reports, and Power BI visualizations query which Snowflake tables. When you deprecate a table or change a column definition, lineage tells you which reports will break. You can proactively notify report owners instead of discovering breakage through user complaints.

4.4 Enable column-level impact analysis


Table-level lineage shows you that Table A feeds into Table B. Column-level lineage shows you that CUSTOMER_EMAIL in Table A maps to EMAIL_ADDRESS in Table B, even though the column names differ. This precision lets you answer questions like “If I add PII masking to this column, which downstream reports lose access?” with confidence.

Layer Tools/Systems Metadata Captured Update Frequency
Source Systems Oracle, PostgreSQL, MySQL, S3 Schema, table relationships, source lineage Daily (schema changes)
Ingestion Fivetran, Airbyte, Stitch Source-to-target mappings, sync schedules Real-time (during sync)
Transformation dbt, Matillion, Dataform Model dependencies, column logic, tests On deployment
Warehouse Snowflake Query history, view definitions, table relationships Continuous (query execution)
BI/Consumption Tableau, Looker, Power BI Dashboard dependencies, report queries On publish

Without lineage, every change becomes a risk. You’ll manually trace dependencies, miss hidden relationships, and break downstream systems. With automated lineage, you see the full impact before making changes, communicate proactively with affected stakeholders, and maintain trust through controlled evolution of your data environment.

Step 5: How do you define and enforce data quality standards? (3-4 weeks)


Define quality as fitness for the specific purpose your business users need. Work with domain owners to create quality rules that reflect business requirements, not just technical constraints. Implement these checks using Snowflake Data Metric Functions for native execution, then monitor quality scores continuously with automated alerts when standards slip.

Poor data quality affects 84% of migrations, causing incorrect business decisions and compliance failures. Data quality problems compound during migration because you’re moving data between systems with different validation rules, schema structures, and business logic. Establish quality standards before migration, test data against these standards as you move it, and monitor quality continuously after go-live.

5.1 Define business-specific quality rules


Collaborate with domain owners to identify what “good data” means for their use cases. For the finance domain, “good” might mean transaction amounts balance to the penny and all transactions have valid account codes. For marketing, “good” might mean email addresses follow RFC 5322 format and geographic data matches ISO country codes. These rules should reflect real business impact, not arbitrary technical standards.

5.2 Implement Snowflake Data Metric Functions


Create Data Metric Functions in Snowflake to execute quality checks natively within your warehouse. These functions run automatically on schedules you define, checking for completeness (null values), accuracy (valid formats), timeliness (data freshness), and consistency (referential integrity). The checks execute close to your data, minimizing data movement and latency.

5.3 Create no-code quality rules in your metadata platform


For business users who can’t write SQL, provide a no-code interface to define quality rules. Business analysts should be able to specify “EMAIL column should match email pattern” or “ORDER_TOTAL should equal SUM of line items” without SQL knowledge. These rules compile to Data Metric Functions automatically, letting business users define standards while technical execution happens in Snowflake.

5.4 Set up real-time monitoring and alerts


Configure alerts that trigger when quality scores drop below thresholds. If the null rate in a critical column jumps from 2% to 15%, notify the data steward immediately. If daily data loads start arriving two hours late, alert the engineering team. Early detection prevents quality problems from cascading through downstream systems.

5.5 Surface quality badges in catalog and BI tools


Show quality scores directly in your data catalog and BI tools so users see data reliability before using it. A dataset with a 95% quality score and passing freshness checks earns user trust. A dataset with a 60% quality score and failing completeness tests signals caution. This transparency helps users make informed decisions about which data to rely on.

Quality Dimension Definition Example Check Implementation
Completeness Required fields contain values NULL rate < 5% in CUSTOMER_EMAIL column Data Metric Function counting nulls
Accuracy Values match expected patterns or ranges EMAIL matches RFC 5322 format Regular expression validation
Timeliness Data arrives within expected windows Daily load completes by 6 AM Freshness monitoring with alerts
Consistency Values agree across related fields STATE matches ZIP code’s state Referential integrity checks
Validity Values fall within acceptable domains COUNTRY_CODE in ISO 3166-1 list Lookup validation against standard
Uniqueness Duplicate records don’t exist where unexpected One record per CUSTOMER_ID Duplicate detection rules

Quality monitoring isn’t about achieving 100% perfection. It’s about understanding your data’s fitness for specific purposes and making that fitness visible to users. A dataset that’s 95% complete might be perfectly adequate for trend analysis but inadequate for customer outreach. Show users the data’s characteristics so they can make informed decisions.

Step 6: How do you configure security controls and encryption? (2-3 weeks)


Secure your data through multiple layers: encryption protects data at rest and in transit, masking hides sensitive values from unauthorized users, row access policies limit which records users see, network policies restrict connections, and continuous monitoring detects anomalies. Proper security configuration prevents most unauthorized access incidents.

6.1 Verify encryption is enabled


Snowflake encrypts all data automatically using AES-256 encryption. Data at rest (stored in tables) and data in transit (moving over networks) both receive encryption by default. You don’t need to configure this—it’s built in. Verify the encryption is active by checking your account configuration, then document this for compliance auditors.

6.2 Create dynamic masking policies


Build masking policies that hide sensitive data from users who shouldn’t see it. For example, create a policy on the SSN column that shows full values to HR analysts but displays only the last four digits (XXX-XX-1234) to other users. Attach these policies to PII-tagged columns so masking applies automatically without manual intervention per table.

6.3 Implement row access policies


For multi-tenant scenarios where different customer data lives in the same table, create row access policies that filter records based on user identity. A retail analyst working for Brand A sees only Brand A’s data, even though Brand B’s data exists in the same table. These policies enforce data separation without creating separate copies of tables for each tenant.

6.4 Configure network policies and IP allowlists


Restrict Snowflake access to connections from approved IP address ranges. For example, allow access only from your corporate network IP ranges, VPN addresses, and specific cloud services that legitimately need Snowflake access. Block all other connection attempts to reduce your attack surface.

6.5 Enable MFA for privileged accounts


Require multi-factor authentication for all ACCOUNTADMIN, SECURITYADMIN, and other privileged roles. MFA prevents credential theft from compromising your environment. Even if an attacker steals a privileged user’s password, they can’t log in without the second authentication factor.

6.6 Set up continuous access monitoring


Monitor access patterns continuously to detect anomalous behavior. If a user who typically queries 100 rows suddenly exports 10 million rows, trigger an alert. If access attempts come from an unusual geographic location, flag it for review. Continuous monitoring catches threats that bypass static security controls.

Control Layer Snowflake Feature Implementation Priority Configuration Time
Encryption Automatic AES-256 encryption Automatic (already enabled) None (verify only)
Masking Dynamic Data Masking policies High 1 week
Row Access Row Access Policies Medium (if multi-tenant) 1 week
Network Policies Network Policy objects, IP allowlists High 2-3 days
MFA Multi-factor authentication High 2-3 days
Monitoring Account Usage views, query history analysis Medium 1 week

Security is about defense in depth. Encryption prevents data theft if storage is compromised. Masking prevents accidental exposure to unauthorized users. Row access policies enforce data separation. Network policies block attackers before they reach your data. MFA stops credential theft. Monitoring catches attacks that slip through other controls. Layer these controls to create comprehensive protection.

Step 7: How do you validate compliance and audit readiness? (2-4 weeks)


Validate compliance before go-live by mapping regulatory requirements to specific controls you’ve implemented, verifying audit trails through Snowflake Account Usage, conducting access certification campaigns, testing policy enforcement with simulated violations, and documenting your compliance posture for auditors. Eighty-two percent of the world’s population is now protected by data privacy legislation, making compliance validation essential for any data migration.

7.1 Map requirements to controls


Create a compliance matrix that shows how each regulatory requirement maps to specific technical controls. For GDPR’s right to erasure, show how your data retention policies automatically delete data after specified periods and how your lineage tracking helps you find all instances of a person’s data for deletion requests. For HIPAA’s encryption requirements, document your Snowflake encryption configuration and masking policies for PHI.

7.2 Verify audit trail completeness


Test your audit trails by reviewing Snowflake’s Account Usage views. Verify you can answer questions like “Who accessed this sensitive table last month?”, “When was this masking policy changed?”, and “Which roles were granted to this user over time?” Auditors will ask these questions—make sure you can answer them with concrete evidence from query logs, access history, and configuration change tracking.

7.3 Conduct access certification campaigns


Have data owners review and certify that current access permissions remain appropriate. Send each domain owner a list of who has access to their data, what level of access they have, and when that access was granted. Domain owners approve continued access or request revocations. Run these campaigns quarterly initially, then move to semi-annual cadence once you’ve achieved stable access patterns.

7.4 Test policy enforcement


Simulate policy violations to verify enforcement works correctly. Have a test user without PII access try to query a PII-tagged column and confirm masking applies. Have a test user from the wrong geography try to access region-restricted data and confirm row access policies block the attempt. Document these test results as evidence that controls function as designed.

7.5 Document compliance posture for auditors


Create compliance documentation that maps your Snowflake configuration to specific regulatory requirements. Include screenshots of policy configurations, exports of audit logs showing monitoring works, and test results proving enforcement. Auditors need evidence, not promises. Give them concrete proof that your controls work.

7.6 Establish incident response procedures


Document what happens when someone reports a potential data breach or compliance violation. Who investigates? What information do you collect? How quickly do you notify affected parties? What remediation steps do you take? Having written procedures prevents chaos during actual incidents.

Regulation Key Requirements Snowflake Controls Validation Method
GDPR Data minimization, access rights, erasure, consent tracking Retention policies, access controls, lineage for erasure, audit logs Test erasure workflow, verify consent tracking
HIPAA PHI encryption, access controls, audit trails, breach notification Automatic encryption, PHI masking policies, Account Usage logs, incident procedures Verify PHI masking, test access restrictions
PCI-DSS Cardholder data encryption, access logging, network isolation Encryption, PCI tagging with masking, query logging, network policies Test that masked cards appear as XXXX-XXXX-XXXX-1234
SOC 2 Access controls, change management, monitoring, incident response RBAC, change tracking in version control, continuous monitoring, documented procedures Annual SOC 2 audit with evidence package
CCPA Consumer data access rights, deletion rights, disclosure tracking Data catalog for discovery, lineage for deletion, access logs for disclosure Test consumer data request workflow

Compliance isn’t a one-time checkbox. Regulations evolve, your data changes, and new risks emerge. Validate compliance before migration, but also establish ongoing processes to maintain compliance as your environment grows.

Common pitfalls in Snowflake migration governance

Most teams make three critical mistakes when governing Snowflake migrations. First, they treat governance as a parallel workstream that can happen while data moves, rather than a prerequisite that must finish before migration starts. Second, they over-provision role permissions during migration and never revoke excessive access. Third, they underestimate change management and assume technical implementation alone will drive adoption.

Pitfall 1: Governance as an afterthought


Teams assume they can migrate data first and add governance later. This creates technical debt that compounds fast. You’ll spend months revoking over-provisioned access, classifying unmarked sensitive data, and fixing broken trust with stakeholders who received bad data. Start with the governance framework and core policies before you migrate a single table.

Avoidance strategy: Complete Steps 1-2 (governance framework and RBAC design) before configuring Snowflake production environments. Have written policies and defined roles before you grant anyone access to production data.

Pitfall 2: Over-provisioning roles that never get revoked


During migration, teams grant broad access to speed up development. Developers get SYSADMIN, analysts get full domain access, and consultants get temporary elevated permissions. The migration completes, but nobody revokes the excessive access. Users depend on permissions they shouldn’t have, creating security risks and compliance gaps. Sixty-one percent of migration projects exceed planned timelines by 40-100%, and one driver is the time spent fixing access granted during rushed migration phases.

Avoidance strategy: Design your target RBAC hierarchy before migration. Grant migration-specific elevated permissions with documented expiration dates (30, 60, or 90 days post-go-live). Schedule access certification reviews to force permission validation before expiration dates pass.

Pitfall 3: Ignoring change management and user training


Technical teams assume that implementing governance policies automatically drives adoption. It doesn’t. Business users need to understand why governance exists, how it helps them, and how to work within its constraints. Without communication and training, users perceive governance as obstacles to bypass rather than guardrails that protect them.

Avoidance strategy: Create user-facing documentation that explains governance benefits in business terms. Train data stewards to be governance advocates within their domains, not governance police. Show users how proper governance reduces data discovery time substantially and increases confidence in analytics.

Pitfall 4: Manual processes that don’t scale


Teams manually classify data, manually approve access requests, and manually track lineage during initial migration. This works for 50 tables but breaks down at 500. Manual governance creates bottlenecks that slow down legitimate work and accuracy problems as humans make mistakes tracking thousands of data elements.

Avoidance strategy: Automate from day one. Use Snowflake Horizon’s automatic PII detection for classification. Implement workflow automation for access requests. Extract lineage automatically from query history. Design governance processes that scale with your data, not your manual effort.

How Atlan Extends Snowflake Horizon Governance for Multi-Platform Estates

The Challenge: Metadata silos across your data estate


Snowflake Horizon provides strong governance within Snowflake—RBAC controls access properly, automatic PII detection identifies sensitive data, Object Tags enforce policies programmatically, and lineage tracks transformations within your warehouse. This works well for single‑platform environments where all governed data lives in Snowflake.

But your data estate typically spans more than one platform. You’re pulling from Oracle and PostgreSQL sources, transforming in dbt, landing in Snowflake, and serving through Tableau and Power BI. Horizon governs Snowflake assets deeply, while governance in the rest of your stack often lives in separate silos, making it hard to keep policies and context consistent across systems.

Business users can’t discover data in Tableau because context doesn’t flow downstream. Data stewards can’t see lineage from source databases through transformations to BI dashboards because it’s fragmented across systems.

The challenge isn’t that Snowflake Horizon lacks capabilities. The challenge is that your governance needs extend beyond a single platform, and manually synchronizing policies across systems creates operational overhead and consistency risks.

Atlan’s Approach: Extending Snowflake Horizon across connected systems


Atlan complements Snowflake Horizon as your governance layer across the entire data estate. Through bi-directional tag sync, classifications you define in Snowflake Object Tags automatically propagate to connected systems, and tags created in external platforms sync back to Snowflake. This keeps policies consistent whether users access data in Snowflake, BI tools, or metadata catalogs.

Lineage extends from source databases through Snowflake transformations to downstream BI dashboards, giving you column-level visibility across the complete data journey. When a PostgreSQL column changes, you see which Snowflake tables, dbt models, and Tableau dashboards depend on it before making the change. Business-defined data quality rules execute as Snowflake Data Metric Functions, bringing governance logic back into Snowflake natively rather than moving data to external quality systems.

Atlan integrates deeply with Snowflake Horizon through native APIs and metadata exchange. It reads Snowflake Account Usage views for lineage and access history, syncs with Object Tags for classification alignment, and leverages Snowflake’s semantic layer through the Open Semantic Interchange partnership.

Atlan, Snowflake’s 2025 Data Governance Data Cloud Product Partner of the Year and a launch partner for Snowflake Intelligence, enables AI agents to work with governed data across platforms.

As part of the Snowflake Horizon Partner Ecosystem, Atlan acts as a universal control plane that extends Horizon’s native governance, bi‑directional tags, policies, and lineage, across 100+ connected systems while keeping Horizon as the authoritative catalog and policy engine inside Snowflake.

The Outcome: Faster implementation with broader coverage


Organizations using Atlan alongside Snowflake Horizon see materially faster governance setup compared to manually coordinating policies across platforms. Business users report higher satisfaction because they can discover and trust data regardless of which tool they’re using. Companies like Contentsquare make Atlan their “home for every KPI and dashboard,” while Workday uses governed data products to enable AI adoption confidently.

The combination lets you start with Snowflake Horizon’s strong native governance and extend it when your needs grow beyond a single platform.

For organizations running multi-cloud data estates, dozens of transformation pipelines, and governance programs serving hundreds or thousands of business users, this extension provides the consistency and scale that single-platform tools can’t achieve alone.

See how Atlan complements Snowflake Horizon to extend governance across your connected systems.

See Why Atlan Is a Leader — Book a Demo →

FAQ

What is data governance in the context of Snowflake migration?


Data governance for Snowflake migration means establishing who owns data decisions, which policies apply during and after migration, and how those policies get enforced automatically in Snowflake. It defines decision rights (who approves access requests), accountability (who’s responsible when something goes wrong), and policy enforcement (how classification, masking, and access controls work). Without governance, you get access chaos, compliance gaps, and broken stakeholder trust. With governance, you ensure trusted data from day one.

How long does it take to implement governance for a Snowflake migration?


Plan for 6-12 months depending on organization size and existing governance maturity. This breaks down into framework setup and stakeholder alignment (2-3 months), technical configuration of RBAC, classification, and lineage (2-3 months), validation and training (2-3 months), and ongoing refinement (continuous). Organizations with executive sponsorship complete implementations faster than teams without C-level support. Starting with clearly defined policies accelerates technical implementation significantly.

Do I need a universal catalog if Snowflake Horizon provides governance?


Snowflake Horizon Catalog provides strong native governance including RBAC, automatic PII detection, Object Tags, masking, and lineage within Snowflake. For single-platform environments where all data lives in one Snowflake workspace, native Horizon may be sufficient. Add a universal catalog when your data estate spans multiple systems (cloud platforms, BI tools, ETL pipelines) and you need cross-platform lineage, consistent tag propagation, or business user adoption beyond technical teams. Universal catalogs extend governance, not replace it.

What’s the difference between data governance and data management?


Governance defines policies and decision rights, while management executes operational tasks. For example, governance defines who can access sensitive data (policy decision), while management implements that through RBAC configuration (technical execution). Governance answers “why” and “who decides,” management answers “how” and “who executes.” Both are essential. Governance without management creates policies nobody follows. Management without governance creates efficient execution of unclear objectives.

Can I implement governance after migration is complete?


Technically yes, but substantially more expensive and risky. Post-migration governance means fixing problems in production including revoking over-provisioned access, classifying unmarked sensitive data, and documenting undocumented policies. Organizations attempting post-migration governance spend considerably more than those who govern upfront. Governance debt compounds fast because users build dependencies on ungoverned data, making it harder to impose structure later. The right time to govern is before you migrate data.

What are the most common governance failures during migration?


Three primary failures cause most problems. First, undefined data ownership creates access chaos because nobody has authority to approve or deny access requests. Second, inconsistent classification creates compliance gaps because sensitive data spreads untagged across your environment. Third, missing lineage prevents impact analysis, so changes break downstream systems unexpectedly. All three failures stem from treating governance as a parallel workstream rather than migration prerequisite. Most migrations fail when governance isn’t prioritized upfront.

How do you handle governance in a phased Snowflake migration?


Establish your governance framework globally before migrating any domain, then apply controls incrementally per phase. Configure core RBAC roles and classification schema before the first domain migrates, then extend policies to each new domain as it arrives in Snowflake. This prevents policy fragmentation while allowing phased execution. The key is maintaining a consistent governance model even though migration happens in phases. Don’t create different policies for different migration waves.

What role does automated lineage play in migration governance?


Lineage tracks data movement from sources through transformation to consumption. During migration, lineage answers critical questions like “What breaks if I change this source system?” and “Which reports depend on this Snowflake table?” Without automated lineage, teams manually document dependencies and miss most relationships, causing production breakage post-migration. Automated lineage extraction from query history captures dependencies continuously without manual effort, giving you complete impact visibility.

How does Atlan integrate with Snowflake Horizon?


Atlan complements Snowflake Horizon through native bi-directional tag sync where classifications flow both ways automatically. Atlan reads Snowflake Account Usage for lineage and access history, extending visibility beyond Snowflake to sources, ETL tools, and BI platforms. Data Quality Studio executes checks as Snowflake Data Metric Functions, bringing governance logic into Snowflake natively. Result: Horizon governs within Snowflake, Atlan extends that governance across your connected systems. Atlan is Snowflake’s 2025 Data Governance Partner of the Year.

What metrics should I track to measure governance effectiveness?


Track four metrics consistently. First, policy compliance rate measures the percentage of data properly classified and tagged. Second, access certification velocity measures time to review and approve access requests. Third, incident response time measures how quickly you detect and remediate policy violations. Fourth, data user satisfaction measures self-service success rates. Baseline these metrics before migration, track monthly after go-live, and use trends to identify governance gaps requiring attention.

How do I get executive buy-in for governance investment?


Frame governance as migration insurance, not overhead cost. Investing 15-20% of migration budget in governance reduces failure risk substantially and prevents costly post-launch remediation. Present the cost comparison showing upfront governance investment versus remediation costs when governance fails. Emphasize that governance enables faster time-to-value by reducing rework, not slower delivery. Executives understand risk mitigation better than abstract compliance arguments. Show them the business case in financial terms.

What’s the first governance step if we’re already mid-migration?


Pause and assess your governance debt immediately. Document three things: current access controls showing who has what permissions, sensitive data locations identifying where PII and PHI exist, and policy gaps listing what’s missing. Then prioritize fixes strategically. Address access over-provisioning first because it creates immediate security risk. Classify sensitive data second to close compliance gaps. Establish ownership third for long-term sustainability. Don’t try fixing everything simultaneously.

Does Snowflake Horizon provide end-to-end lineage for migrations?


Snowflake Horizon provides robust lineage within Snowflake workspaces, tracking transformations, queries, and dependencies effectively. However, lineage coverage is limited for external source systems and federated multi-workspace architectures. For migrations involving multiple source databases, ETL tools, and downstream BI platforms, extend lineage tracking across the complete data journey from source to consumption. This gives you full impact visibility when source systems change or transformation logic updates.

What happens after you complete this governance checklist?

Following this 7-step checklist before migrating to Snowflake reduces failure rates from over 80% to under 30%. You’ll have trusted data from day one, clear ownership preventing access chaos, automated controls catching problems before they reach production, and audit-ready systems passing compliance reviews without scrambling.

The effort you invest in governance now pays dividends for years. Trusted data enables faster analytics because users don’t question accuracy. Clear policies enable confident AI adoption because you know which data is safe to use. Strong foundations enable scale because governance processes work at 5,000 tables the same way they work at 500.

Organizations using Atlan alongside Snowflake complete governance setup faster while improving data user satisfaction substantially. The combination of Snowflake Horizon’s strong native capabilities and extended governance across connected systems gives you both depth within Snowflake and breadth across your data estate.

Your migration success starts with governance. Complete these seven steps, and you’ll build the foundation for reliable data that drives business value for years to come.

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Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business's disparate data infrastructure, cataloging and enriching data with business context and security.

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