MINT Elevates Media Data Architecture with Snowflake: A Guide for Platform Users

What Is MINT's New Data Warehouse Architecture?

MINT's ARM platform now runs on an advanced data warehouse architecture built on Snowflake, specifically designed to handle campaign and channel performance data at scale. This architecture addresses the core challenges marketing teams face: slow query performance, inconsistent data across channels, delayed reporting, and difficulty scaling with growing campaign volumes.

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Why Snowflake to upgrade MINT’s Data Architecture?

When it comes to handling the scale and complexity of modern marketing and advertising data, Snowflake stands out from traditional database solutions. Snowflake's cloud-native architecture supports MINT ARM platform with:

• Cross-cloud compatibility with existing marketing technology stacks.

• Continuously refreshed data sharing across departments and external partners (updated multiple times daily).

• Enterprise security meeting the highest compliance standards.

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How Does the New MINT’s Architecture Improve Campaign Data Performance?

When media planners and operations teams need to track pacing or optimize spend, slow systems often get in the way. Let's be honest: in campaign management, every minute counts. The last thing you want is to sit around waiting for systems to catch up. Our new architecture removes those barriers.

• Reduced latency in reconciling data across multiple campaign and channel sources.

• Faster queries thanks to optimized star-schema models tuned for media reporting.

• Scalability to handle spikes in campaign data, ad impressions, and multi-channel performance metrics.

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What Campaign Data Sources Does our Architecture Support?

Our centralized data core integrates data from:

• Paid search campaigns (Google Ads, Microsoft Advertising, etc..)

• Social media advertising (Facebook, Instagram, LinkedIn, TikTok, etc..)

• Programmatic advertising platforms

• Connected TV and streaming advertising

• Email marketing platforms

• Affiliate marketing networks

• Direct publisher relationships

• Offline media tracking systems

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What Data Quality Measures Are Built into the System?

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I. Automated Data Validation

Nobody wants to make decisions based on questionable data, which is why quality control is baked into every layer. Great Expectations framework continuously validates:

• Spend data accuracy and completeness

• Impression volume consistency

• Click-through rate reasonableness

• Conversion tracking integrity

• Attribution model consistency

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II. Master Data Management (MDM)

Consistency across all your data sources isn't just nice to have: it's essential for accurate reporting. The system maintains:

• Standardized channel identifiers across all data sources

• Consistent campaign metadata including naming conventions

• Unified customer segmentation across channels

• Standardized KPI definitions preventing metric confusion

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III. Error Handling and Data Quality

When something goes wrong with your data, you want to know about it immediately, not weeks later when you're questioning your results. Our updated system now includes:

• Automated anomaly detection flags unusual spending or performance patterns

• Data lineage tracking shows exactly where each metric originates

• Version control for all data transformations and business rules

• Rollback capabilities to revert problematic data updates

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How Does the new MINT’s Architecture Scale with Marketing Needs?

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I. Modular Scaling Approach

As your marketing programs grow, your data infrastructure should grow with you seamlessly. The system scales three components independently:

1. Data Ingestion: Handle more campaign data sources without affecting performance

2. Data Transformation: Process more complex attribution models and KPI calculations

3. Data Storage: Accommodate historical data retention requirements

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II. Deployment and Updates

We know downtime during critical campaign periods can be costly, so we've eliminated it entirely. Our approach includes:

• Blue-green deployments ensure zero downtime during system updates

• Feature flags allow testing new capabilities with specific user groups

• dbt model versioning enables safe updates to data transformation logic

• Automated testing validates all changes before production deployment

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III. Flexibility for New Requirements

The marketing landscape changes fast, and your data architecture needs to keep pace. Our new data warehouse architecture supports:

• New marketing channels through configurable data connectors

• Custom KPI definitions via dbt model extensions

• Changed attribution models without historical data reprocessing

• Additional user access patterns through role-based permissions

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How Is Governance and Security Strengthened?

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I. Department-Level Data Access

Different teams need different data, and they shouldn't have to see everything to get what they need. Access can be easily organized by role, for example:

• Marketing teams access campaign performance, spend data, lead attribution and conversion metrics;

• Finance teams access budget and cost reconciliation data;

• Executive teams access summary dashboards and global or regional ROI reporting according to specific needs.

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II. Security and Compliance Features

With marketing budgets and sensitive data at stake, security can't be an afterthought. Our system includes enterprise-grade protection:

• Apache Ranger enforces consistent access policies across all datasets

• Encryption at rest and in transit protects sensitive financial data

• Audit trail logging tracks all data access and modifications

• GDPR compliance features for customer data handling

• SOC 2 Type II compliance for enterprise security requirements

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What’s Different for Teams Using Campaign Data?

For all campaign managers, media buyers, and operations teams working daily on MINT ARM, immediate benefits include reduction in time to generate cross-channel performance reports, faster campaign optimization decision-making, improvement in data accuracy across all marketing channels, and reduction in IT support requests for campaign data issues.  

• User-friendly dashboards simplify access to spend, pacing, and performance reports.

• Consistent data sources ensure alignment across departments and partners.

• Self-service tools remove bottlenecks, letting teams optimize campaigns independently.

• Training and support help teams adopt new processes quickly.

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How to Get Started with MINT’s New Data Architecture

Getting the most value from any new system depends on how well your team can use it. We've designed the rollout process to minimize disruption while maximizing value as quickly as possible. MINT provides:

• Hands-on training for media managers, ad operations specialists and marketing data analysts;  

• Best practices documentation for campaign data analysis;

• Always-on technical support;

• Regular updates on new features and capabilities

This architecture transformation represents a fundamental shift from reactive reporting to proactive campaign optimization, enabling marketing teams to make data-driven decisions faster and more accurately than ever before. The result is a marketing data infrastructure that doesn't just support your current needs but anticipates and adapts to whatever comes next.

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