Philosophy
This page provides an outline of the general Daasity Data Model, both our Unified Schemas and Data Marts, why we designed the data model this way and how our transformation layer works
The Daasity Philosophy
Our Mission: Turn Your Data Into Decisions
At Daasity, we believe that every commerce brand deserves enterprise-level analytics without enterprise-level complexity. Our philosophy is built on three core principles that guide everything we do:
1. Unified Truth - One source of truth across all your channels 2. Future-Proof Architecture - Built to adapt as your business evolves 3. Actionable Intelligence - Data that drives decisions, not just dashboards
🎯 Why Daasity Exists
The Problem We Solve
Modern commerce brands operate across multiple channels, platforms, and systems. Your data lives in dozens of places:
E-commerce platforms (Shopify, BigCommerce, Magento)
Marketplaces (Amazon, Walmart, Target)
Marketing channels (Meta, Google, TikTok, Email)
ERP and inventory systems
Customer service platforms
The Challenge: Each system speaks its own language, uses different metrics, and tells a different story. Teams waste countless hours reconciling data, building fragile reports, and arguing about which numbers are "right."
Our Solution
Daasity creates a Unified Commerce Data Platform that:
Automatically ingests data from 50+ sources
Normalizes it into a consistent, reliable format
Delivers insights through intuitive dashboards and reports
Activates data directly in your marketing channels
The Result: Your team spends time acting on insights, not searching for them.
🏗️ The Daasity Data Architecture
Philosophy: Build Once, Use Forever
Our data model is designed with a fundamental philosophy: "Change is inevitable, rebuilding is not."
When platforms update their APIs, when you add new sales channels, or when your business model evolves, your analytics shouldn't break. That's why we built a three-layer architecture that isolates changes and protects your reporting.
The Three-Layer Model
Layer 1: Extractor Schemas
Raw data, exactly as it comes from the source
The extractor layer creates an exact replica of your source data in our data warehouse. This means:
No data loss - We capture everything, even fields you don't use today
Full history - Historical data is preserved even if the source changes
Debugging capability - You can always trace back to the original data
API independence - When APIs change, only this layer needs updating
Example: Your Shopify data lands here exactly as Shopify structures it, with all custom fields, metafields, and platform-specific attributes intact.
Layer 2: Unified Schemas (Normalization)
The magic layer where everything becomes consistent
This is the heart of Daasity's innovation. We transform disparate data sources into unified models that represent universal business concepts:
Core Unified Schemas:
Unified Order Schema (UOS) - Every order from every channel in one consistent format
Unified Customer Schema (UCS) - Single customer view across all touchpoints
Unified Product Schema (UPS) - Consistent product catalog across channels
Unified Marketing Schema (UMS) - Normalized marketing data across all platforms
Why This Matters:
Platform agnostic - A Shopify order and an Amazon order look the same
Future-proof - Built to handle capabilities platforms don't even have yet
Consistent metrics - Revenue is calculated the same way everywhere
Multi-everything ready - Multi-warehouse, multi-currency, multi-brand support
Real-World Example: Our Unified Order Schema supports multi-shipment/multi-recipient orders even though most platforms don't. When Shopify adds this feature (or when you switch to a platform that has it), your reports don't change—we just update the transformation logic.
Layer 3: Data Marts & Reporting Schemas
Optimized for analysis and decision-making
The reporting layer transforms unified data into purpose-built data marts optimized for specific business questions:
Specialized Data Marts:
Executive Data Mart - High-level KPIs and trends
Marketing Analytics Mart - Attribution, CAC, LTV, channel performance
Operations Mart - Inventory, fulfillment, supply chain metrics
Customer Intelligence Mart - Segmentation, cohorts, behavior analysis
Product Performance Mart - SKU analytics, category trends, pricing
Design Principles:
User-specific views - Marketers see marketing metrics, ops sees operations
Pre-calculated metrics - Complex calculations happen once, not every query
Business logic layer - Your custom rules and definitions live here
Self-service ready - Users can explore without breaking anything
💡 Core Design Principles
1. Resilience Through Isolation
Changes in source systems affect only the extractor layer. Your reports and dashboards remain stable even when platforms update their APIs or data structures.
2. Semantic Consistency
We maintain consistent definitions across all data sources. "Revenue" means the same thing whether it comes from Shopify, Amazon, or your ERP.
3. Incremental Complexity
Start simple with pre-built templates, then gradually customize as your needs grow. You don't need to understand the entire model to get value.
4. Extensibility by Design
Every component is built to be extended:
Add custom fields to any schema
Create calculated metrics specific to your business
Build custom data marts for unique use cases
Integrate proprietary data sources
5. Performance at Scale
Columnar storage for fast analytical queries
Incremental processing to minimize compute costs
Smart caching for frequently accessed data
Parallel processing for large datasets
🔄 The Transformation Philosophy
ELT Over ETL
We deliberately chose an ELT (Extract, Load, Transform) approach over traditional ETL:
Why ELT?
Preserve raw data - Never lose information in transformation
Flexible transformations - Change business logic without re-extracting
SQL/Python based - Use familiar tools, not proprietary languages
Version control - Track changes to transformation logic
Testing friendly - Validate transformations before deploying
Transformation Rules
Our transformations follow strict principles:
Idempotent - Running twice produces the same result
Auditable - Every transformation is logged and traceable
Reversible - Can rebuild from raw data at any time
Testable - Automated tests ensure quality
Documented - Clear documentation for every transformation
🚀 Practical Benefits
For Business Users
Before Daasity:
"Which report has the right numbers?"
"Why don't these totals match?"
"Can we add Instagram data to this report?"
"The dashboard broke when we upgraded Shopify"
With Daasity:
✅ Single source of truth everyone trusts
✅ Consistent metrics across all reports
✅ New channels integrate seamlessly
✅ Reports that don't break with platform changes
For Technical Teams
Before Daasity:
Maintaining brittle ETL pipelines
Rebuilding reports for each new data source
Dealing with API changes and breaking integrations
Managing complex transformation logic
With Daasity:
✅ Managed pipelines with 99.9% uptime
✅ Unified schemas that work across sources
✅ API changes handled by Daasity team
✅ SQL-based transformations you can customize
For Growing Brands
Starting Out:
Use pre-built templates and standard metrics
Focus on key KPIs without complexity
Get insights in days, not months
Scaling Up:
Add new channels without rebuilding
Customize metrics for your business model
Create team-specific dashboards
Maintain historical continuity
Enterprise Level:
Multi-brand/multi-region support
Custom data marts for unique needs
API access for embedded analytics
Advanced ML/AI capabilities
📚 Learning More
Technical Deep Dives
[Unified Schemas Documentation] - Detailed schema specifications
[Data Marts Guide] - Understanding our reporting layer
[Transformation Logic] - How we process your data
[API Reference] - For custom integrations
Business Resources
[ROI Calculator] - Quantify the value of unified data
[Implementation Guide] - Get up and running quickly
[Best Practices] - Learn from successful customers
[Case Studies] - Real-world success stories
🤝 Our Commitment
To Your Data
Security First - SOC 2 Type II certified, GDPR compliant
Privacy Protected - Your data is never shared or sold
Always Accessible - Export your data anytime
Fully Auditable - Complete transformation lineage
To Your Success
White-Glove Onboarding - We help you get set up right
Continuous Innovation - Regular updates and new features
Community Driven - Your feedback shapes our roadmap
Success Partnership - Your growth is our growth
💬 Philosophy in Practice
"We used to spend 20 hours a week just preparing reports. Now we spend that time acting on insights. Daasity didn't just give us better data—it gave us our time back." - Head of Analytics, $50M DTC Brand
"When we added Amazon as a channel, our existing reports just worked. No rebuilding, no consultants, no delays. That's when I understood the power of Daasity's approach." - CFO, Multi-Channel Retailer
"The unified schema concept seemed complex at first, but once I saw how it protected us from platform changes, I was sold. We've upgraded Shopify twice and never lost a single report." - Data Engineer, Fashion Brand
🚀 Ready to Experience the Daasity Difference?
Understanding our philosophy is just the beginning. See it in action:
[Schedule a Demo] | [Start Free Trial] | [Technical Architecture Review]
Frequently Asked Questions
Q: How is this different from a traditional data warehouse? A: Traditional warehouses just store data. Daasity provides the entire ecosystem: ingestion, normalization, transformation, visualization, and activation—all maintained and updated for you.
Q: What if I have custom data sources? A: Our unified schema approach extends to custom sources. We'll help you map your proprietary data into the unified model.
Q: Can I write my own SQL? A: Absolutely. You have full SQL access to create custom reports, metrics, and even entire data marts.
Q: How do you handle data quality? A: Multiple layers of validation, automated testing, and anomaly detection ensure data quality at every step.
Next Steps: [Platform Overview] → See how it all works together [Getting Started] → Begin your Daasity journey [Technical Docs] → Dive deep into the architecture
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