Decision Speed — Decision
making velocity accelerated 5x with real - time dashboard feeds
Data & Analytics
Enterprise Retail Data Warehouse & Executive BI Suite
Centralized Snowflake Data Warehouse & Real-Time Analytics Dashboard Powering C-Level Retail Intelligence
We consolidated fragmented multi-channel retail and marketplace data into a centralized Snowflake data warehouse, powering an executive BI dashboard with predictive sales modeling and anomaly detection.
Retail Intelligence8 monthsTeam: 12 Engineers$180,000Completed & Deployed
Technology
Snowflake Data Clouddbt (data build tool)Apache AirflowReact / TypeScriptRecharts & D3.jsPython Data PipelinesAWS S3 Data Lake
Platform
Executive ConsoleMobile BI AppSnowflake Cloud
500TB+
dataVolume
<1.2s
queryLatency
320%
analyticsRoi
45+
dataFeeds
5x Faster
decisionSpeed
100%
accuracyRate
Overview
Project Overview
For a large retail brand selling across physical stores, direct Shopify storefronts, and multiple Amazon marketplaces, decision-making was stalled by disconnected data silos and slow manual reporting. Ctas Info Services architected a unified enterprise data warehouse built on Snowflake and dbt. Processing data from 45+ sources in real time, the solution feeds a custom React BI executive dashboard featuring predictive sales models, profit margin heatmaps, and instant anomaly alerts—reducing query speeds from 30 seconds to under 1.2 seconds and driving a 320% ROI on analytics.
Client Context
A major retail enterprise operating physical retail chains, direct-to-consumer storefronts, and online marketplace channels.
Consolidated 500TB+ of raw retail and marketplace data into a single authoritative source of truth
Reduced executive report query execution times from 30+ seconds to under 1.2 seconds
Delivered a 320% ROI on analytics investment by identifying hidden margin leaks and excess inventory
Challenges
Key Challenges
Obstacles we identified and addressed during the project.
- 1Consolidating fragmented data feeds from 45+ sources (ERP, WMS, POS, Amazon SP-API, Shopify, Google Ads, Stripe)
- 2Eliminating slow 30+ second query load times on executive dashboards containing billions of row records
- 3Guaranteeing 100% data integrity, automated anomaly detection, and zero discrepancy between financial feeds
- 4Delivering executive-ready mobile and tablet responsive dashboards with sub-2 second query execution
Solutions
Our Solutions
Technical and strategic approaches that resolved each challenge.
- Architected a modern data stack centered around Snowflake Data Cloud with modular dbt data transformations
- Built automated Apache Airflow DAGs for reliable hourly ETL/ELT ingestion and data pipeline monitoring
- Implemented pre-aggregated incremental materializations and dimensional star-schema data modeling
- Developed a custom lightweight React executive console with server-side caching and streaming chart updates
Highlights
Project Highlights
Key features and achievements delivered.
1
Consolidated 500TB+ of raw retail and marketplace data into a single authoritative source of truth
2
Reduced executive report query execution times from 30+ seconds to under 1.2 seconds
3
Delivered a 320% ROI on analytics investment by identifying hidden margin leaks and excess inventory
4
Enabled executive team to make data-backed merchandising and inventory decisions 5x faster
5
Implemented automated anomaly alerts notifying leadership of sudden revenue shifts or fee spikes
Goals
Strategic Objectives
Strategic objectives that guided the project.
Eliminate reporting lag and manual spreadsheet aggregation across retail operations
Uncover hidden revenue metrics, category profit margins, and customer lifetime value (LTV)
Empower executives with real-time predictive analytics accessible on desktop, tablet, and mobile
Strategy
Implementation Approach
Implementation approach and technical decisions.
Outcomes
Achieved Results
Measurable results and business impact.
Margin Optimization — Identified and eliminated $280,000 in annual inventory carrying cost leaks
Data Integrity — Achieved 100% financial data reconciliation between sales channels and ERP
Client
Our Client
Who we built this for.
DataMetrics Retail Analytics
Retail Analytics & Merchandising · Enterprise ($120M+ Retail Sales) · Boston, USA
A major retail enterprise operating physical retail chains, direct-to-consumer storefronts, and online marketplace channels.
Client Requirements
- 1
Centralized data warehouse handling 500TB+ of structured and un-structured retail data
- 2
Sub-2 second query response times on executive tablet and web dashboards
- 3
Automated hourly data ingestion with 100% data financial reconciliation
Solution
Proposed Solution
Our approach and rationale.
Our Approach
A modern data stack centered around Snowflake for cloud data warehousing, dbt for SQL data transformation pipelines, Apache Airflow for DAG workflow orchestration, and a custom React BI frontend utilizing D3.js and Recharts.
Why We Choose This Solution?
- Separation of compute and storage in Snowflake allows unlimited scaling without skyrocketing costs
- dbt transformations ensure modular, version-controlled, and fully documented data pipelines
- Proven track record in retail data architecture and executive dashboard development
Benefit of This Solution
A centralized data warehouse and executive BI suite that turns complex raw retail data into real-time, high-margin strategic growth insights.
Features
Key Features
Core platform capabilities delivered.
Unified Data Lake & Warehouse
Single source of truth integrating ERP, WMS, e-commerce marketplaces, and ad channels.
Sub-Second Executive Dashboards
Lightning-fast BI console with custom KPI tiles, revenue heatmaps, and trend charts.
Predictive Inventory & Demand Modeling
Machine learning models forecasting seasonal inventory needs and safety stock levels.
Automated Anomaly Detection
Instant alerts for sudden revenue drops, unexpected fee spikes, or stockout risks.
Role-Based Access & Data Governance
Granular permissions ensuring sensitive financial data is restricted by executive role.
Automated ETL Pipeline Monitoring
Apache Airflow DAGs with automatic error recovery and data quality verification.
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