One warehouse for all your data, not one spreadsheet per department.
Your operational data is scattered across ERP, WMS, CRM, MES, spreadsheets, and shared drives. Every report requires someone to pull data from 3–4 systems and reconcile it manually. We centralize everything into a cloud data warehouse with automated pipelines so your analytics run on a single, consistent source of truth.
Data Trapped in Silos That Don’t Talk to Each Other
- →Monthly reports require manual data exports from ERP, WMS, and CRM — then hours of reconciliation in Excel
- →Finance, operations, and sales each have their own version of "revenue" and "inventory" because they pull from different sources
- →No historical data in a queryable format — trend analysis means digging through archived spreadsheets
- →Data freshness measured in days or weeks because ETL processes are manual or broken
Data Warehouse & Integration
Cloud Data Warehouse
Deploy Snowflake, BigQuery, or Azure Synapse as your central analytics warehouse. Schema designed for manufacturing data models — orders, inventory, production, quality, and financials.
ERP Data Integration
Automated extraction from Odoo and legacy ERP systems via Python pipelines. Transaction data, master data, and configuration data pulled on schedule or in near-real-time.
WMS & MES Integration
Warehouse transactions, production completions, quality records, and shop-floor data integrated alongside ERP data. The warehouse sees the full operational picture.
Automated ETL Pipelines
Scheduled and event-driven data pipelines that extract, transform, and load data from source systems. Built-in data quality checks, deduplication, and standardization at every stage.
Data Quality Layer
Validation rules, anomaly detection, and data quality scoring applied during ingestion. Bad data is flagged and quarantined — not loaded into the warehouse to corrupt downstream reports.
Semantic Layer & Data Models
Business-friendly data models that define "revenue," "inventory," "on-time delivery," and other metrics once. Every dashboard and report uses the same definitions — no more conflicting numbers.
The Engagement Process
Source System Inventory
Catalog every data source, document data volumes, update frequencies, and access methods. Map the data flows that need to converge in the warehouse.
Schema & Model Design
Design the warehouse schema and semantic models based on your analytics requirements. Define dimensions, facts, and business metric calculations with stakeholder sign-off.
Pipeline Development
Build ETL/ELT pipelines for each source system. Implement data quality checks, transformation logic, and incremental refresh strategies.
Validation & Go-Live
Validate warehouse data against source systems. Reconcile counts, totals, and key metrics. Go live when data accuracy meets defined thresholds.
Monitoring & Maintenance
Deploy pipeline monitoring, data freshness alerts, and quality dashboards. Ongoing maintenance as source systems change or new data sources are added.
Frequently Asked Questions
Data Analytics · Data Warehouse
Every engagement starts with an assessment.
We scope work after we understand your operation — not before. The Launchpad assessment maps where you are, quantifies what it's costing you, and sequences what to do first.
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