Your data lives in 12 systems.
None of them agree.
POS says one thing. Inventory says another. Finance reconciles manually every month. Until you have a unified data layer, every AI initiative is built on sand. We design the architecture that makes your data AI-ready.
You can’t run AI on data that contradicts itself.
Each system has its own schema, its own update cadence, its own definition of what a “store” or a “transaction” even means.
- POS data lives in one vendor’s cloud
- Inventory tracked in a legacy WMS
- Financial reporting runs through an ERP implemented eight years ago
- Marketing has its own analytics stack nobody else can access
- Labor scheduling is a standalone tool with no API
A data lake isn’t a nice-to-have. It’s the prerequisite for everything else.
What happens without a unified layer
- “What’s our margin by category by region?” takes two weeks to answer
- Demand forecasting trained on inconsistent data produces inconsistent forecasts
- Shrinkage detection can’t cross-reference POS and inventory in real time
- AI doesn’t fix fragmented data. It amplifies the problem.
One intelligence layer. Every system feeding into it.
We design the full data architecture from ingestion to transformation to serving, based on your actual systems and use cases.
- Real-time ingestion where it matters (POS, inventory)
- Batch processing where it doesn’t (finance, marketing)
- Validation rules and anomaly detection from day one
- Platform-agnostic: Snowflake, BigQuery, Databricks, Redshift
Unified data model
One definition of a store. One definition of a transaction. One definition of a SKU. No more reconciliation spreadsheets.
Ingestion pipelines
ETL or ELT, batch or streaming. We spec the tooling and help you evaluate vendors based on your actual data volumes.
Data quality & governance
Validation rules, monitoring alerts, and quality scoring built into the architecture. You’ll know when a feed breaks before your forecasts do.
Cloud architecture
We recommend based on your existing cloud footprint, team skills, and budget. No vendor lock-in by default.
Architecture you can hand to your engineering team tomorrow.
We don’t deliver theory. You get detailed technical specifications, architecture diagrams, vendor comparisons, and a phased implementation plan.
- Entity definitions, relationships, and normalization rules
- Pipeline design with vendor evaluation and cost estimates
- Quality rules, access controls, and compliance considerations
- Projected storage, compute, and ingestion costs at your data scale
Unified data model specification
Entity definitions, relationships, and normalization rules across all systems.
Pipeline architecture & tooling
ETL/ELT design with vendor evaluation, cost estimates, and implementation sequence.
Data governance framework
Quality rules, monitoring alerts, access controls, and compliance considerations.
Cloud architecture & cost model
Platform recommendation with projected storage, compute, and costs at your scale.
We built data pipelines for 850+ stores before we advised anyone else.
Ward’s own platform ingests data from POS, inventory, ERP, marketing, and finance systems across hundreds of retail locations.
- We learned data architecture by building pipelines that had to work at 2 AM
- We account for the POS system that drops records
- The ERP that only exports CSV
- The WMS that hasn’t had an API update since 2019
- Your architecture will be built for the systems you actually have
Your data should work together.
One unified data layer. Every system feeding into it. Designed for the AI workloads you need tomorrow.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches — and the ones your current tools miss.