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Data warehouses, BI and AI
Data your teams trust, and AI that uses it
Data warehouses and lakehouse platforms, management cockpits and live sales reporting, forecasting and data science. Built on retail know-how, so the numbers mean the same thing in every department.
| Chain, all channels | 98.4% of plan | +6.1% LY |
| Stores, Poland | 102.7% of plan | +8.3% LY |
| E-commerce | 96.2% of plan | +4.9% LY |
| Stores, Czechia | 88.5% of plan | -2.4% LY |
Sales, traffic and conversion from every point of sale, with drill-down to store, concept, country and channel.
Three ways to start
Pick the entry point that matches where your organisation is today.
BI as a service
For smaller organisations that want analysis without a large upfront investment. Short project, fast deployment on the Microsoft stack with Power BI, built on best practices from leading retailers.
Data transformation
For companies with an existing warehouse and data quality, governance or distribution problems. New technology and processes, without losing the knowledge built over years.
New data platform
For larger organisations starting advanced analytics. Discovery, integration of many sources, automated cleaning and processing.
Data warehouse and platform
One source of truth for retail data
We built the data warehouse foundations for one of Europe's largest footwear retailers: data model, integration architecture, ETL, OLAP cubes, data marts and cockpit templates. Today we build the same on modern lakehouse platforms.
- Sources: ERP, POS, e-commerce, PIM and PLM, CRM, marketing, flat files
- Landing, curated and consumer layers with data quality rules
- Business data models built with users (BEAM) so KPIs have one definition
- Data organised by domain, each with an owner, as a product
- Self-service SQL and models for analysts, CI/CD and monitoring for IT
- Migration to the cloud and optimisation of existing reports
- Business requirements and logical model
- Source systems mapping
- Physical data model
- ETL and ELT design
- Platform development
- Data quality checks
- User acceptance, training, go-live
- Documentation and stabilisation
Roadmap first
14 workshop days across 15 reporting areas, from sales, product and allocation to logistics, HR and marketing, with priorities and a roadmap.
Cockpits and reports people use
Management dashboards and operational reports designed with the people who use them every day.
Live sales
Sales every 15 minutes from every store and channel. Traffic, conversion, rankings and drill-down. We designed and built this system for a chain of over 1,000 stores.
Sales and plans
Plan realisation for sales and margin, cumulative and against last year, with the source of each gap.
Product performance
Sales leaders and laggards globally and per category, with dynamic KPIs and rankings.
Stock, logistics, HR
Inventory, allocation, claims, transport and returns, staff hours per store and labour planning.
Data science and AI for retail
Models that answer real merchandising questions, connected to your own sales, stock and product data and to the planning processes that act on them.
Demand forecasts
Automated forecast models and on-demand predictions for planning, allocation and replenishment.
Price optimisation
Elasticity, markdown and promotion models that feed price decisions and budgets.
Product similarity
New products matched to similar past products from text, attributes and images, to forecast them before they sell.
Recommendations
Recommendation engines, personalised marketing and consumer insights.
Order and quality risk
Prediction of problematic orders, late deliveries and samples likely to be rejected.
Assistants and content
Assistants that answer questions on your data in plain language, and generated product descriptions and attributes reviewed before publishing.
Case study · eobuwie.pl and MODIVO
One version of the numbers for a European e-commerce leader
Clothes, footwear and accessories sold in 19 European countries, with hundreds of top brands.
- No consistent indicators
- Manual reports out of control
- Many sources and levels of data
- Errors and inconsistent naming
- Late reports
- Automated data loading
- Standard indicators company-wide
- Trust in data and stable reporting
- Fewer manual reports
- Management dashboards