Industries
Data Expertise Across Industries
Sector detail
Common problems, and where we focus
Datalloy does not claim named clients in these sectors. The points below describe patterns the work repeatedly deals with.
Manufacturing
Production, quality and supply data usually sit in separate systems with different part and plant codes.
Typical challenges
- Plant-level systems reporting on different definitions
- Production and ERP data reconciled by hand
- No single view of yield, scrap or downtime
Where Datalloy focuses
- Conformed product, plant and time dimensions
- Production, inventory and quality fact tables
- Operational dashboards for plant and group level
Finance
Financial reporting demands traceable numbers, controlled definitions and a history that does not change retrospectively.
Typical challenges
- Month-end reporting assembled from spreadsheets
- Ledger, sub-ledger and operational data disconnected
- Restatements with no audit trail
Where Datalloy focuses
- Auditable transformation logic and lineage
- Slowly Changing Dimensions for structure changes
- Governed measure definitions for reported figures
Healthcare
Clinical, administrative and operational systems rarely share identifiers, and access has to be controlled at row level.
Typical challenges
- Patient and activity data split across systems
- Manual reporting to meet regulatory deadlines
- Access control applied per report rather than per model
Where Datalloy focuses
- Standardised reference and identifier mapping
- Row-level security in the semantic model
- Operational activity and capacity reporting
Retail
Sales, stock and customer behaviour move quickly, and reporting has to keep pace across channels.
Typical challenges
- Channel data held in separate platforms
- Stock and sales figures that never quite agree
- Product hierarchies that change mid-period
Where Datalloy focuses
- Product, store and channel conformed dimensions
- Sales and inventory facts at a defined grain
- Trading dashboards with like-for-like comparison
Logistics
Movement data arrives from carriers, telematics and internal systems, each with its own format and timing.
Typical challenges
- Carrier files in inconsistent formats
- Delivery performance measured differently per depot
- No historical view of route or cost trends
Where Datalloy focuses
- Automated file and API ingestion with validation
- Shipment and movement fact modelling
- Service level and cost-to-serve reporting
Distribution
Multi-warehouse operations need one view of stock, demand and fulfilment across locations and systems.
Typical challenges
- Separate stock records per site
- Supplier data with inconsistent identifiers
- Demand planning based on stale extracts
Where Datalloy focuses
- Supplier and location master data standardisation
- Stock movement and fulfilment fact tables
- Availability and replenishment reporting
Professional Services
Utilisation, delivery and profitability data is spread across time recording, finance and CRM systems.
Typical challenges
- Project profitability calculated manually
- Pipeline and delivery data not linked
- Utilisation defined differently by team
Where Datalloy focuses
- Client, project and consultant dimensions
- Time, revenue and cost fact modelling
- Utilisation and margin dashboards
Technology
Product, billing and usage telemetry grow quickly, and analytics has to be engineered rather than improvised.
Typical challenges
- Event volumes outgrowing ad-hoc reporting
- Product and finance metrics that disagree
- Analytics logic embedded in application code
Where Datalloy focuses
- Scalable ingestion of high-volume event data
- Usage, subscription and revenue modelling
- Governed metric definitions across teams
Different sector, same problem?
Fragmented sources, disputed numbers and manual reporting are not industry-specific.