Data integration
“Our data is scattered across systems that do not talk to each other.” If that sounds familiar, these are the problems we solve here and the services that do it.
The services that solve this
26 use cases across 10 industries come back to these 2 services. Each one has its own page with what it covers and how work starts.
Data integration: problems we solve
What companies tell us, and what we build for it. Pick your industry to see the ones from your sector first.
26 use cases
Problem: Stock figures differ between the ERP, the warehouse system and the stores.
What we do: Pipelines that bring ERP, warehouse and store data into one reporting store, refreshed on a schedule.
Problem: Machine stoppages are logged by hand, if at all, so their causes are hard to trace.
What we do: Collection of machine and production records into a database the maintenance team can query.
Problem: Online and in-store sales are reported separately and never quite reconcile.
What we do: One customer and order model across channels, so sales reports add up.
Problem: Patient records are split across hospital, lab and billing systems that do not share identifiers.
What we do: Integration pipelines that match patients across systems, using HL7 or FHIR where your systems support them.
Problem: Reports, scans and notes arrive as PDFs and images rather than structured data.
What we do: Extraction of key fields from reports into structured tables, with the source document kept for reference.
Problem: Bookings from OTAs, the website and walk-ins land in different places and are reconciled by hand.
What we do: Pipelines that bring channel, booking engine and property data into one reservations model.
Problem: Guest history is scattered, so repeat guests are treated like new ones.
What we do: A single guest record built from stays, bookings and loyalty data, with consent respected.
Problem: Consignment status lives in phone calls and spreadsheets, so customers chase updates.
What we do: A shipment tracking model built from bookings, scans and GPS, with status views for operations and customers.
Problem: GPS data is collected but never joined to trips, costs or delivery times.
What we do: Pipelines that tie telematics data to trips, routes and delivery times.
Problem: Warehouse, transport and billing systems disagree about the same shipment.
What we do: One shipment record across TMS, WMS and billing, with mismatches flagged for review.
Problem: Usage and network event volumes outgrow the databases and batch jobs built for them.
What we do: Streaming and batch pipelines sized for your event volumes, using tools such as Kafka and Spark where they fit.
Problem: Subscribers leave without warning, because usage, complaints and billing are never read together.
What we do: A subscriber view joining usage, complaints and billing, as the base for churn analysis.
Problem: Audience figures differ between the app, the website and each ad platform.
What we do: One audience model built from app, web and partner data, with definitions agreed up front.
Problem: Playback problems show up on social media before the team sees them in data.
What we do: Player and app event pipelines with dashboards and alerts on errors and buffering.
Problem: Overnight batch jobs on old databases leave reports a day behind.
What we do: A modern data warehouse or lakehouse, on platforms such as Snowflake or Databricks where they fit.
Problem: Admission, LMS, exam and fee data each sit in their own system.
What we do: One student record across admission, LMS, exam and fee systems.
Problem: Secondary and tertiary sales arrive late and in a different format from each distributor.
What we do: Pipelines that collect and standardise distributor and marketplace data on a schedule.
Problem: Field sales data and ERP figures tell different stories.
What we do: One sales model reconciling field apps, distributor data and ERP dispatches.
Problem: Suspicious transactions are reviewed days later, from batch reports.
What we do: Transaction pipelines and rules that flag unusual activity for review as it happens.
Problem: Regulatory returns are compiled by hand from several systems every period.
What we do: Automated, reconciled data feeds for regulatory and MIS reports, with a trail back to source.
Problem: Our data sits in separate systems that do not talk to each other.
Any industry
What we do: We connect the sources and move their data into one warehouse on a schedule you set.
Problem: Our nightly jobs fail and someone fixes them by hand each morning.
Any industry
What we do: We rebuild fragile jobs with retries, monitoring and alerts, and document how they run.
Problem: Reports disagree because each team cleans the data its own way.
Any industry
What we do: We move the cleaning and business rules into the pipeline, so every report starts from the same tables.
Problem: By the time our reports refresh, the moment to act has passed.
Any industry
What we do: We stream the events that matter to a live view or an alert, and leave the rest on batch.
Problem: We need to spot failed payments or stock-outs while they are happening.
Any industry
What we do: We build alerts on the event stream, routed to the team that handles them.
Problem: Our streaming setup works, but nobody trusts it after the last outage.
Any industry
What we do: We review it, fix how it handles failures and replays, and add monitoring.
What you can hold us to
Companies we have delivered projects for:
A written plan before any work starts
After the first call we review your setup and send scope, timeline and team in writing.
Documentation with everything we deliver
Pipelines, databases, dashboards and deployments are handed over with documentation your own team can follow.
One team accountable for the result
The same engineers work across your databases, pipelines, dashboards and releases.
Talk to us about data integration
Tell us what is going wrong and an engineer from our team will get back to you to set up a call.