Clear, owned outputs.
- Integration map of systems and flows
- Implemented integrations
- Error handling and retry logic
- Monitoring and alerts
- Integration documentation
Most organizations run many systems — ERP, CRM, e-commerce, marketing, finance, support — and each holds part of the picture. Data integration connects them so data flows where it's needed, without manual re-entry or nightly spreadsheet exports. We design integrations that are reliable, secure and easy to monitor.
The problems Data Integration solves.
API, event and iPaaS integration that keeps ERP, CRM and operational systems in sync.
Staff re-enter the same data into several systems.
Customer or product records differ between systems.
Point-to-point scripts break when one system changes.
Integrations fail without anyone noticing.
What Data Integration covers.
Integration architecture
Choosing APIs, events, files or iPaaS for each flow.
API integration
Connecting systems through their APIs, with error handling.
Event-driven integration
Publishing and consuming events as things happen.
iPaaS workflows
Low-code integration platforms where they fit.
Master data alignment
Consistent identifiers and records across systems.
Monitoring
Visibility of every integration's health and failures.
Ways to connect systems.
Integration isn't only about analytics. Applications also need to share customers, orders and stock in near real time. The right pattern depends on latency and who owns the data.
| Pattern | Use it for | Consider |
|---|---|---|
| Scheduled batch | Nightly or hourly syncs into a warehouse or between systems. | Simple and cheap; data is only as fresh as the last run. |
| Change data capture | Replicating database changes continuously without heavy queries on the source. | Needs log access on the source database and careful handling of schema changes. |
| APIs and webhooks | Application-to-application updates: a deal closes in the CRM, an invoice is raised in finance. | Rate limits, retries and idempotency have to be designed in. |
| Event streaming | Many consumers reacting to the same events, using Kafka, Event Hubs or Pub/Sub. | More moving parts; worth it when several teams need the same stream. |
| Reverse ETL | Pushing modelled warehouse data back into CRM, ad platforms or support tools. | Clear ownership of which system is the source of truth for each field. |
Why it's worth it.
Less manual work
Data moves automatically between systems.
Consistent records
Customers and products look the same everywhere.
Resilience
Monitored integrations recover from failures gracefully.
When Data Integration is the right move.
- Syncing CRM and ERP customer and order data.
- Connecting an e-commerce platform to inventory and finance.
- Feeding marketing platforms with product and customer data.
Technologies & platforms
- REST APIs
- Webhooks
- Apache Kafka
- Azure Service Bus
- Amazon EventBridge
- MuleSoft
- Boomi
- Azure Logic Apps
- Zapier
How Data Integration work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Data Integration questions.
Should we use an iPaaS or build integrations?
iPaaS tools speed up common integrations and suit smaller teams; custom integrations suit complex, high-volume or unusual needs. Many organizations combine both.
How do you deal with duplicate customer records?
With matching rules, a defined system of record for each field and processes to merge and prevent duplicates.
Can integrations be real time?
Yes, using APIs, webhooks or events, where source systems support them. Some flows are fine as scheduled batches.
Talk to us about data integration.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.