06Data & Integrations
One version of the operational truth.
Integration and data platform work for organisations where the answer to a simple business question depends on four systems that disagree.
01Where it starts
What we usually walk into.
Every system holds part of the picture. Integrations were built point-to-point over years, nobody owns the contracts, and reconciliation happens manually in a spreadsheet at month end.
Three systems report three different revenue figures and each is defensible.
A point-to-point integration mesh breaks whenever any system is upgraded.
Month-end reconciliation takes a week of manual work.
A legacy system holds critical data and has no API.
Reporting is built directly against production databases and slows them down.
02Approach
How we do the work.
We define explicit data contracts, build resilient event-driven and batch integrations, and make quality and lineage observable so problems surface as alerts rather than as wrong numbers.
- 01
Contracts before connectors
We define what each system promises — schema, semantics, timeliness, ownership — before building the pipe. Undocumented implicit contracts are the reason integrations break on upgrade.
- 02
Events where it fits, batch where it does not
Not everything needs to be real time. We match the integration pattern to the business requirement instead of applying one architecture to every flow.
- 03
Quality as a first-class signal
Expectations about completeness, freshness and referential integrity are encoded as checks that alert. Silent data quality failures cost more than outages because nobody notices.
- 04
Legacy without a rewrite
Change-data capture, adapter services and controlled extraction let modern systems consume legacy data without waiting for a modernisation programme that may never be funded.
Capabilities and deliverables
Capabilities
- Integration architecture and data contract design
- Event streaming and message-based integration
- Change data capture
- ETL and ELT pipeline engineering
- Data modelling for analytics and operations
- API design and gateway patterns
- Data quality and observability
- Master data and identity resolution
What you receive
- Data contracts and integration architecture documentation
- Event-driven and batch pipelines with automated tests
- Change-data-capture and legacy adapter services
- Data quality checks with alerting and ownership
- Lineage documentation from source to consumed figure
- Reconciliation tooling replacing manual month-end work
- Operational dashboards for pipeline health
Common use cases
- Consolidating operational data across business units
- Post-merger system integration
- Migrating from point-to-point integrations to an event backbone
- Building a reporting layer that does not touch production databases
- Real-time operational data feeds
- Master data alignment across systems
Technologies typically involved
- Go
- Python
- PostgreSQL
- MongoDB
- Kafka
- NATS
- RabbitMQ
- Redis
- Elasticsearch
- Debezium
- Airflow
- dbt
- Kubernetes
- OpenTelemetry
03Engagement
How it runs.
- 01
Landscape review
2 weeks
System inventory, existing flows, ownership, and the decisions that depend on them.
- 02
Architecture
2–4 weeks
Data contracts, integration patterns, quality model and phased plan.
- 03
Delivery
Ongoing
Flow-by-flow implementation, highest-value or highest-risk flows first.
- 04
Handover
2–3 weeks
Operational documentation, alert ownership and team enablement.
04Security
Security considerations.
These apply to this service specifically. They are engagement conditions, not aspirations, and we will put them in the contract.
- Data classification agreed before any flow is built
- Field-level minimisation — integrations move what is needed, not whole tables
- Encryption in transit and at rest across every hop
- Per-integration service identities with least-privilege access
- Personal data flows mapped for your records of processing
05Outcomes
What changes afterwards.
Reliable data movement between systems, a single reconciled view of the operations that matter, and integrations that survive their source systems changing.
- 01
Business questions have one answer, with a traceable derivation
- 02
Manual reconciliation work is replaced by automated checks
- 03
Source system upgrades stop breaking downstream consumers
- 04
Data quality problems are found by monitoring, not by customers
06Questions
Asked before every engagement.
Often the answer is neither, at least not first. Many organisations get more value from fixing the operational integration layer than from adding another analytical store on top of unreliable inputs. We assess before recommending a platform purchase.
Usually yes. Change data capture at the database level, file-based extraction, or a dedicated adapter service can expose the data safely without modifying the legacy application.
Yes. If you have an ESB, iPaaS or streaming platform in place, we build within it. Replacing working infrastructure is a decision with its own business case, not an automatic step.
Frequently runs alongside
Ready to scopedata & integrations?
Bring the problem, the constraints and the deadline. We will tell you what is achievable, what it costs and what we would do first.