// 01
Each team keeps a different total for the same metric.
Three departments produce three different figures for the same measure. Meetings open with an argument about whose number is correct, not about what to do next.
Services · Data & Analytics
Shared metrics your teams can trust. Ready when the period closes.
Operational data warehouses, reporting backbones, and self-service analytics so “what happened, and why?” has one agreed answer — available the day after close, not three weeks later.
The problem
Why reports lose trust
It usually traces to one cause: a reporting layer added after the systems were built, instead of designed alongside the systems that generate the data.
// 02
Reports lag the business by days, sometimes weeks.
Manual ETL, spreadsheet refreshes, and hunting for the right column in the right export. By the time the report arrives, the decision it should have informed has already been made.
// 03
Self-service tools that only specialists can use safely.
A dashboard layer nobody outside finance can query without breaking something — because the data model is hard to navigate and metric definitions live in a few people’s heads.
Capabilities
What we build
Warehouses, pipelines, reporting, and governance — scoped to what the business needs.
// 01
Operational data warehouse
An audit-ready store for operational data from ERP, WMS, HRMS, IoT, and CRM. Kept separate from transactional databases, built for analytical reads, and governed by metric definitions the business agrees on.
PostgreSQLClickHouseSnowflakeBigQuery
// 02
ETL & event pipelines
Monitored ingest from every system you depend on — event-driven where that fits, scheduled where it is more practical, with retries and dead-letter handling from the start.
AirflowKafkadbtCDC
// 03
Reporting & self-service analytics
Management dashboards designed for the people who use them daily. A semantic layer underneath lets non-engineering teams answer their own questions without corrupting the model.
Power BITableauMetabaseLooker
// 04
Data quality, lineage & governance
Schema tests, freshness monitors, column-level lineage, and a metric catalogue that outlasts whoever built it. Audit-ready from the start, not a rush project before a compliance review.
dbt testsGreat ExpectationsLineageMetric store
Reference architecture
A typical Golden Synapse data backbone
Conservative, well-understood components, tuned per client. We reach for specialised tools only when standard ones cannot meet the requirement.
// Sources
ERP
VAQT HRM
Sentinel Strix · IoT
CRM
Finance
Files / SFTP
// Warehouse
Central warehouse
Modelled, versioned, governed
Postgres / CH
// Outputs
Power BI
Tableau
Embedded dashboards
API → AI/ML
Scheduled reports
What changes when it lands
Reports people use. Decisions that wait for numbers.
What teams typically see within a quarter of go-live — outcomes of the architecture, not aspirational claims.
We publish outcome metrics once clients confirm them, not before.
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End-of-period close in days instead of weeksReconciliation becomes work the system has already completed, not a manual scramble.
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One agreed figure per metricMeeting time shifts from debating whose number is correct to deciding what to do about it.
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Self-service without breaking the modelThe semantic layer makes it hard for a non-technical team to corrupt the numbers by accident.
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Audit prep in hours, not weeksColumn-level lineage and event sourcing turn a compliance review into a query instead of a project.
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AI/ML and analytics share the same warehouseModels read from the same warehouse the dashboards use, so there is no second parallel data set to keep in sync.
Scope a data project
A 30-minute call with a data engineering lead. Bring the reports your teams no longer trust; we will outline what it would take to fix them.