We design and build ETL pipelines and data warehouses that bring your ERP, CRM, finance, and operational data into a single, reliable source of truth — including environments where direct database access isn't possible, as we demonstrated for Atlantic LNG.
Conflicting numbers, stale data, connectivity constraints — these are the structural problems that dashboards alone can't fix.
Finance says revenue is $4.2M. CRM says $4.4M. Operations says output corresponds to $3.9M. Each system is right by its own rules — but there's no single agreed number. A data warehouse with a canonical data model creates one validated version of every metric, applied consistently across all sources.
Power BI running Direct Query on the ERP database. Complex dashboard queries consuming production server resources. Performance degradation during peak operational hours. Reporting queries competing with transactional queries on the same database. A warehouse moves analytical workload off production systems entirely.
The IT or security policy doesn't permit BI tools to connect directly to production databases. Or the database is on a platform that the BI tool doesn't support natively. The Atlantic LNG engagement was exactly this situation — and we built a complete data platform using staging and PowerApps without ever connecting to production.
ERP data and CRM data can't be joined. Financial data and operational data live in separate systems with no common key. Cross-functional questions — "which customers have both overdue invoices and open support tickets?" — simply can't be answered because the data was never in the same place.
The ERP shows current stock levels. The CRM shows current deal stages. But what did stock look like 6 months ago? How has the pipeline evolved over the quarter? Without a warehouse preserving historical snapshots, trend analysis is impossible or requires manual reconstruction from old reports.
Someone exports data from the ERP every Monday. Someone else downloads a CSV from the CRM every Friday. Both files dropped in a shared folder and manually combined in Excel by an analyst. This is informal ETL — fragile, unmonitored, and consuming significant time from people who should be doing other things.
Each stage has a specific job. When any stage is skipped or done poorly, the downstream data can't be trusted.
From source system mapping to BI-ready warehouse — every layer designed and documented.
Before any build, we map every data source — database platforms, API availability, export capabilities, access permissions, data owner contacts. We assess which sources can be connected directly, which need a staging approach, and which need a data capture layer built (as with Atlantic LNG's PowerApps approach). Every connectivity constraint identified upfront — no surprises mid-project.
Extract pipelines connecting to source systems — direct database connections, API integrations, scheduled file imports, or staging layer reads. Transform logic cleaning, standardising, and applying business rules to raw data. Load processes writing validated data to the warehouse on defined schedules with full audit trails. Pipeline monitoring with alerts on failure or unexpected data volume deviations.
Dimensional model designed for analytical query performance — fact tables for transactional events, dimension tables for descriptive attributes, slowly changing dimension handling for historical accuracy. Schema agreed with your data team before build begins. Documentation produced as a deliverable — not optional.
Before the warehouse is built, we work with finance, sales, and operations leadership to define every key metric in writing — revenue recognition timing, deal close definition, margin calculation method. These definitions are encoded in the transform layer and applied consistently across every table. One number everywhere — by design, not by accident.
Automated data quality checks run on every pipeline load — referential integrity, null validation, row count delta checks, business rule validation. Quality failures halt the pipeline and alert the data team rather than loading bad data silently. Data lineage documented so every metric can be traced back to its source. Row-level security applied so each audience sees only appropriate data.
Not every environment allows direct database connectivity. Where it isn't available — due to security policy, technical constraints, or system limitations — we design alternative ingestion architectures. Structured Excel staging from controlled exports. Microsoft PowerApps forms for operational data capture. SharePoint or Dataverse as structured intermediary layers. The Atlantic LNG project ran entirely on this approach.
The Atlantic LNG approach is our most referenced — a complete data platform built without a single production database connection.
Atlantic LNG had operational and financial data in multiple systems across different technical environments. Direct connectivity to production databases was either constrained by security policy or technically unavailable for the BI platform. The conventional ETL approach — connect Power BI directly to the databases — was not an option. We designed and built a two-stream ingestion architecture: structured Excel exports from each system into a controlled staging area, and Microsoft PowerApps for data that needed to be actively captured and didn't have an existing digital source. Both streams fed a Power BI data model that served the executive dashboard and departmental views.
The restaurant chain (NDA) had data in POS systems across multiple locations, a finance system, a supply chain management platform, and operational spreadsheets tracked at the kitchen level. Each system used different identifiers for locations, products, and time periods — making cross-system analysis impossible without a transformation layer. We built a unified data warehouse with a common location and product dimension across all sources, enabling the executive, sales, production, and churn dashboards to be built from a single consistent data model.
A professional services business had three systems: an ERP for project management and resource allocation, a CRM (Zoho) for pipeline and client management, and QuickBooks for finance. Every Monday morning, someone manually exported data from all three, combined in Excel, and emailed a static report to leadership. The report was out of date by Tuesday. We built ETL pipelines connecting all three systems to a central data model in Power BI Service — refreshing on a 4-hour schedule, replacing the manual Monday process entirely.
Direct connections, API integrations, staged exports, or PowerApps capture — we've connected data from all of these.
The dimensional schema, table definitions, and metric calculations are documented and handed over. Your team knows exactly what's in the warehouse, where it came from, and how every number is calculated.
Every pipeline run logged, row counts validated, data quality checks executed. Failures alert the data team before bad data reaches dashboards. You know the pipeline ran and the data is valid.
The Atlantic LNG approach — structured staging plus PowerApps — works for any environment where direct database connectivity isn't possible. Security policy, legacy systems, third-party platforms — we've worked around all of these.
A warehouse isn't just a current-state view — it's a historical record. We design the load strategy to preserve snapshots so trend analysis, period-over-period comparisons, and time-series modelling are all possible from the day the warehouse goes live.
ISO 27001. NDA before any data is shared. We map your sources and assess connectivity before recommending any architecture.
Source mapping first. Schema agreed second. Pipelines built third. BI tools connected last.
Map every data source, assess connectivity, identify constraints, and define canonical metric definitions with your business team.
Dimensional model designed and documented. Agreed with your data team before any build starts. No surprises in the warehouse structure.
ETL pipelines built, data quality checks configured, historical load executed, and all data validated against source systems before go-live.
BI tools connected to the warehouse. Dashboard numbers validated against source system extracts. Monitoring and alerting live from day one.