Business intelligence services
Most organizations do not have a reporting problem. They have four reports that disagree, and a monthly meeting spent deciding which one to believe.
We build the data platform underneath the dashboard: ingestion from every operational system, a modelled warehouse, metric definitions agreed once, and reporting that people trust enough to make a decision from without exporting it to a spreadsheet first.
The layers of a reporting platform
Data Warehousing
Cloud warehouses on Snowflake, BigQuery, Synapse or Redshift — modelled for the questions the business asks, not mirrored from the source schema.
Pipelines & Ingestion
Batch and streaming ELT from ERP, CRM, EDI, finance and operational systems, with schema change handled rather than discovered on a Monday.
Semantic Modelling
One definition of revenue, margin, active customer and on-time delivery, held in a governed layer every report reads from.
Dashboards & Reporting
Power BI and Tableau built around a decision — who reads this, how often, and what they do differently afterwards.
Self-Service Enablement
Curated datasets, documentation and training so analysts can answer their own questions without breaking the definitions.
Data Quality & Governance
Tests, freshness checks, lineage and ownership — so a number on a slide can be traced back to the row that produced it.
What usually brings a company to this conversation
- Two departments reporting different revenue for the same month
- Month-end close depending on one analyst and one workbook
- Dashboards nobody opens because nobody trusts them
- Operational questions answered days after they mattered
- Every new report requiring an engineering ticket
- Data extracts emailed between teams as a matter of routine
How a BI programme runs
Decisions first
Start from the decisions being made badly, and work backwards to the numbers they need.
Source assessment
Where each number lives today, who owns it, and how much of it is trustworthy.
Model and load
Warehouse design, pipelines and the semantic layer, built in a slice narrow enough to prove.
Publish
Reports and dashboards delivered against agreed definitions, with access and row-level security applied.
Adopt
Training, documentation and a retirement plan for the spreadsheets the new reports replace.
What we build it with
- Snowflake, BigQuery, Synapse and Redshift
- Azure Data Factory, Fivetran and Airbyte
- dbt for transformation and testing
- Power BI and Tableau
- Databricks and Spark for scale
- Airflow for orchestration
- Great Expectations for data quality
- Purview and Collibra for catalogue and lineage
A dashboard nobody opens is a cost, not an asset
The technical build is around half of a BI programme. The other half is agreeing definitions across departments that have quietly disagreed for years, and then retiring the shadow spreadsheets that encoded the old disagreement.
We plan that work explicitly — named metric owners, a definition register and a decommissioning list — because a platform that runs alongside the old reporting instead of replacing it has doubled the cost and settled nothing.
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