One version of the numbers
Revenue, headcount, and cost mean the same thing in every meeting.
Data Warehousing
One trusted data foundation.
Every system, one warehouse, and numbers the whole company can rely on.
What changes
Finance, payroll, identity, and support each keep their own version of the truth.
One warehouse. One set of numbers everyone trusts.
Reporting that runs itself
Dashboards refresh from the warehouse, not from someone's export.
A foundation ready for AI
Clean, governed data that automation and AI can safely build on.
In practice
One client, start to finish.
What the work looked like on a recent engagement.
- Situation
- Data was trapped in separate silos across finance, identity management, payroll, and Zendesk. Answering a cross-department question meant hunting through four systems by hand.
- What we built
- We extracted every source and consolidated it into a single warehouse on a modern cloud platform, with automated pipelines and shared definitions.
- Outcome
- One reliable source of truth. Manual data hunting disappeared, and cross-departmental reporting became possible for the first time.
What you get
What you walk away with.
Warehouse architecture
A target design on Snowflake, Databricks, or BigQuery, sized for your data and your budget.
Automated pipelines
Extraction and loading from finance, HR, payroll, support, and the rest, scheduled and monitored.
Modeled, documented data
Shared definitions for every number people argue about, with clear ownership.
Handover
Runbooks, access, and a team of yours that can run it without us.
How it runs
- Step 1
Discover
Inventory your systems, the questions they need to answer, and what is broken today.
- Step 2
Design
Agree the platform, the data model, and the definitions before anything is built.
- Step 3
Build
Stand up the warehouse and pipelines source by source, validating each against the original.
- Step 4
Run
Monitor, document, and hand over so it keeps working after we step back.
Is this for you
A good fit if this sounds familiar.
- Finance and operations teams reconciling spreadsheets by hand every month
- Companies whose reporting depends on one person's exports
- Leaders who want AI and automation but know the data is not ready
Questions
What people ask first.
How long does a warehouse take?
It depends on how many sources you have and how clean they are. We scope it in discovery and put the timeline in the statement of work before work starts.
Which platform will you recommend?
Whichever fits your data, your team, and your budget. We work with Snowflake, Databricks, and BigQuery and have no reason to push one over another.
Do we have to replace our current systems?
No. The warehouse sits alongside the systems you already run and reads from them. Nothing changes for the people entering data.