Data & Analytics
Data Warehousing & Analytics
Built From the Ground Up
We don't just build dashboards — we build the data foundation underneath them. From raw data in disconnected systems to a trusted warehouse, master data layer, and live reporting your team actually uses.
Why Most Data Projects Underdeliver
Dashboards built on bad data
A Power BI report is only as good as the data feeding it. Without a clean, structured warehouse underneath, dashboards look great and lie constantly.
No single source of truth
Your CRM, finance system, and ops tools all define "customer" differently. Until that's resolved at the data layer, every report is a negotiation.
BI tools bought before the data was ready
Tableau or Power BI licences get purchased, a dashboard gets built on top of raw exports, and six months later nobody trusts the numbers.
What We Build
End-to-end data infrastructure — from source systems to trusted reporting — on your own cloud account.
Data Warehouse Design & Build
We design and build your warehouse on Snowflake, Databricks, BigQuery, or Redshift — structured for your business logic, scalable as you grow, and deployed to your own cloud account.
Master Data & Data Modelling
We create the authoritative definitions of your key business entities — customers, products, accounts — so every system and every report agrees on the same record.
Data Pipelines & Ingestion
Automated pipelines that pull from your source systems — CRM, ERP, finance tools, spreadsheets — clean the data, and load it into the warehouse on schedule.
Analytics Impact Mapping
Before we build anything, we work with you to identify which metrics actually drive decisions in your business. We build what matters — not a comprehensive dashboard nobody looks at.
Transformations with dbt & Python
We use dbt and Python to transform raw data into clean, business-ready models — documented, version-controlled, and maintainable by your team.
Dashboards & Reporting
Power BI, Tableau, Looker Studio — we build on whichever BI tool fits your team. The reporting layer sits on top of a clean warehouse, so the numbers are always trustworthy.
The Tools We Work With
We choose based on your existing stack and what will be easiest for your team to maintain long-term.
Snowflake
Cloud data warehouse — flexible, scalable, works across AWS, Azure, and GCP.
Databricks
Large-scale data processing, ML pipelines, and unified analytics on AWS or Azure.
BigQuery / Redshift
Native warehouse options for teams already on GCP or AWS.
dbt
Data transformation and modelling — clean, documented, version-controlled.
Apache Spark
Large-scale distributed data processing for high-volume pipelines and complex transformations.
Apache Iceberg
Open table format for large analytic datasets — reliable schema evolution and time-travel queries.
Apache Airflow
Pipeline orchestration — scheduling, monitoring, and managing complex data workflows.
Python
Custom ingestion scripts, data cleaning, pipeline orchestration, and complex transformation logic.
Power BI / Tableau / Looker Studio
BI layer — we are tool agnostic and recommend based on your team and budget.
How We Work Differently
Foundation first, dashboards second
We build the warehouse and data model before touching a BI tool. Reporting built on a solid foundation stays reliable as your business changes.
We map impact before we build
We start by understanding which decisions your business needs to make and which data supports them. No vanity dashboards, no unused reports.
BI tool agnostic
The warehouse is the real work. We recommend a BI tool based on what you already have and what your team will actually use — not what we prefer.
Built on your cloud, owned by you
Everything lives on your AWS, Azure, or GCP account. No dependency on our infrastructure, our credentials, or our continued involvement.
Data Analytics Consulting for SMBs Across Canada and the US
We work best with growing businesses where data is scattered, reporting is manual, and decisions are made on incomplete information.
- ✓ Businesses whose data lives in multiple disconnected systems with no unified view
- ✓ Teams spending hours every week manually compiling reports that should be automated
- ✓ Companies who have purchased BI tools but can't trust the numbers they produce
- ✓ Businesses ready to build a proper data foundation for the first time
- ✓ Finance, ops, or sales leaders who need reliable metrics to run their function effectively
Frequently Asked Questions
Do you build full data warehouses or just dashboards?
Both — but we always start with the data foundation. Dashboards built on top of poorly structured data are unreliable. We design and build the warehouse, master data layer, and transformation logic first, then build reporting on top. You get dashboards you can actually trust.
Which cloud do you build on?
AWS is where we are most experienced, but we work across Azure and GCP as well. We build on whichever cloud your business already runs on — everything is deployed to your own account.
Which BI tool do you recommend?
We are BI tool agnostic. Power BI, Tableau, Looker Studio — the reporting layer is just that, a layer. We recommend based on what your team already uses, your budget, and your licensing situation. The warehouse and data model underneath is where the real work happens.
What is master data and why does it matter?
Master data is the single, authoritative definition of your key business entities — customers, products, suppliers, accounts. Without it, the same customer might exist differently in your CRM, your finance system, and your ops tool. Master data creation ensures every system agrees on the same record, which is the foundation of reliable reporting.
How do you decide what to build?
We start with an analytics impact mapping exercise — working with you to identify which metrics and decisions actually drive your business, and which data you need to support them. We build what matters, not a comprehensive dashboard that nobody looks at.
How long does a data warehouse build take?
A focused data warehouse connecting 2–4 source systems with a core dashboard typically takes 6–10 weeks. Larger multi-source warehouses or complex transformation layers run 10–16 weeks. We scope clearly upfront with no surprises.
Ready to build a data foundation you can actually trust?
Book a free 30-minute discovery call. We'll assess your current data state and map the highest-impact place to start.
Book a Discovery Call