The clean data foundation everything else runs on.
Fifty One Degrees builds the modern data stack mid-market firms run on, a governed warehouse and clean metrics, the foundation everything else depends on.
Executive Overview
dbt · governed metrics
Why doesn’t your data add up?
What is a modern data stack?
A modern data stack moves data from your source systems into a warehouse, models it so the numbers are consistent, and surfaces it in BI people trust, then pushes insight back into the tools where work happens. Fifty One Degrees builds all four layers, the foundation beneath Data Science & ML and AI Agents.
How does Fifty One Degrees build a data platform?
Spreadsheets, off-the-shelf BI and a governed data platform compared
| Dimension | Manual spreadsheets | Off-the-shelf BI tool alone | Fifty One Degrees data platform |
|---|---|---|---|
| Source of truth | a different number per team | one dashboard, unverified inputs | one governed warehouse, tested and traceable |
| Freshness | a week or more to update | as fresh as the last manual export | pipelines sync on a schedule, monitored for breaks |
| Trust | nobody agrees on the figures | trusted until it is not, no lineage | automated tests plus lineage on every metric |
| Ready for AI | not without heavy rework | not designed for it | the foundation Data Science & ML and AI Agents run on |
| Ownership | scattered across individuals | locked to the BI vendor's model | you own the warehouse, pipelines and dashboards |
Why this comes first
Predictions and agents are only as reliable as the data beneath them. Fifty One Degrees frequently builds this layer first, then layers Data Science & ML and AI Agents on top.
What a Fifty One Degrees data foundation has delivered
Foundations in production
Who is this for?
How does a data engineering engagement run?
Fixed-scope, not an open-ended platform project: Fifty One Degrees starts from the questions the business needs answered and builds back, so cost and timeline are clear up front. You own the warehouse, the pipelines and the dashboards, on standard tools with no lock-in, and your team is trained to run them.
Questions firms ask Fifty One Degrees about data engineering
How do you set up a data warehouse for a growing business?
Start from the questions the business needs answered, then build back: connect the source systems with pipelines (Fivetran or Airbyte, orchestrated with Airflow or Dagster), load into a warehouse such as Snowflake or BigQuery, model it in dbt on the medallion pattern, and surface it in BI. Fifty One Degrees builds this fixed-scope so cost and timeline are clear up front, rather than an open-ended platform project.
Do we need a data warehouse before using AI?
Usually, yes. AI and predictive models are only as good as the data underneath them. Fifty One Degrees frequently builds the foundation first, then layers AI and data science on top, so results are reliable in production rather than impressive in a demo and wrong in practice.
What is the difference between ELT and ETL, and which do you use?
ETL transforms data before loading it; ELT loads raw data into the warehouse first and transforms it there. Fifty One Degrees uses ELT by default because transforming in the warehouse (with dbt) is cheaper, more auditable and easier to change. The right choice still depends on the data and the use case.
How do you fix dashboards nobody trusts?
With data quality built in. Fifty One Degrees adds automated freshness, volume and schema tests, a semantic layer so a metric means one thing everywhere, and lineage so anyone can see where a number came from. When the numbers are tested and traceable, people stop exporting to Excel.
How long does it take, and what does it cost?
It depends on how many systems and how messy the data is, but Fifty One Degrees scopes to fixed deliverables so you know cost and timeline before starting. Prove value early, scale what works.
Which tools do you build on?
Warehouses on Snowflake, BigQuery or Redshift; transformation in dbt; ingestion with Fivetran or Airbyte and orchestration with Airflow or Dagster; BI in Tableau, Power BI or Looker; and reverse-ETL to push data back into operational tools. Fifty One Degrees picks per use case, on standard tools, with no lock-in.
Is our data governed and GDPR-compliant?
Yes. Fifty One Degrees builds cataloguing and lineage, role-based access control and PII masking, with handling aligned to UK GDPR, so access is controlled and every number is traceable.
Do we own what you build?
Yes. You own the warehouse, the pipelines and the dashboards, built on standard, portable tools, and your team is trained to run and extend them. No lock-in to Fifty One Degrees or to a proprietary platform.
Book a 30-minute discovery call and we’ll map your data and what it takes to reach one trusted source of truth.
Fixed-scope, no lock-in. You own the warehouse, the pipelines and the dashboards.