Data engineering
Reliable ETL/ELT pipelines from databases, APIs, files and devices, scheduled, tested and monitored.
Data analytics & BI
One version of the numbers, delivered before the meeting
Your data lives in ERPs, spreadsheets, apps, machines and cloud services. We connect it, clean it, model it and put it in front of the right people, from automated weekly reports to live operational dashboards.
What we deliver
From raw data to decisions
Warehouses & lakehouses
A modelled, documented single source of truth in Snowflake, BigQuery, Redshift, Azure or PostgreSQL.
Dashboards & BI
Dashboards people actually open, in Power BI, Looker, Tableau, Metabase or Grafana, designed around decisions.
Automated reporting
Board packs, client reports and regulatory submissions generated on schedule instead of assembled by hand.
Real-time & IoT data
Streams from machines, meters and buildings, stored for analysis and shown live, an area we know particularly well.
Data quality & governance
Validation rules, lineage, definitions everyone agrees on, and access controls that satisfy privacy requirements.
How we build it
Get the first three stages right, and the rest follows
Every analytics project follows the same path: collect, ingest, give the data context, analyse, deliver. Most failed dashboards fail in the first three stages, in unreliable feeds, missing definitions and mismatched IDs.
We put the engineering effort there, so the numbers at the end can be traced, reproduced and trusted.
- 01Collect
- 02Ingest
- 03Contextualize
- 04Analyze
- 05Deliver
Process
A typical analytics engagement
Questions first
Agree the decisions the data must support, the metrics and their exact definitions.
Source audit
Map where each number comes from, how reliable it is, and what is missing.
Pipeline & model
Build the pipelines and data model, with tests that catch bad data before it reaches a report.
Dashboards & adoption
Ship dashboards and reports, train the teams, and refine them with real use.
Frequently asked questions
We already have Power BI (or another tool). Can you work with it?
Yes. We often keep the BI tool you have and fix what is underneath: the pipelines, the data model and the definitions.
Do we need a data warehouse?
Not always. For a few sources and modest volumes, a well-modelled database can be enough. We recommend a warehouse when sources, volume or history make it worthwhile.
Can you combine operational data from machines and buildings with business data?
Yes, and it is where we are strongest: we integrate equipment and building systems as easily as ERPs and SaaS tools.
How do you handle personal data?
With minimisation, role-based access, and masking or pseudonymisation where analysis doesn’t need identities, in line with the privacy rules that apply to you.
Spending Mondays building reports?
Tell us which numbers you need and where they live today. An engineer replies within 24 hours.