Intelligence & Automation
Data Engineering & Analytics
Data infrastructure for companies that are serious about it: ETL pipelines, cloud warehouses, real-time event streams, and BI dashboards built for the people who actually read them.
Pipelines, warehouses, and dashboards that drive decisions.
- One source of truth for your numbers
- Dashboards people actually read
- Decisions backed by live data
The problem
The numbers you need live in six systems and one overworked analyst.
Most companies already have the data they need, spread across a billing system, a CRM, a product database and several spreadsheets. Reports take days because someone has to combine them by hand, and two people give two different numbers for the same question.
We build the plumbing that brings those sources into one place, cleans them, defines each metric once, and puts the results in front of the people who decide. The aim is one number for each question, and a way to see how it was calculated.
What you receive
What a project produces.
A source inventory
A list of every system that holds data you care about, what each contains, and how it can be read.
Ingestion pipelines
Scheduled or streaming loads from your systems into a central store, with retries and alerts.
Warehouse and data model
A store organised around your business entities, with each metric defined once in version-controlled code.
Data quality checks
Automated tests for missing, duplicated or out-of-range data, run on every load.
Dashboards and reports
Views built with the people who use them, showing the numbers they are actually asked about.
Documentation and access rules
A catalogue of what each field means, and rules on who may see what.
How we approach it
The positions we take.
Start from the questions
We list the decisions people need to make and the numbers behind them. Pipelines are built for those, not for everything that could be collected.
Define each metric once
Revenue, active customer and churn are defined in code, reviewed and reused, so every dashboard agrees.
Test the data like software
Checks run on each load, and a failed check stops bad data reaching a report.
How it runs
Four steps, from the first call to hand-over.
1
Questions and inventory
We list the decisions and numbers that matter, then inventory the sources behind them. You receive a fixed quote for the first pipeline and dashboard.
2
Pipelines and model
Data is loaded, cleaned and modelled around your business entities, with quality checks from the first load.
3
Dashboards with users
We build views with the people who will read them and change them until they answer the questions they were built for.
4
Hand-over and training
Your team receives the documentation, the access rules and a session on how to extend the model themselves.
Is it a fit
When we are the right people, and when we are not.
A good fit
- Reports are put together by hand, or different people give different numbers.
- Data sits in several systems that do not connect.
- You are growing and need answers your current tools cannot give.
Another route is better when
- You have little data and a single system. A well-built spreadsheet or the tool's own reports are enough.
- You want dashboards before agreeing what the metrics mean. That disagreement has to be settled first.
- You are looking for a research-style data science team. We build the foundations and the reporting.
What we ask on the first call
- Which numbers do you look at each week, and who produces them?
- Where does each source of data live?
- Which two figures disagree today?
- Who should be able to see what?
- How fresh must the data be: daily, hourly or live?
Questions
About Data Engineering & Analytics.
Something missing? Write to [email protected] and an engineer will answer.
Do we need a data warehouse?
Not always. For a small volume, reporting straight from a well-organised database can be enough. We recommend the simplest option that meets your needs.
How long before we see a first dashboard?
The first working dashboard belongs in the earliest phase, built on one or two sources. Further sources are added after that.
Can you connect to the tools we already use?
Most common business tools offer an API or an export we can read. We check each one in the inventory phase.
Where will the data be stored?
In cloud accounts owned by your company, in a region you choose, with access rules agreed up front.
Describe what you need.
Describe the problem in plain language. An engineer reads every inquiry and replies within one business day, with a written scope and fixed price before you commit to anything.
- Reply
- Within one business day
- First call
- Free, no commitment
- Confidentiality
- NDA on request, before you share anything
- [email protected]