Fields

What the work is about.

These fields appear in all three routes. Which route fits is decided only once it is clear what is missing.

The nine fields of work

Data analysis: abstract composition of squares

Data analysis

How do we get insight from what we already have?

Most organisations have more data than insight. Data analysis makes visible what is happening in the numbers and translates it into decisions someone can actually take.

Business Intelligence: abstract composition of squares

Business Intelligence

How do we build management information people actually use?

A dashboard nobody opens costs more than no dashboard. We build management information around the question someone asks every Monday morning, not around what is technically possible.

Data engineering: abstract composition of squares

Data engineering

How do we build pipelines that keep working?

Data moved by hand goes wrong eventually. Data engineering turns loose actions into a process that runs, even once the person who built it has moved on.

Data quality: abstract composition of squares

Data quality

Why do our numbers disagree, and how do we secure them?

Two departments, two numbers, the same question. Data quality is about cleaning, validating and securing, so the conversation returns to the outcome instead of the figures.

Data migration: abstract composition of squares

Data migration

How do we move to a new system safely?

A migration has only succeeded if nobody noticed it. That takes repeatable scripts, checks that surface differences, and documentation that stays with your team.

Data strategy: abstract composition of squares

Data strategy

Where should we start, and what comes first?

Plenty of ideas, no ownership, and the fear of investing in the wrong thing. Data strategy brings direction and priority, and says where not to invest as well.

Automation: abstract composition of squares

Automation

How do we take the manual work out?

Merging the same file every Monday morning is work a computer does better. Less manual work means fewer mistakes and more time for the work that deserves attention.

AI & machine learning: abstract composition of squares

AI & machine learning

How do we get AI from proof of concept into production?

Most AI initiatives fail not on the model but on everything around it: data, maintenance, ownership. We take it from proof of concept through MVP into production.

Generative AI: abstract composition of squares

Generative AI

How does our team use AI responsibly?

Generative AI is already being used inside your organisation, whether you set it up or not. The question is whether it happens in a way you can explain.

Also under What we do

Not sure which field it is?

You do not have to know. Tell us what is going wrong or being left, and we will say which field that is and who solves it. Including when the answer is that it is not us.