From imperfect data to meaningful decisions: A week in my role at Altis

No two weeks in consulting look exactly the same but the core of my role stays pretty consistent: turning data into something meaningful and usable.

Most of my time (around 80%) is spent working directly with data. That might be building out reporting, shaping data models, or working through quality issues. The rest of my week is more collaborative - engaging with clients, working through challenges together, or supporting the team through stand-ups and knowledge sharing.

I like that balance. You're deep in the detail, but you're also constantly sense-checking what you're doing with others.

Working through ambiguity

One of the parts of the role I find most rewarding is working through ambiguity, especially when the data isn't perfect.

Recently, I was on a project where we had limited and imperfect datasets but still needed to estimate key metrics for the client. It was a good reminder that strong data work isn't just about having perfect inputs. It's about understanding the context well enough to make informed, transparent decisions.

That's a big part of consulting, helping organisations move forward with the data they have, not waiting for it to be perfect.

Collaboration makes the difference

A lot of the work is collaborative.

I regularly work with both client-side data teams and data warehouse teams, and building strong relationships makes a real difference. When there's a shared understanding of the data, including its limitations, and a common goal of improving it, it becomes much easier to navigate challenges and keep things moving.

Keeping things simple

From an organisational point of view, I try to keep things simple so I can stay on top of everything.

I rely heavily on my calendar and a running to-do list to prioritise work and stay focused. It helps me avoid analysis paralysis and makes sure I'm spending more time delivering outcomes rather than overthinking.

How I'm using AI

With the shift in AI, I've been spending more time building my understanding of how it can support data work.

There's a lot of potential, particularly in accelerating tasks like drafting logic or helping with documentation, especially in environments where time is tight or data maturity is still evolving.

But I see AI as an enabler, not a replacement.

The value still comes from knowing how to frame the right questions, interpreting outputs critically, and validating results against the underlying data and business context. It's something I'm continuing to develop, and I see it becoming increasingly important in how we work.

What I enjoy most

What I enjoy most about the role is the balance.

You're solving technical problems, but you're also working closely with people and helping them make sense of complex situations.

At the end of the day, it's about translating imperfect data into something stakeholders can confidently use to make decisions, and that's where the impact really comes from.