I’ve spent the last few (many!) weeks doing something that doesn’t feature much in the romantic version of being a researcher: cleaning data.

Not the exciting kind of work. Or so I thought.

I’m in the middle of my doctoral level research exploring the impact of coaching on resilience and wellbeing. That means surveys, lots of them — 173 participants across 8 time points, all needing to be pulled together into one clean dataset (or person-wide to get technical).

I taught myself Power Query in Excel. I built merge processes. And then I spent a disproportionate amount of time hunting down errors that were quietly causing chaos under the surface.

Here’s what surprised me.

The 1% Problem

At one point, roughly 99% of my data was perfectly fine. But that remaining 1% — a small misalignment, a record that wouldn’t merge cleanly — was throwing out extra rows, corrupting file joins, and casting a shadow over everything else.

The kind of thing that looks like a bigger problem than it is, until you trace it all the way back to its tiny, unremarkable origin. Finding it required something I didn’t expect to need for a spreadsheet: curiosity.

Not frustration. Not assumption. Curiosity.

Why is this happening?

Where is it coming from?

What am I not seeing yet?

I had to resist the urge to decide what the answer was before I’d actually found it. I had to stay in the not-knowing, follow the breadcrumbs, and resist the pull of the nearest convenient explanation.

And Then It Hit Me.

That’s coaching.

When a client says something unexpected — something that doesn’t quite fit the story they’ve been telling, or lands with more weight than the words alone carry — the instinct can be to move past it, make sense of it quickly, file it away. But the most important moments in coaching are usually in that 1%.

The small thing that’s throwing the bigger thing out. Staying curious rather than conclusive. Asking where is this coming from? and what does this mean? Drilling down, gently, to find the root cause — not to fix it immediately, but to understand it properly first.

The British Psychological Society’s standards for Coaching Psychology actually name this explicitly: the capacity to tolerate uncertainty and make informed judgements in the absence of complete data (a core doctoral-level competency). I’d always understood that in the abstract. Spending weeks debugging survey files made it feel genuinely real.

What This Means for Coaching Practice

Data analysis looks black and white. You either have the right answer or you don’t; the file either merges or it doesn’t. But getting there requires navigating a vast pool of possibilities with only partial information — following the faint trail of something that isn’t quite right, without knowing where it leads.

That’s not so different from sitting across from a client. The ‘issues’ aren’t always obvious. The root cause is rarely the first thing you see.

And the work — whether you’re in Excel or in a coaching conversation — is to stay open, stay curious, and trust that if you partner in following the breadcrumb, you’ll find what is buried underneath.

Sheela Hobden

www.bluegreencoaching.com

April 2026

I share musings and monthly updates from my research into coaching, resilience, and wellbeing. If you’re a coach, a psychologist, or just curious about what doctoral research actually looks like from the inside — I’d love for you to follow along.

Image credit: Mika Baumeister on Unsplash​