Don’t shoot the Excel-guy

Modelling

Everybody loves to hate the Excel-sheet. The model that shows the necessary cuts to costs. The model that shows the hockeystick blowing into the sky. Any model, really.

But don’t blame Excel. Blame the complexity of the world instead.

Why?

Because – newsflash – the world is a super complicated space, where nothing can be reduced to black & white, 0’s and 1’s and binary choices. It’s full color, total chaos.

All. The. Time.

For the very same reason you should never look down on the guy or girl whose job it is to use Excel to give a representation of the world. Because it is not only a super hard job. It is an impossible job. Because the world is complex.

Yet, it is great that someone is doing it. Cherish the Excel wizard.

Love the fact that someone is putting the chin out for you and others to hit – first on one side, and then on the other. Because at the end of the day we, as humans, need some kind of structure in chaos. Something from which we can navigate, have (informed) discussions and make (hopefully) slightly less bad decisions.

Because the best decisions aren’t made in a void with no overview. They are made where there is a sense of structure, overview and idea of what the heck is really going on in this world of constantly moving parts.

(Photo: Pixabay.com)

Choosing the right experiment

Work

One of the things I spend a significant amount of time on is devising, designing and running experiments on various different ideas for new concepts. It is both fun and challenging.

The challenging part is mostly about not reverting to the same 2-3 types of experiments and use them again and again. But because it is wrong to do so, and you might develop bias. But also because there are actually a lot of different ways, you can design and run experiments based on what kind of hypothesis, you’re trying to (dis)prove.

For that reason I have built yet another Excel-model; a simple database of all the different experiments, we know and can run with titles, applicable stages, ‘how to’-recipies and our know-how and experience in running them with valid results. Using the filter option on that one quickly allows me to narrow down the list of useful experiment-types for any given idea, broaden our horizon – and generate better results.

It is really that straightforward.

(Photo: Pixabay.com)

Excel’ing in assumptions

Modelling

What do you do, when you are a big fan of Assumptions Mapping as brought forward to David J Bland of Precoil, but you are not into doing a lot of Post It’s on a wall? You of course build an Excel model for it.

I have been using Assumptions Mapping for a couple of years now, but I have always struggled to use it in fx a workshop setting, because the concept with the quadrant, identifying knowledge gaps etc is foreign to many people. My experience is that it often goes much better if you just have a conversation, ask questions and plot down the answers.

So, I build a model in Excel that does exactly that. It lets you ask all the questions, make notes and score each answer based on the degree you have hard data on it and its criticality to the overall project. Once scores, the model will build a scatter chart with the correct labels, and in an instant you will have a visualization, you can work from. Cool, huh?

(Illustration: Visualisering fra modellen)