Data-driven tips: avoid lists and optimize data storytelling

In our 2022 year-end blog, we already wrote that lists are something to avoid if you want your organization to increase the impact of its data. But what's the story there? In this blog, Data Strategy Leader Louis de Roo explains.

Sorting, organizing and categorizing

First of all: as humans, we tend to want to organize complex flows of information. Organizing helps us interpret information faster: by focusing on the deviations in the resulting pattern, we can quickly identify the right data points. By sorting, organizing and categorizing our data, you find what you're looking for faster.

The downside of wanting to find something is that you often have to search. And anyone who has ever helped their partner find a lost set of keys, phone or wallet at home will agree that one person's search logic can be radically different from another's. It's no different with all those sortings, categories and orderings: to find what you're looking for, you are completely dependent on someone else's categorization. And just as your socks mysteriously disappear when your partner has done the laundry, in someone else's list you quickly lose track of the information you needed.

It's therefore only logical that when working with data, you soon start making your own lists. Enter a jungle of different Excel files... with just as many versions of the truth.

Logic

The ideal situation, of course, is that everyone follows your logic when looking at a list of data. And that they not only look for deviations through the same lens, but preferably also draw the same conclusions from that list as you do. The chance of that is slim, though, and the chance that you trade impact and decisiveness for discussions about the data and its interpretation is all the greater.

The solution, of course, is to make sure your data consumer has no choice but to follow your logic. In the past, it was quite a task to visualize your data every time in a way that left no doubt about the interpretation, but for quite some time now, a few good data visualizations let you easily convey your own search logic to your intended audience.

Data Storytelling

This so-called 'data storytelling' is essentially nothing more than pre-sorting the data for your audience. What do you want them to see? What conclusions do you want them to draw? By visualizing your data smartly and clearly, you ensure there's no doubt left about the conclusion. An added benefit: your users need much less time to get started with the data, and it's much easier for them to share the conclusions with colleagues, so the desired information spreads faster within your company.

To do this well and without misunderstandings, the most important thing is to try to translate your own search logic into a concrete set of parameters. Which exceptions do you look out for? What do you look at when you search for deviations in your list? Once you have that logic clear, it's a matter of creating a clear structure for communicating your data. Think of a data house style for your organization, or at least following a few basic principles when publishing your data. That way, a clear and unambiguous picture emerges by itself, with the decisions that go with it, based on data!

Design your own data house style?

Has this story inspired you, and do you want to develop your own data house style too? Then take a look at our dashboard design workshop or training via the buttons below.

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