mind mapping data visualisations mindmap visualization - Worked example

mind mapping data visualisations mindmap visualization - Worked example

Why does a data bloke love a Mind Map?

Me and the Mind Map, we shouldn’t be friends. I like data, and I really like data architecture. This means I spend a lot of time thinking about what goes where. In this a data architect is just like a normal architect: structure matters. Mind maps on the other hand, well, you know, anything goes really; just plonk your idea in the centre of the screen, draw some spokes and go crazy.
Never the twain should meet. Right?
Wrong! Wrong and thrice wrong. I love mind mapping for exactly this reason. Data architecture is difficult, resistant to change and defines one version of the truth . It makes my left brain hot. Mind maps are simple, fast moving and readily changeable. They fire up my right brain, get me creative. Let me see the whole solution.
What does this mean to you, the user?
A good data architect should spend a lot of time with the user, taking the requirement, asking questions, probing the solution. This is nothing other than a brainstorm and mind maps are perfect for brainstorming.
For example, I sat with a client recently who made their initial request by waving their arms. They drew an imaginary data visualisation in the air with their fingers. My left brain lit up because this was its territory: axes, a graph, data, information and hallelujah! The left brain solves problems and it wanted to get to work right there and then. But we didn’t do that. Instead we mind-mapped the solution. This woke up the right brain and once the right brain was going we unearthed many more questions. That client has the data visualisation they envisaged. Yet here’s the thing: they also have 318,239 other data visualisations that they can select from a handful drop down menus on a simple dashboard. All because we mind-mapped it!
I see brainstorming, and therefore mind-mapping, as the vital first part of taking any user requirement. A step that should happen way before any data architecture. A step that non-data people understand