Data analysis – jigsaw puzzling writ large?



I do love a good jigsaw. The more complicated the better. Tiny pieces. Ambiguous shapes that could be one of any number of things. Large slabs of mono colour. What’s not to like?

And over Christmas I got hooked on the digital jigsaw puzzle. No more analogue piles occupying the dining room table for weeks. No more accidental knocks destroying several day’s work. No more sideways looks at this quaint old fashioned pastime.

But… it’s not all good. The ipad jigsaw app I’ve acquired really chews through the battery and on a particularly hard puzzle I have to recharge midway. The other down side is that when you combine screen size with the need for the pieces to be visible to the naked eye, there is an inevitable limit to the number of pieces in any one puzzle. The equivalent of the enormous tablecloth size piece this is not.

However, the digital jigsaw has an added bonus, a big plus over analogue. You don’t have to have a picture to guide you. That’s right. Nothing to guide you. Unless you hand over actual money, or some bit coin equivalent, you begin with a lot of pieces and no idea what the end product is going to be. I realise that this would drive some people crazy, but I really like the process of putting just enough pieces together to get to a point when I can imagine  the final image.


Of course, you’d likely never get to the point where you could recognise the subject unless you had a system. You have to follow the grammar of the puzzle. This usually means putting the outside frame together by finding all the pieces that have a flat side. Corner pieces are particularly important as they can help to work out the flow on each side. Once you’ve made the edges, you then have to choose something to build up.

Because I work largely with colour and line, I usually choose something that seems smallish, working inward from the frame. I like to find something that goes from top to bottom or side to side. I work by colour and line first but eventually by shape. I’m not so good at shape. I’m really good at colour and line so I rely on that a lot. At some point, I know I’ll have to sort out pieces of the same colour – where there is no recognisable difference – by shape.

So you can see my system – find and make the border, compile the more obvious sections by colour and line until you can see the big picture, then put the pieces for each section of the picture together. I then continue working section by section rather than scatter-gunning all over the image. I pick off the sections that look more obvious first, leaving the most difficult to the end. This way, I have the reward of seeing the image filling out, rather than being faced with bits everywhere, bits here and there.

The jigsaw puzzle is all about looking, being systematic, and being able to imagine a completed work. It’s also about an eye for detailbeing patient and not being daunted by something that looks completely incomprehensible at the start.

Do you recognise this? Sound familiar? There is something important about this piecing together process – it’s a lot like dealing with (any kind of 0f) research data.

Now – reading warning – this isn’t a metaphor. The jigsaw isn’t a terrific metaphor for research IMHO. Usually, research data doesn’t arrive with a predetermined border; the researcher has to find/make/sort-out what the edges of the research actually are. And there are often not right answers. There are generally multiple interpretations possible in social science and humanities research – lots of possible final images that can be made from the data. There is judgement to be exercised in putting together the pieces of the analytic puzzle.

No, when I say research is like a puzzle what I actually mean is the material practice. The process of solving a jigsaw puzzle is a lot like the process of data analysis.

To start with both the jigsaw puzzler and the researcher have to have a systematic approach. This may mean you construct the borders first. Or it might mean putting bits of things together in order to determine possible avenues for analyses. Or it might mean taking a frame from the literature and sorting data according to predetermined categories. Or it might mean following an analytic convention. It doesn’t matter. What does matter is that you have a system – and you can describe the ways in which you worked through the data. You also need to be able to provide a rationale for the system of analysis.  (Just as I can describe how I approach a jigsaw.)

And there’s more. I think that the embodied practice of jigsaw puzzling is a lot like the embodied practice of analysing data. You have to be prepared to face something that looks like there is no way it can come together. You have to be patient and understand that it takes time to work out what the big picture is – it doesn’t necessarily come quickly or easily. You have to have a tolerance for ambiguity – things that might fit in multiple places have to be held onto until you can work out where the best place for them is. You also have to sweat the small stuff, the tiny differences.  Above all, you have to be relaxed, accept that this isn’t going to be a quick process. In fact, you’re likely to think its in-soluble and undo-able several times before you’re done.

Ideally, you get to the same point with data analysis as dedicated jigsaw puzzlers do. You understand and accept that it takes as long as it takes, and as long as some progress is being made – and that may be just working out what doesn’t work – then it’s all good. You stick to the system and trust in it, or you change the strategy, but then make it systematic. And you enjoy the process. No. You relish the prospect of making sense of a big pile of stuff – of bringing order to something that appears to be random and arbitrary, of making a picture at the end that is both recognisable and pleasing.

These puzzle/analysis behaviour and attitudes are dispositional. And I suspect that there might be some resonance between a disposition for puzzling – not just jigsaws, but any game which requires sustained engagement, concentration, strategic thinking, imagination and persistence – and the practice of research. I don’t think that playing games necessarily makes you better at research analysis of course, but I do think that the correspondence between the two practices is – well – interesting!