Are your data visualisations too cluttered and confusing? I explain how you can simplify your visuals, whilst amplifying the impact of your data.
Okay, so today I’m going to be talking about data visualisation and why I believe we’re doing it all wrong.
Now, you think that’s probably quite a bold statement I’m making, but some of the reasons that I think we’re doing data visualisation wrong, is as follows:
Now, I really think we need to do something about this and start turning poor visualisations into great visualisations, and I believe it starts with delivering impact with the right visual.
Here, we have an example of a data visual with two data sets plotted across time. If I draw your attention to the title, it’s mentioning about correlation, so I would expect to see something similar across the two data sets. However, our visualisation, clearly, isn’t delivering that.
We can see, for example, the blue line has a seasonal peak, but the orange line looks relatively flat. Now, one of the main issues for that could be to do with the scale. Because we plotted both data sets on the same scale, we may not be seeing the true trends of the orange line at the bottom. Furthermore, in terms of our legend, that’s not overly clear either. For example, blue line says ice cream, but we don’t know whether that’s revenue or consumption or anything else.
There’s definitely a few things in this visualisation that can be improved, and we can convert it into something that looks a little bit like this. I’ll just talk through each of the key areas we’ve done here to try and improve the visualisation to really deliver on the insight, but also to make it clear and simple to understand.
In terms of the title, we haven’t changed too much there. We just added a small subheading, which relates to both the data sets so that they’re there and clear from the outset. We’ve done a lot of work on the actual visualisation itself. One of the key things is on the axes, so now we’ve split the data between each of the two axes. By doing that, it’s really brought out of trend of the shark attack data, which we put in a bar just for a nice bit of sort of alternative. From there, that really links back to our title as well, so now we’ve got a visual that’s really befitting of the insight that we’re trying to put across.
In terms of the legends, we’ve moved that from the bottom up to the top, but now, importantly, we actually have context with our data as well, so we know that we’re looking at number of shark attacks and ice cream consumption.
Furthermore, we’ve used effective use of colouring here as well. If you take note of the axes and the legend descriptions, they’re all coloured in the same way as the data sets themselves, so every time you’re seeing something orange, you now know it has to do with the shark attack data set, and everything blue has to do with the ice cream consumption data set. It’s just little bits there which really help make the visualisation simple for the audience but, importantly, really delivers on the insight as well.
We’ve covered visuals and how important that is. Next really important step is to cut the clutter.
Here’s an example of another visual, which is plotting sessions by day over the last 13 months. Now, if we take a step back and look at this visual, what we’re asking you, the audience, to do here is to digest around 400 points of data in a single visualisation. Now, that’s going to really, really difficult to achieve, and not only that, the chart itself looks very, very cluttered and very, very messy.
Furthermore, the headline that we’ve got for this chart is that sessions peaked by 643% in early March 2017. Now, realistically, that is a tiny proportion of the graph. The rest of the graph isn’t delivering in terms of the headline that we want to take across.
One of the simple things we can to, to start with, is to narrow the date frame, so let’s just say we look at March. Here, the visualisation brings it out a lot clearer. We can clearly see the peak. Actually, it’s a little bit easier to understand and digest. However, we can take this a step even further. Having had a look at the data, we could understand that all of that increase was coming from one channel. It was coming from direct traffic, and it was a result of some potential bot traffic coming into Google Analytics.
With that in mind, let’s make it even simpler. Let’s just focus on the direct traffic because we know that’s where it’s coming from. We’ll shorten the time period even more to make it easier to digest. Not only that, because we did that little bit of extra work, we’ve got a stronger insight come out, as well, so rather than just saying that sessions have increased 643%, we can now say that potential bot traffic caused a 643% increase in sessions. Not only have we simplified the visualisation drastically, to make it easier to digest and understand, but we’ve delivered a stronger insight off the back of it as well, which I feel is really, really important.
I’m also a very, very keen believer that simplicity in data visualisations add significant value, and it doesn’t have to be on chart-based visualisations like we’ve seen today. For example, quite often on a presentation, we’ll see something like this, which is promoting either a product, a service, or introducing a person.
Now, the format of this would be great if it was on a physical handout. However, in a presentation situation, this visualisation just doesn’t work well at all. One of the main reasons is it’s very, very text heavy, so you, the audience, are going to spend your time reading the text, and it’s diverting attention away from the speaker itself. Furthermore, in this particular instance, the visual just doesn’t need to be there. It’s cluttering. It’s taking up space.
What I’m going to show on the next slide is that I can drastically reduce what’s on the actual slide but give you more information than what’s on there at the moment. If we move on to this next slide, if we just focus on the title for the minute, just something really simple there: ‘Meet Neil, Your Digital Analyst’, so you now know who I am but, importantly, you know what I do, which you wouldn’t have done before.
In terms of all that text that was on the previous slide, it was broken down into three key pillars. One pillar is around academic, so I can put that down as a pillar, and we can mention Surrey University, mathematics and business studies, but I’ve also added an extra line of when that study took place, so between 1998 and 2002, so there’s an additional piece of information there.
The second piece was talking about experience; so again, let’s treat that as a separate pillar and put the key elements there, so GA certification, client and agency side experience, and been in industry since 2008. Finally, it was talking a little bit at the end of the text about interests outside of work. It focused around sporting interests. It mentioned about running, but I’ve split that out into more information, in terms of liking park runs and having an ambition to run a marathon, but also liking football and following Farnborough Football Club as well. So a nice example of that where less really is more if we use the visualisation in the right way.
Another important thing to consider with visualisations is to not lose sight of the insight. If we take this visualisation as an example, draw your attention to the headline: ‘Mobile Engagement is High for Those Under 35’. However, the visualisation is really, really difficult to understand. We’ve got three individual buckets of data: for desktop, mobile, and tablet. Then, for each of those, we’ve got a bar for each age range. Because of the way it’s been coloured, and you’ve got the data there in the buckets, it’s actually really, really difficult to understand that, for the insight, which piece of data am I trying to point you towards?
In this case, I’m trying to point you towards these two pieces of data to demonstrate the point. However, again, all of these pieces just aren’t adding value in terms of the headline that’s trying to be put across.
Let’s drastically simplify this whilst delivering some great insight. The first thing we could do is we could focus just on the under-35 audience, but split it between mobile and non-mobile engagement. Doing this changes things drastically. All I’ve done is just rearranged that data set, put it in a simpler format, and bang, we’ve got a really, really strong piece of insight now. We can see that mobile engagement is three times larger for under-35s than non-mobile, so the insight packs a punch, and the visualisation is so much cleaner and so much simpler to understand.
Similarly, we could just look at mobile engagement and compare that between under-35s and over-35s. Again, nice stat there. Mobile engagement is one-and-a-half times larger compared to 35-plus. Now we’re in a situation where we could use either one of those two previous visuals to demonstrate our point much better than the very first visual, but also, we’ve got a much stronger insight with some really impressive numbers behind it.
I’ve talked a bit about various examples of how we can improve data visualisations, but how do we achieve great data visualisation? Now, I think it’s a combination of all of these points here.
In summary, if I could just collate that together into a couple of sentences, I believe that great data visualisations enable your audience to fully understand the data being presented, and it showcases the insight in the simplest manner.
I believe if we can think about this ethos when we’re putting together our data visualisations, the world will be a better place, and we’ll have many more great visualisations to look at. Thank you.