Ok, maybe not so exciting to look at, but, according to All Things Considered, these manual tools got man on the moon! Slide rules were vital instruments for 350 years, helping scientists, engineers and spacemen make multiple, complicated calculations, such as finding square roots. You move the sliding pieces and the right answer just seems to appear. Now, I don’t understand the mechanics - even the curator of the MIT Museum calls it ‘magic’ - but I can understand that the slide rule gets you thinking about “math in geographical terms – you begin to know where the right answer is supposed to be.” So, in other words, what it does, what it represents, is a better grip on the relationships between numbers. It’s a hands-on way of understanding data, using a visualisation tool.
I have favourite examples of data visualisation - so I’ll add the slide rule to my list – and I collect them because of my frequent data dilemmas - How do I apply order to this data, which our systems keep producing, in the most communicative way? How do I draw out the most insight? How can I present this in the best way for this team?
Data analytics can seem like a disconcerting minefield to a fundraiser - but we all know that statistics, presented well, help us to make better decisions. Luckily, it's easier than ever for fundraisers to develop their data analysis skills – and we know there's so much insight we could generate and share. In this post, the first of a series about data, I’ll cover graph dos and don’ts, some handy free insight tools, and discuss ways in which we as a sector could dig deeper into data, starting with data visualisation.
The good, the bad and the ugly
Examples of beautiful data analysis are everywhere –
- #MakeoverMonday, where talented twitterers compete to create the most compelling designs
- Free, interactive dashboard tools like Google Data Studio or Microsoft PowerBI help you order your data
- The amazing Wikidata demonstrates cross-referencing, so we find unexpected delights such as a list of all cats ever named in wikipedia articles.
- More recently, we’re now accustomed to the frighteningly exact research of the John Hopkins University Coronavirus map.
This is data that helps us to understand, to make connections, and helps us to be safe. And we all take for granted that visualising data helps us make better decisions – from a weather map to a workout machine display. But how do we know which graph will work best in each scenario?
Here’s a case in point. It turns out that the ‘data community’ really doesn’t rate the pie chart – or rather, feels they have a far narrower application than is generally understood. Business Insider calls them 'easily the worst way to convey information ever developed in the history of data visualization.’ Wow. Did you know that? How many other data faux pas am I guilty of?!
‘Data dos’ and ‘data don’ts'
Here are some things I’ve learned about graphs:
- The best visualisations don’t seem to be the ones that attempt to show complex, multi-faceted data. Instead they pull insight out of a few, carefully chosen, lines of data – about three strands seems right: such as a line graph showing how visitor donations are influenced by guest footfall and weather conditions.
- It can help to consider ‘cognitive load’, especially if you’re experimenting with a new graph type, because unfamiliarity can mean you lose your impact.
- The number of colours you use (three again seems best practice), how many different graph types you use and how much ‘clutter’ you include (like data keys and labels) can all get in the way of understanding
- Think twice before making something 3D
- Graphs can also skew and manipulate data really easily, so there’s some best practice to follow for clarity and honesty as well.
Where my data visualisers at?
There is widespread demand for data analysis skills, and fundraising (and the cultural sector more generally) is probably not the first area new graduates with these abilities consider: in a survey conducted in 2019, a huge 95% did not connect the skills of researching and analysing data with fundraiser jobs. There is also pressure on arts and cultural organisations to catch up with this explosion of data-led decision making and huge interest in their creative projects. I don’t have an IT degree, and I have never worked for a technology company, but, like many fundraisers, working with fundraising databases has led to many data projects, and I’ve seen extraordinary things become clear through good data analysis. I've picked up skills along the way, and I interact with people who coax data in complex and insightful ways – but they are often outside of our sector.
However, opportunities to upskill within arts and culture organisations are increasing – the Turing Institute offers a Data Science for Social Good summer course – the Arts Council recently launched the amazing Digital Culture Network - and my own city of Newcastle hosts DataJam, where a wide-ranging group of participants pool experience and skills to solve real challenges.
Wrangling data can feel like an unfamiliar world but, as data visualisation is inherently creative, it’s such a fruitful space for collaboration - we should be in on the conversation!
What’s next?
Working with data can be experimental, collaborative and fun. Lets’ explore the wonderful world of data.. Here’s an example infographic, what do you think?
