Not everything that counts can be counted, and not everything that can be counted countsStephanie Miller
10 January 2019
“Not everything that counts can be counted, and not everything that can be counted counts”, William Bruce Cameron, 1963
In this era of big data, data-driven decisions and dashboards galore, the amount of data many businesses have available to them can be overwhelming, and filtering what is and isn’t valuable can be a huge challenge.
In location intelligence, the tyranny of choice that exists amongst open datasets can be overwhelming, and challenging to navigate.
In experience design, a poorly crafted survey with sparse results, given to the wrong group, produces data that is wildly misleading, and ultimately design that is unintuitive and doesn’t serve its customers.
In project delivery, without data that properly measures progress and success, a project can become a costly laundry list of features, tasks and defects with no prioritisation.
In marketing, dashboards loaded with metrics that don’t link to business goals, and don’t connect with other departments’ numbers, definitely don’t help measure impact or success.
Powerful, rich, valuable data, visualised poorly doesn’t pack the punch it should. And not all valuable data can be packaged into a visualisation.
A great example of an important, but famously hard-to-measure factor for all companies is company culture. Culture is a huge element in staff retention, successful hiring, cultivating a great working environment, and even brand. A multitude of surveys have been crafted to try and measure company culture, with wildly varying success. It’s incredibly hard to capture in a quantitative report the tensions felt between teams, the effect workplace layout can have on human dynamics, the combination of camaraderie, and exhaustion felt in a team after a hectic project delivery, or the subtle impact of working in a truly diverse workplace. Data on human emotion and moreover, how human interaction is perceived, is challenging to accurately capture quantitatively. However, it can be observed and experienced in person, and then effectively passed on .
The inverse is often true: too much data that’s being counted, and not enough insight from it that counts. In this scenario more than most, as the saying goes, a picture paints a thousand words, and good data visualisation can be the key to unlock what really counts. Here are a few examples of data visualisation that tackles that problem head-on.
- Here in Australia we are lucky enough to have a wealth of data made publicly available to us by the Australian Bureau of Statistics (ABS). Anyone who’s tried delving into the ABS census data for example, knows that there is an overwhelming amount of highly valuable data available, and a variety of ways to view it. For the majority of users, it’s a daunting prospect and doesn’t always lead to the insight that’s being sought. Indicatrix has broken this census data down to a standardised hexbin map across all Australia, and created population growth visualisation to enable users to quickly visualise Australia’s many growth hotspots. Users can then dig further down into the rich ABS data in a dynamic, browser-based map environment.
- Exploring equality factors such as the freedom individuals have to travel is a subtle, but revealling exercise. Christian Laesser’s Travel Visa Inequality visualisation allows users to easily understand relative inequality between all countries, as well as quickly comparing one country to another. Where a simple list of the data would have displayed the data, his visualisation brings it to life, and allows users to quickly interrogate it. https://projects.christianlaesser.com/travel-visa-inequality/
- WorldPoverty.io visualises the number of people living in extreme poverty globally. Backed by huge and robust dataset from WorldDataLab.io, here you can understand world poverty in numbers, alongside illuminating infographics, and emotive design elements that emphasise and honour the profoundly human elements of the data you’re visualising. Their copy line “making everyone count” couldn’t be better chosen.
These are just a few examples that stand out, but what they have in common is finding the questions that need rich data to answer them, being selective about the data that answers these questions, and what meaning their audience will take from the final visualisation.
If not everything that counts can be counted, and not everything that can be counted counts, we need to look to a balance between art and science, insight and empathy, as the secret sauce is unlocking the value in data for a wide range of audiences, and on the insights that count.