In a series of posts over the next few weeks, and in response to various questions I’ve received both on social media and in person, I am going back to basics to try to define some of the key topics and areas of research and activity that are fundamental to what we do at Umbrella.
To start off, let’s talk about data. Data is so foundational to our philosophy at Umbrella that it feels like the obvious place to start, and like all good literature and language students my understanding of a concept always begins with a definition. The OED defines data as “facts and statistics collected together for reference or analysis,” a pretty reasonable starting point but as so often with definitions, one which leads on to some important further questions and ideas. So let’s try to unravel that a bit, and also to think about the implications.
Firstly, data is about facts; in other words, things we can observe or prove to be true, as opposed to interpretations or beliefs. And this is really critical when we are trying to address difficult and emotive issues; almost everyone has a view on the importance (or otherwise) of diversity and inclusion — but often this is based on values and beliefs, and the facts get lost. This is not to downplay personal experiences (and I’ll talk in a later post about qualitative and quantitative data and how they interact), but it is to point out that things we believe and things that are observably true are sometimes not identical; more than that, it is often the case that people will believe things that are demonstrably false. Think about climate change deniers, and the furore when BBC news repeatedly gave airtime to their views, ultimately leading to a new set of guidelines warning against giving “false balance” to the climate change debate, in other words stopping them from giving airtime to people whose beliefs were in contradiction of the scientific facts. Similarly, I am often faced with people who don’t “believe” that workplace gender discrimination is an issue, despite the ever-growing evidence base that being a women still restricts your career prospects and earning potential when compared to being a man. In these scenarios, we need to go back to the data to prove what is observably true, and data is a way that we can do that and work to overcome some of this resistance to change.
But data is not just about facts, it is about collecting facts together to a specific end; for reference or analysis, as the OED definition tells us. This is really important to understand, because data shapes the world around us. Data analysis helps governments decide how our taxes get spent (although, of course, beliefs also have a strong role to play here…), provides guidance to businesses on how to allocate resources and where to invest and divest, enables researchers to identify successful treatments for illness, and so on and on and on.
Of course, these types of statistical analyses are much derided by some: “There are lies, damn lies, and statistics,” as Mark Twain, or maybe Disraeli, once said. And undeniably, data can be manipulated and twisted into telling the stories that we imperfect, irrational humans want to tell. But this is not the fault of the data; data is what provides the building blocks on which knowledge and — ultimately — wisdom are built, but how those blocks are constructed into an edifice of knowledge is down to us. So don’t blame the data, blame the way that data is translated up the pyramid; this is why I urge everyone to become more data literate, so that you can understand what data is really telling us, and also when and where it is being used and misused.
I’m passionate about data; I believe it is the most powerful tool we have to counteract bias and prejudice, and to prove unpalatable truths that we may otherwise want to turn a blind eye to. But data and how we use it is a big, complex topic, to which I’ll return in future posts.
If you’d like to vote on what I should blog about next, please click here and let me know. Let’s use data to see what you are all most interested in hearing more about :-)
Nancy is the founder and CEO of Umbrella, using data analytics and AI to improve workplace diversity and inclusion. Follow her on Twitter @umbrelladata and learn more about Umbrella at umbrellaanalytics.net.