There are so many definitions of data literacy out there. Depending on the company, job role or industry, it can mean different things to different people. But they tend to have some key elements in common - the ability to read, work with and analyze data, and crucially, the ability to challenge it.
As I explained to Joe DosSantos on the latest episode of Data Brilliant, for me, the most important part of this definition is knowing how to use data to make informed decisions. And in what is perceived by many as a technical discipline, many try to achieve this by improving their ‘hard’ digital skills. In fact, developing soft skills is equally important to ensure you are interpreting data accurately.
From critical thinking to emotional intelligence; active listening to mitigating bias; these all play a part in helping us understand data in its full context. Think of it like an escape room – you need to use all sorts of skills to reach the end goal, and often there is more than one way to succeed. The process isn’t black and white – everyone in the room has valuable input to the task at hand.
Diversity in data
As Joe and I discussed, the best way to capture balanced perspectives is ensure a team has cognitive diversity. We know that diverse teams perform better, and that is true when it comes to analyzing and interpreting data too. The more people involved, the wider the range of perspectives you’ll benefit from.
I spoke to Joe about my experiences of having ADHD and how earlier in my data career, people often thought I wasn’t engaged in meetings. But it’s more that I process things differently and look at them in a different way. And that’s not a bad thing. Data interpretation needs more people who think differently.
My son has autism, and I’ve been on this journey with him too. I’ve learned not to tell him that he’s doing something wrong because he’s only doing it differently. And when he’s explained his thought process, it’s amazing how many times I’ve learned from him. It’s made me more receptive to broader ways of thinking, including with my other kids. This should be applied in business too. When it comes to interpreting data, no one is right or wrong. Rather, challenging each other’s views and welcoming more perspectives will, ultimately, lead to more informed decisions and improved outcomes.
The power of asking, “why?”
Unfortunately, this ‘challenge culture’ is not encouraged at school, where we’re taught to accept data at face value. After all, 2 + 2 = 4, right? The sum itself might be an absolute truth, but data literacy is about the interpretation of the numbers. It’s about what this sum means to you – and what it could mean to others. It’s like picking up the first piece of a puzzle and getting a glimpse of the full picture.
When we’re young, we constantly ask “why?” My own children say it so often, but I encourage them to ask more. This curiosity is trained out of us through education. We’re taught not to challenge; to listen to our teachers, lecturers or professors and accept the lessons they share. But, in reality, this creates a culture where questioning isn’t the norm – and that needs to change.
This is a lesson businesses worldwide are still learning. Organizations are facing a major data skills gap, as today only a shocking 11% of employees globally feel fully confident in their data literacy skills. Often, that’s because that culture of data curiosity isn’t effectively fostered. This is what led me to write, Turning Data into Wisdom, which introduces a 12-step process to help anyone use their knowledge, skills, and experience to make data-informed decisions.
From creators to curators to consumers – it’s vital to ask questions and exercise those soft skills that we all have but aren’t naturally applied when working with data. We need to question everything and keep digging deeper to make the most of data and uncover the valuable insights within.