SNR: It’s my story, it’s my life, from the moment I left college to now. It’s the skill I needed to get a job where I could afford to put food on my table. Data skills have defined my professional growth and my ability to find a thriving career. I struggled for a long time to gain relevant skills that would help me to “thrive” and I’ve continued to nurture those skills ever since.

CH: I’ve always enjoyed engaging people in my career. I traditionally held roles in operations, or as a business analyst, where I got to work with different functional groups in my organisation. I wanted more face time with colleagues, and my love for analytics piqued my curiosity. I sought to change industry and function with my move from life sciences and IT, to my current role in software technology and sales.

AH: How can data literacy make an impact outside of one’s professional life?

SNR: Quality of life, hands down. Data literacy increases quality of life. When I used to teach Tableau in a classroom, I would frequently use my mom as an example. Whenever I was teaching a specific topic, in this instance histograms, I would think of how I would explain it to my mom. She doesn’t know this by the way, but she would be my baseline. If I could explain it to her, and she could come away understanding what that was, and why it was important, then I met my goal.

I think of data literacy the same way, especially with the baby boomers and aging populations. How are they going to navigate online banking or telehealth? All of these actions require a baseline level of data literacy. By knowing that, they increase their access to information and services, and be able to make better decisions.

AH: What will it take for organisations to become more data-informed?

SNR: Again, investing in people. Chris and I talk about how investments aren’t just financial, it’s about investing in people for them to gain the required skills that will be put back into businesses. Gartner says that we’re at the early stage of data literacy adoption, and that the need for data literacy is going to increase, peaking around 3-5 years from now. But it’s going to take a couple years to invest in a large community of people with proper education. The journey to being data literate is a movement and movements take time. No time like the present to get started.

CH: It’s definitely about investing in the employees and building out culture with an education state of mind. This means investing in skills but also giving employees the time and environment to do so. I see big companies taking steps to make their organisations more data-informed, but I encourage small and medium businesses to do the same. They need to invest in people to break into bigger markets. So opening up this window is advantageous to both the employer and employees.

AH: Data literacy isn’t one size fits all. What is bloom’s taxonomy and how does it illustrate this point?

SNR: Bloom’s taxonomy has secretly impacted your life and you didn’t even know it. Anyone, and I really mean ANYONE who has gone to school has benefited from Bloom’s. Simply put, it helps to build out learning objectives into increasing levels of complexity. If data literacy is about skills gaps, and to fill that skill gap we need education, then we need formalised models to help close that gap.

Bloom humanises data literacy and breaks it down in logical components of how people learn, and how we build on existing knowledge. To properly think of how Bloom’s taxonomy fits into data literacy, we have to think of how we learn, or something we have learned in recent memory. To some it’s running a marathon, to others it’s learning a language. I know for Chris, it’s learning to be a photographer. We do not start out as experts of our specific domains, we learn how to be experts in our specific domains and continue to grow on existing knowledge. That, my friends, is what Bloom’s does.

AH: Why do you think data literacy is often confused with data visualisation?

SNR: This is such a good question! First, I think of it like scotch, if anyone out there collects scotch at all. Whisky can be made anywhere, but Scotch whisky can only be made in Scotland, so there’s an axiom regarding scotch: “All scotch is considered whisky, but not all whisky is scotch.” It’s the same for data literacy when we break down the components of it. “All data visualisation is data literacy, but not all data literacy is data visualisation”.

There’s not an equivalence between the two, but data visualisation is part of the data literacy journey. That’s why it’s important to think of data literacy in terms of people, because no two people will be exactly the same. Everyone’s journey is going to be different, so the starting points are going to be different.

CH: I have a personal story to share. When I was looking to move over into the data space, I wanted to show off my DataViz skills. I thought data literacy was building a “nice viz” and showcasing it. So I worked on my modern dashboard with all of the bells and whistles displaying the company’s metrics. When I presented it to the entire team they found it fascinating. One of the managers commented, “It’s definitely easy on the eyes, but what do I take away from this?” There had been no actionable takeaways or recommendations.

AH: Sarah, What is the key takeaway that you want people to receive from your presentation?

SNR: Awareness, understanding and empathy. To see the world of data literacy beyond the upstream impact in a corporate setting, that’s what is needed. I want people to think of the downstream impact, and the people and faces who need data literacy today. I want them to see that each person we know today will either be a) an advocate for data literacy or b) a person who needs data literacy.