We live in a digital era where 1.7MB of data is created every second for every person on earth. Amid this exponential growth, it is essential to understand how to work with data. We hear the phrase Data Literacy a lot as a term to neatly summarize a rounded data skill set, but misconceptions remain over what Data Literacy really is.
These misunderstandings prevent businesses from supporting employees to maximise the potential of the data at their disposal. It might lead them to invest in technology to this end, or even educate some of their employees on various aspects of working with data. But very few put in practice the upskilling that employees really need to increase their Data Literacy.
Even though there are many definitions, a common version I hear is: “the ability to read, work with, analyze and communicate with data”. While this definition is helpful, it misses some key components. For example, it only talks about actions rather than outcomes and the things that Data Literacy enables us to do. The outcome is gaining insights from data so people can make decisions and take actions that add value. Otherwise, Data Literacy is not just an “ability” that people have. It is more than that, including both skills and mindsets. The key distinction here is that skills can be gained via training or experience rather than something you are born with.
Data Literacy also requires individuals to not just have various skills, but also a mindset for approaching data. Mindsets relate to our views and beliefs on certain topics and our willingness to apply them. For example, someone may have the skills to analyze data, but if they don’t have the mindset to be open to diverse perspectives, it’s a moot point.
Based on this, a feasible definition of data literacy would be the combination of skills and mindsets that allows individuals to find insights and meaning within their data to enable effective, data-informed decision-making.
Another common misconception is that data literacy is a one-size fits all approach. To the contrary, the range and level of skills required for individuals varies depending on their role, domain or industry they are working in, or the size of their organization and even the stage of their career. The skills required for a consumer of data to achieve basic Data Literacy competence are different than those required for a producer of analytics.
Of course, creating the environment foster Data Literacy goes beyond the individual. There are also organizational components required to make data-informed decisions. Businesses require the right culture, the right organizational processes and the right tools and technology to create a data-led culture. But ultimately, having a data literate workforce is undoubtedly the foundation of a data-informed organization.
To find out more about data literacy and how to master it in your role, you can sign up to my Data Literacy Master Class here.
You can also find out more about the selection of courses available from Data Literacy Project partners, via our Learn Page.