DATA LITERACY EXPLAINED: Ep 1 &2
What is Data Literacy?
Hello and welcome to the series ‘Data Literacy Explained’! My name is Kiitan Olabiyi, and I’m the founder of DATA4FASHION. I started this Data Literacy series with a simple yet significant purpose in mind: to bridge the knowledge gap and equip people with the essential skill of understanding and communicating with data.
Through this series, I hope to demystify the often-confusing world of data and provide you with the skills necessary to properly exploit data insights. Join me on this trip as we explore the potential of data literacy and its real-world applications.
Why is data literacy important?
However, you may be asking why data literacy is so crucial. Data is present in everything, from the applications we use to the things we buy. Navigating this flood of information is no longer only for experts; it’s a talent for everyone.
From the line chart below, there has been a surge in the worldwide search for “data literacy” on Google, thus contributing to the increasing popularity of the term.
In the business environment, for example, 87% of employees consider fundamental data abilities to be extremely critical for their day-to-day operations. On the other hand, just 40% of employees believe they have received adequate training in the data abilities demanded of them.
According to Accenture, data-induced procrastination costs each employee 43 hours each year. Isn’t that a lot?
Let us go from our professional lives to our personal lives.
Assume you’re looking for a restaurant for a particular occasion. You may use data literacy to look at internet reviews, compare ratings, and even consider geography. You may also go through the 360 virtual tours to get a feel for the restaurant.
With such a data-driven approach, this decision-making process becomes considerably more informed and accurate. Let me dive into the core of what data literacy is and the technical and non-technical data skills you need, and wrap this up with an analogy.
Grab your coffee!
What is Data Literacy?
To answer this question, I would like to reference a few resources;
- Data literacy is the ability to read, understand, create, and communicate data as information. -Wikipedia
- Data literacy is a term used to describe an individual’s ability to read, understand, and utilise data in different ways. It doesn’t require an individual to be an expert. -Harvard Business School
- The combination of skills and mindsets that allows individuals to find insights and meaning within their data to enable effective, data-informed decision-making is called Data Literacy. -Data Literacy Project
From the definitions above, what do you find common? Not sure? Then read the three definitions again.
Here is how I would define Data literacy;
In simple terms, it is the skill set that allows you to not only understand data but also interpret it accurately and communicate insights effectively. It’s about being able to navigate through numbers, charts, and graphs, and make sense of the stories they tell.
The keywords here are;
- Understanding
- Interpreting
- Communicating
Data literacy can be at varied levels, from basic understanding to soft skills and advanced technical skills. This brings me to the next point.
Is data all about technical skills? Certainly not!
Beyond Technical Skills
Now, here’s the exciting part: data literacy isn’t just about being a technical wizard. While understanding tools and software is important, there are other data skills that are relevant to being data literate.
According to Tableau, Data Literacy skills are divided into technical and non-technical skills.
Let me briefly explain these categories
Non-Technical Data Skills
All data skills may seem technical and difficult to acquire, yet many are non-technical and available to anybody who wants to develop them. Examples of these non-technical data skills are:
- Research: Understanding your data requires knowledge of the topic area. This ability may be developed by learning how to assess sources, focus your search, and identify subconscious or explicit biases.
fun-fact; there are times when I get stuck while using Tableau or any of the data softwares, and run to google to research, what I need to do and how to achieve it in Tableau- don’t underestimate your ability to research!
2. Critical thinking: This is another essential skill that will help you convert your understanding of data into logic. It enables you to question assumptions, avoid biases, and draw logical conclusions from data.
3. Communication: What’s the point of being able to understand and interpret data without communicating it?
An important part of data literacy is the ability to communicate to others what your data is showing you. You need this to keep your audience interested and thinking about whatever you want to communicate with them. That is why it is referred to as a data-driven story.
4. Domain knowledge: It is hardly possible to make sense of data without understanding the industry the data is from.
I worked with healthcare data at times and had to spend time understanding certain medical jargon in the data. Other times, I find myself googling insurance terminologies, such as policies and premiums, and what metrics are significant to this business.
And, of course, fashion is easy for me to dive into since it is my domain. So it’s reasonable to argue that researching and staying current with the sector from which your data comes can help you improve your data literacy.
Alright, now is a good time to wrap up! Let me conclude with an analogy;
Think of data literacy as the skill of reading a map. Imagine you’re on a road trip, and you have a map with various symbols, lines, and labels. Being map-literate doesn’t just mean you can identify the symbols; it means you can trace your route, understand the distance between landmarks, and make decisions based on that map.
Similarly, data literacy equips you with the ability to navigate through data to reach your destination—better decisions and insights.
What Next?
You may be thinking, “What technical skills do I need to be data literate? Subscribe to my weekly newsletter to get the latest episode in your email.
Subscribe to my weekly LinkedIn Newsletter, Data Literacy Explained, to increase your data literacy — understanding, interpretation, and communication of data.
Until then, stay curious, stay engaged, and I’ll see you in the next episode!
To drop a feedback,review or need clarification, reach out on info@data4fashion.org.Connect with me on LinkedIn and Twitter
Cheers,
Fashion Data Queen
