Data literacy explained: Definition, importance, examples, and more

In this day and age, data literacy is one of the most important skills a business or individual can have. Businesses depend on data-literate employees to drive them forward, and businesses need to build a thriving data culture in order to empower their employees. And that’s why Tableau has made a commitment to spreading data literacy wherever we can. Below, we cover the basics of data literacy including:

1. What is data literacy?
2. Importance of data literacy
3. Data literacy skills
4. Challenges of data literacy
5. Build data literacy with a framework
6. How to become data literate
7. Data literacy and data culture
8. Getting started with data literacy

So what is data literacy? The definition is: the ability to explore, understand, and communicate with data in a meaningful way. This can be on different levels: technically and advanced, or on a much more basic level.

According to a study we sponsored with Forrester Consulting , 87% of employees rate basic data skills as very important for their day-to-day operations. That same percentage of business owners expect basic data skills from their employees at all levels. Despite this, only 40% of employees feel they’ve been properly trained on the data skills they’re expected to have. On top of that, according to a study conducted by Accenture , companies lose an average of 43 hours per employee per year due to data-induced procrastination.

Not to mention the amount of data produced and cataloged grows by the day, and utilizing data becomes increasingly important for businesses to stay ahead of their competition. Creating a culture of data literacy at your company can provide many benefits, including:

• Better decision making
• Clearer understanding of ROI and attribution
• Increased employee satisfaction and retention
• Better customer experience and satisfaction

When you start saying “data” and “skills” in the same sentence, people can get intimidated. Luckily, there are data literacy skills that anyone can learn and master, regardless of their current knowledge level. We’ve divided these into technical and non-technical skills.

It can see like all the data skills are technical and difficult to learn, but in reality, many are completely non-technical and accessible to anyone who wants to build their skills.  This includes things like problem-solving, critical thinking, researching, and more.

Some of the non-technical data literacy skills include:

• Critical thinking: Essential for analyzing and understanding data, critical thinking skills are developed through questioning your assumptions, using logic to work through problems, and diversifying where you get your information.
• Research: Knowing about the subject matter of your data is critical to understanding it. You can develop this skill by learning how to evaluate sources, narrow your search, and spot implicit or explicit biases.
• Communication: A large part of data literacy is being able to communicate to others what your data is telling you. You can sharpen your communication skills by practicing active listening, working on your public speaking, and seeking feedback from trusted peers.
• Domain knowledge: And perhaps most important is keeping up with the industry and latest trends. You can work on expanding your data knowledge by reading books, following blogs, or researching trends.

Technical skills are, of course, equally important to developing data literacy. These range from relatively simple skills to learn like data analysis and visualization, to much more complex such as calculus and statistical programming.

Some of the technical data literacy skills include:

• Analysis: Data analysis is the statistical and logical technique used to interpret and evaluate data. It includes collecting, formatting, cleaning, and processing data as well as analysis and interpretation.
• Visualization: Data visualization is the graphical representation of information in different forms, such as charts, graphs, maps, etc. 
• Management: Data management is the entire process of collecting, vetting, and storing data. It includes data cleaning, data mining, and data warehousing. 
• Mathematics: If you want to really understand data on a deep level, you need to know the basis for its analysis. That involves learning about statistics, linear algebra, and calculus. Even a conceptual understanding of each will further your knowledge.
• Programming languages: If you want to build dashboards or complex data analysis programs, you need to understand and use programming languages. Some of the best for data work include Python, R, and SQL.

So what challenges can you expect when pushing for data literacy in your organization? You may encounter such challenges as your employees being resistant to change or new technology, there being a skills gap between your users, issues with data governance, and silos in your organization.

• User resistance: You may find people are resistant to new technology or processes, and don’t want to embrace change. Ensuring that you get these people onboard with the benefits will help you handle any such resistance and ensure success.  
• Skills gap: When training your team to handle new procedures or tools, you may find that some of your team already knows how to use it and some struggle to adopt. Ensuring a thorough education of new concepts and tools will help to eliminate this issue. 
• Silos: You must be careful that the people on your team who best understand data don’t silo into certain departments (such as IT or BI), but that each team has an understanding and can utilize data to the best of their ability. 
• Data governance: The more data your organization learns to handle, the better your data governance practices need to be. Ensuring you have best practices for every stage of the data governance lifecycle will ensure that your processes run smoothly and your data is accurate. 

Creating data literacy and a culture of data at your organization can be a huge task, regardless of how well-versed you and your staff already are in data. It requires a top-to-bottom approach, buy-in from stakeholders and contributors, and all around a lot of teamwork and effort. That’s why we recommend building a framework or blueprint in order to meet all your goals. 

1. Ask the important questions
2. Align stakeholders
3. Create employee journey map
4. Assess business needs
5. Fill out initial framework
6. Teach data skills

Of course, not everyone is in an organization trying to build a culture of data literacy. Some people want to build their own data literacy to further their careers and learn more about the growing ecosystem of data in the professional world. 

Other than working on learning the data skills we outlined above, how can you work to become data literate? 

• Sources: You need to know where the data you’re working with came from, and if it’s biased or came with any sort of agenda attached. Make sure you examine all data critically to judge its source and if its trustworthy. 
• Understand: Work on understanding the data you see and interact with in your day to day life. Do you understand where it came from? What it’s telling you? What importance it has? Can you draw any conclusions from it?
• Analyze: Once you know your data source and understand what it’s saying, you can analyze the data. Learn about statistical and analytical methodologies and common analysis used in your field of data study, so you can better understand data analysis.
• Learn: And, as always, we recommend furthering your data education. Learn the basics, learn current trends, keep up with leading experts in the field. 

We’ve already talked a lot in this article about how building data literacy at your organization means creating a thriving data culture. But what is data culture, and how can you create one?

A data culture refers to the collective behaviors and beliefs of a group of people who value, practice, and use data throughout your organization. This results in data being woven into the core of everything you do, from operations, to mindset, and the very identity of your organization. A data culture is characterized by three things: praciting data-driven behaviors, valuing strategy use of data, and creating community.

So how do you create a data culture at your organization? We’ve created a data culture playbook to help organizations move in the right direction. The playbook covers four important steps:

1. Aligning leadership metrics and business priorities.
2. Building data sources to address critical decision points.
3. Growing value through targeted use cases.
4. Promoting widespread data discovery.

Learn more about data culture. 

You know the importance of data, and having data skills and creating a workforce that values data literacy. Now is the time to get started. Start following the steps we outlined above. Work on building a data culture at your company. Create and execute against a data literacy blueprint. Follow data trends and stay informed.

Work with us here at Tableau to access personalized tools and expert support to make your company data literate.