Top data literacy skills for becoming data literate

It's estimated that the world produced 79 zettabytes of data in 2021 . That means that globally, we aren't struggling to create data: we're struggling to help leaders use it effectively. 

Data literacy is growing in importance for multiple reasons. On an individual level, career opportunities and job openings for data professionals are increasing. Contrary to how most of us have been educated, impactful learning is based on self-directed activity, hands-on learning and collaboratively playing with ideas. Most people become fascinated with the topic once they understand what it is and why it is important. 

On an organizational level, data is now one of its most valuable, renewable assets. There is enormous pressure to use their vast stockpiles of data for business insight, innovation, and value creation. The same data can be used and reused in different applications to fuel various projects and enrich multiple value streams. Lastly, Market Forces punish organizations that lag in these areas and trail their competitors in recruiting top talent and generating market value.

According to Forrester’s research, 82% of decision-makers expect at least basic data literacy from all employees in their department, regardless of level; 55% expect advanced data skills from the same group. 

Where to begin? The experts at Tableau are committed to spreading data literacy. That’s why we’ve compiled this list of both technical and non-technical data skills. No matter your current understanding, you can begin your data education.

In this article, we cover:

1. What is data literacy?
2. What are data literacy skills?
   1. Non-technical data literacy skills
   2. Technical data literacy skills
   3. Organizational data literacy skills
3. How to develop data literacy skills

Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value. When thinking about where to start your Data Literacy journey, it’s important to recognize what efforts are currently happening around the organization where data is ingested, managed, stored, analyzed, reported, and used to drive business decisions. Employees who sit at these points have unique data literacy needs and employee unique data literacy skills. Data literacy is important for all businesses because it delivers benefits such as employee engagement, increased productivity, innovation, and improved customer experience.

Data Literacy isn’t just a math skill–it’s a life skill. Data is everywhere. Nearly everything is digital and those digital things produce and consume data. We negotiate data by reading food labels, interpreting medication dosages, and when we are monitoring voting activities. Understanding data and making an informed decision is a skill anyone can learn, at any level. 

Literacy has become a loaded term, but simply put it means: can you ask answer questions about your business, and if you don’t know the answer, do you know where to look? Data Literacy starts with curiosity–and from there, being able to ask a good question. After enough questions, we get to a point where we understand the “permanent truths” of our area of inquiry. That’s when the “digging” begins, and we learn what supporting measures will drive impact.

Data literacy has several components, which together add up to someone becoming a data-literate person. Being data literate means acquiring an understanding of what data is and its characteristics (sources, types, formats, and data features), data applications (for analysis, business intelligence, data science, decision support, artificial intelligence, automation, and analytics), data techniques (such as pattern discovery, pattern recognition, and prediction), and data communication (for instance, storytelling, evidence-based reasoning, decision support, and visualization). 

We’ll talk about five key skills here such as critical thinking, attention to detail, and domain knowledge. Then there are also more technical skills for people who use data in an advanced way. These skills include things like calculus and statistics, data visualization, machine learning, and more. There are also a series of skills that entire organizations must develop in order to empower their employees to be data literate. That includes things like having a data culture, a blueprint for success, and more. Below, we’ve laid out all of the most important skills for those seeking to become data literate.

At a basic level, the non-technical data literacy skills include a basic level of self-education, as well as critical thinking, and high-level communication skills. Other such skills may include problem-solving, collaboration and teamwork, and more. Anyone can learn and refine the following skills, which will help when exploring, storing, and communicating with data. 

Arguably one of the most important skills to understand data, critical thinking is the ability to understand and explore the implications of data. Data literacy means not only understanding data, but knowing what questions to ask in the first place to get to your conclusions. It’s also important to examine the data and conclusions critically so that you can ensure its accuracy. If you develop your critical thinking skills, you can more easily think analytically about data and identify patterns, spot outliers, and draw conclusions that lead to actionable insights.

But how do you develop this skill? It can seem at first glance like an innate talent, rather than a skill. But that’s not true. According to Harvard Business Review , there are three habits you can develop to improve your critical thinking skills:

1. Question assumptions: Don’t take everything at face value. This can be anything from instructions you’re given, to values put forth by an organization. Question where those came from, and the value they bring.
2. Use Logic: When coming to a decision (or being presented with one), reason through each step of it to ensure you understand the logic behind the decision or conclusion.
3. Seek out diversity: Ensure you hear from people's viewpoints who don’t experience life the same way you do. Whether that’s people of other races, sexualities, or genders. Learning from their points of view will make you better able to see fallacies that may be present in your own point of view. 

Sometimes being data literate means researching your topic. Sometimes you need to find data sets that you can use in analysis and visualization. Or, you need to cross-check data. Or, perhaps, you just need to dive into the subject matter behind your data to truly understand it. Regardless, honing your research skills is important for anyone wanting to become data literate.

Research skills go far beyond simple Googling. It involves finding sources, engaging with it in meaningful ways in order to draw conclusions, and evaluating those findings after your research is done. Here are some ways you can start honing your research skills:

• Sourcing: Learn how to identify a quality source of information. And even if you find the information from a quality source, make sure you verify it with multiple trusted sources to ensure its accuracy.
• Narrowing: Start your search broad, with high-level sources. Then dig deeper into specifics as you learn more about the broad start.
• Biases: Don’t approach your research with a conclusion already in mind. Be open to the fact that what you find may not be what you expect. 

Being data literate means more than just understanding data. It also means being able to communicate the data and findings with other people around you. This may be in any number of formats – speaking, writing, presenting, or storytelling. 

Honing your communication is an important business skill in general, but especially when dealing with data. It’s important to ensure everyone involved in gathering, parsing, analyzing, and understanding the data is on the same page. Misunderstandings may lead to business issues further down the road. So here are some tips to improve your communication:

• Active listening: This may seem counterintuitive, but the first step to being a better communicator is to be a better listener. Engage with people speaking to you by nodding and asking follow-up questions.
• Public speaking: There’s no better way to develop your communication skills than by seeking out opportunities to speak or present in public. It makes you truly think about what you’re saying, and how you say it.
• Seek feedback: Whether you’re doing a speech, presentation, or telling a story, seek feedback from your audience or respected peers. They can tell you areas to improve and help you hone your communication skills.

The last of the soft skills on our list is a basic level of domain knowledge about data and data science. Anyone can learn how to interact with data, but knowing the basics and educating yourself will only help as you seek to become data literate.

Luckily, there’s an abundance of resources available for anyone looking to learn about data. You can find videos, webinars, blog posts, books, and more all over the internet. But because there’s an abundance of resources, we’ve spent time choosing some of our favorites. Here are some of our resources to learn more about data:

• Books: The tried and true resource of the ages, books are a great place to begin. We’ve compiled a list of the best data science books for beginners, so check it out to begin your education. Learn more. 
• Blogs: One of the best ways to get up-to-date information in a rapidly growing field, blogs are a great resource. However, since anyone with a website can write blogs, we’ve created a list of the best data science blogs for anyone to follow. Learn more. 
• Trends: One of the keys to being educated is to know what the newest data trend is. These can change rapidly. Our experts project that in 2022, some of the biggest trends in data research are in artificial intelligence (AI) and machine learning (ML), data ethics, workplace development, and more. Learn more. 

Now, of course, equally important to data literacy are the more technical skills involved in data analysis. These are numerous and include things like data management, building and maintaining dashboards or reports, data visualization, and various kinds of math or programming, just to name a few. Since there are so many, we’ve pared this list down to some major categories to cover a lot of ground. 

The first and most obvious category of data literacy skills is those to do with analysis and visualization. These are fairly basic data skills that anyone hoping to be data literate needs to master. We’ll go over the basics here, but these are broad topics that will require further learning to truly understand.

Data analysis refers to statistical, systematic, or logical techniques to data in order to describe, visualize, assimilate, and evaluate it. The process includes collecting, formatting, cleaning, processing, exploring possibilities, identifying patterns, and drawing conclusions from data. It’s the lifecycle of data in business. It can seem simple on the surface, but in reality, it is a complex process that involves many steps.

Data visualization is the graphical representation of information to provide easier analysis and more accessibility. Data visualization usually involves elements such as charts, graphs, maps, and more. These provide an easy and accurate way to help see patterns, trends, and outliers in data.  

Learn more about data visualization.

Data management is everything involved in collecting, cleaning, and storing data in a secure and efficient manner. The goal of data management is to allow people and organizations to interact with data in a secure, structured way. This includes (among other things), three major actions:

• Data cleaning: You may guess from the name, but data cleaning is the process of ensuring your data is accurate and formatted correctly. This involves removing incorrect, corrupted, duplicate, incorrectly formatted, or incomplete data. Learn more. 
• Data mining: Often confused with analytics or data governance, data mining is the process of understanding data through working with raw data. This happens through data cleaning, finding patterns, running models, and testing those models. Learn more. 
• Data warehousing: A data management system that uses data from multiple sources to promote business intelligence. Learn more. 

Now we’re wading into the highly technical end of the pool. If you want to really understand data and be able to interact with it in meaningful ways, it’s important to have a strong foundation in mathematics. In particular, linear algebra, calculus, and statistics will come in handy when it comes to using data programs, or perhaps programming your own data analysis.

• Statistics: Easily the most useful skill is understanding statistics and how to create models. In particular, make sure you have an understanding of distribution, mean, and standard deviations. 
• Linear Algebra: Some of the basic functions of linear algebra are the backbone of many data programs, as well as prediction-based programs, and even AI.  
• Calculus: Even a conceptual understanding of calculus will get you ahead when it comes to data. Make sure you understand the core of multivariate calculus as it works alongside linear algebra in many data functions and tools. 

If you want to take your data skills to the next level and start building dashboards, creating analysis programs, and making visualizations, it’s important to learn at least one programming language. Three of the most relevant for data literacy are the following:

• Python: Python is a useful programming language because it’s quite general-purpose and easy to learn. In particular, it has many applications including running both simple and complex analyses, and lending itself to AI and ML.
• R: Another highly-popular programming language, R’s syntax was originally created with analytical work in mind. It has some default data organization commands, unlike other languages that you may have to create your own.
• SQL: Structured Query Language (SQL) is a database language created to query data in relational databases. Consider SQL a baseline language that will enhance your ability to then use Python or R in the future. 

Now that we’ve covered soft skills and technical skills, we’re done, right? Not quite. You see, another important aspect of data literacy is ensuring that your entire organization has a baseline of data literacy. That way you’re all on the same page when it comes to decisions, adopting new software or protocols, and more. 

How do you ensure that your organization is data literate? Well, there are three important aspects to adopt. First, you need a blueprint. Then, you need to create a data culture at your organization. And lastly, you start using data whenever making your business decisions.

The first step to success in any endeavor is to have a plan of implementation. And why start from scratch when we’ve created our own blueprint for success? We understand that different organizations have different needs, so our process is designed around benchmarking and improving as it’s implemented.

The Tableau Blueprint process follows a set of 4 repeatable steps:

1. Discover: Take an audit of your business’ specific needs to help shape your approach.
2. Govern: Define all roles and the specifics of your approach in order to ensure a smooth roll out.
3. Deploy: Roll everything out, including installing and configuring any necessary software, investing time into educating your staff, and ensuring all your communications are enabled and working correctly.
4. Evolve: Monitor your program and measure your success. Engage all your users and ensure they understand their roles. Use your data to tweak and change your approach, and then start over at step 1 to roll out changes. 

Learn more about Tableau Blueprint. 

Arguably the most important part of a data literate organization is a fully developed and realized data culture. A data culture is a collective group of behaviors and beliefs of people who value, practice, and encourage the use of data in an organization. This helps to improve decision-making. This core-level approach helps to weave data into the mindset, operations, and very identity of the organization. 

But how to make such a drastic change? Follow Tableau’s data culture playbook, which comes in 4 chapters:

1. Align leadership metrics to business priorities.
2. Build data sources to address critical decision points.
3. Grow value through targeted use cases.
4. Promote widespread data discovery. 

Learn more about data culture.

This last one is both something that you need to consciously implement and something that comes from implementing the first two points. And that’s data-driven decision making, which means using data, metrics, and facts to guide the strategic business decisions to ensure they align with your overall goals and initiatives. 

There are six steps to effective data-driven decision making:

1. Identify business objectives.
2. Find key data sources.
3. Collect and prepare the relevant data.
4. Explore the data.
5. Develop insights.
6. Share your insights.

Learn more about data-driven decision making.

Now that we’ve touched on each of the major data skills needed to develop data literacy for yourself and your business, you may be asking – what’s next? 

The most crucial part is taking the first step. Take the leap today and start your education, either by taking advantage of the resources outlined above, signing up for a class or webinar, or buying yourself a few books. Don’t know where to start? Take advantage of Tableau’s Data Skills resources.