Mastering data literacy: Developing data literacy skills

We’ve known for more than a decade that data-driven decision-making measurably improves professional output and productivity. To begin making business decisions based on data and business analytics, decision-makers need to be data literate.

In short, data literacy is a fundamental skill in today's data-driven world.

MIT Sloan senior lecturer Miro Kazakoff explains that there are three core competencies necessary for data literacy. “You have to be verbally literate, numerically literate, and graphically literate.” That’s because data fluency requires the ability to use words, numbers, and visualization techniques. With these skills combined, you can understand and communicate with data.

Which exact skills will data literacy entail for you? That depends on what types of data you work with and how you need to use the data. Continue reading to learn how to assess your data competencies, develop your data literacy skills, and overcome any barriers you and your organization may face when it comes to building data literacy.

Introduction to Developing Data Literacy Skills

Building data literacy can seem intimidating to those of us without a data science background. It can help to remember that data is everywhere, and we all have everyday data literacies. Think about it: if you’re a driver, you literally look at a dashboard every time you get in your vehicle.

Every driver learns to read the various gauges on their dashboard — at least, we hope they do. A vehicle’s dashboard provides at-a-glance information about the status of the vehicle, its operational functions, and its maintenance needs. It alerts you if there’s a problem and
helps you make decisions like when to stop for fuel.

If developing data literacy skills for work sounds stressful, build your confidence by identifying your existing, everyday data competencies. You might be surprised by how much you already know.

Everyday examples of data literacy

We can all find data competencies somewhere in our lives.

A seasoned traveler will know how to read arrival and departure signage at an airport, and understand when (or if) to expect their flight. Knowing about factors such as storms or holiday rushes can add important context.

A serious baseball fan will understand all the information encoded on a baseball scoreboard. For example, this game is over. It ended at the top of the ninth inning, with the home team beating the visiting team 4-0.

Weather maps have been a vital data visualization tool for decades. Most of us can recognize a local map and understand what the weather symbols represent. This station’s viewers will know from context that the numbers are temperatures in Fahrenheit. A meteorologist will typically explain more abstract graphics, like the blue and red lines representing low and high pressure systems on the map above.

Data literacy needs vary

A high level of data expertise is required for understanding the algorithms behind machine learning, or for starting a career in data governance or data management. But it doesn’t take advanced data science knowledge to be data literate.

Instead of getting overwhelmed by how much there is to learn, focus on the skills that are appropriate for your career path. Do you need data analysis and data visualization skills? Will you need to tell stories with data? What will help you understand data that’s relevant to
your job? What skills do you need to develop in order to communicate with data? What skills will help you make better decisions based on data?
 
Most business professionals should learn how to represent data with a simple graph. A graphic designer can be expected to have deep expertise in data visualization. If you want to learn how to start making data visualizations, consider what level of skill will meet your needs.

Setting expectations for data literacy

Professionals, managers, and students may all have strong motivations to improve their data literacy skills, but they will all have different needs.

Students and early-career professionals should focus on critical thinking skills and learning to understand the language of data. You can learn a lot with common tools including Microsoft Excel and Airtable. Don’t just rely on online courses. Make sure to practice technical skills with real-world applications. For example, if a project requires a report with metrics, go above and beyond by adding infographics.

Professionals who want to improve their decision-making ability may have more specialized needs. If you regularly use any data analysis software at work, a first step is to make sure you’re familiar with all of its features. With a quick search on LinkedIn Learning, Coursera, or Udemy, you can find countless training programs for business intelligence tools like Tableau and Looker. Professionals should also take advantage of data literacy initiatives and upskilling programs their employers offer. If your company doesn’t have a data literacy training program yet, let your business leaders know that it’s important to you.

For managers and management-track professionals, fluency in the language of data is a must. After all, management is all about optimization. You can only optimize when you have trusted data and data-driven decision-making skills. As they say, what gets measured gets managed.

Understanding data literacy skills

“Data literacy has always been a requirement in successful organizations. It's just that data illiteracy is more obvious now — or data illiteracy just causes more damage now than it used to,” says Kazakoff, who teaches about communicating with data. “It’s a challenging skill set to build for organizations, because data literacy requires people to perform at a high level and master a set of foundational skills that haven't always been taught together.”

Kazakoff has said these three literacies are components of data literacy skills:

1. Verbal literacy — what we might think of as traditional literacy skills, the language skills to fully understand and exchange information
2. Numerical literacy — also known as mathematical literacy or simply numeracy, the ability to interpret and communicate mathematical information
3. Graphical literacy — also referred to as visual literacy, the ability to extract, understand, and communicate information presented from graphs, maps, charts, tables, timelines, and other diagrams

Data literacy skills aren’t just valuable on the job, either. Since data is everywhere today, data literacy is important to many everyday literacies. Your day-to-day media literacy, advertising literacy, and health literacy skills all depend on your baseline ability to understand data.

Developing data literacy skills

Think of data literacy development as an ongoing learning process. The data literacy journey benefits from a growth mindset. In other words, any skill you lack is just something you haven’t learned yet.

Using the metaphor of “information language” as a second language, Gartner defined 5 levels of proficiency for data literacy skills:

1. Conversational: understanding data, analytics and use cases at a basic level
2. Literacy: communicating, writing, and engaging with data, data analytics programs, and data use cases
3. Competency: designing, developing, and applying data and analytics programs
4. Fluency: fluent in information language across most business domains within and industry vertical
5. Multilingual: fluency in information language across multiple business domains

To improve your understanding of data, you need to learn the languages of data. As with learning a new language, there are many methods for developing data literacy skills. Learners can seek out formal training programs, explore online resources, and find opportunities for hands-on experience.
 
The language of data also grows and changes like any language. And, as with any language, ongoing learning and improvement are the key to keeping your data literacy skills up to date. You couldn’t learn another language from an old textbook and expect to keep up with young peoples’ conversation. The same is true of data literacy. To keep up with data culture, you must always keep practicing and learning your data literacy skills.

Data literacy training programs

If you’re learning data literacy skills independently, you can find a lot of resources to support your journey. Whatever area of data literacy you’re focused on, you can probably find a wide range of training options from casual, free online courses to intensive data bootcamps. To learn more about data literacy learning resources, read our ultimate guide to data literacy training.

Corporate data literacy programs don’t just teach data literacy skills. They also build a unique data culture that serves the needs of your organization. To learn how kick off a data literacy training program at your workplace, read Data literacy framework: A guide to creating an effective data literacy program

Online resources for data literacy

You’re not the only one interested in learning data literacy skills. Data literacy courses are popular on every online learning platform. Take a look at these top online courses about data literacy:

• Data Fluency: Exploring and Describing Data on LinkedIn Learning
• The Data Literacy Course: Learn How to Work With Data on Udemy
• Data — What it is, What We Can Do With It from Johns Hopkins University on Coursera

Hands-on experience for data literacy

Beware of online courses that expect you to learn passively from videos. Look for courses that involve practical projects. Some courses will provide access to tools and environments you’ll need. In other cases, you may be able to use a free trial or download open source software.

Applying data literacy skills

Classes and practice projects are one thing, but let’s talk about the real-world applications of data literacy skills. Once you’ve learned a skill, it’s time to flex your data literacy knowledge with practical applications.

Think of questions you’d like to answer with data — at work, or just for fun. At first it might be hard to think of real-world projects that test your newfound data skills. Try to think of questions you wish you could answer, and what data might help answer them. Here are some ideas:

• Look for interesting patterns or correlations in publicly available data sources
• Start making your own infographics and publishing them on LinkedIn
• Build your own tools and dashboards, for example by pulling your FitBit data

Overcoming barriers to data literacy

A few common barriers can prevent individuals and organizations from developing data literacy. Barriers typically fall into two categories: lack of motivation and lack of access.
Lack of motivation is probably the larger hurdle. Some still fail to recognize the value of data literacy. Maybe it seems faster to go with your gut than to wait for data analysis. Or maybe a lack of data trust keeps leaders from making data-driven decisions confidently.

If you’ve read this far, you clearly see the value of data literacy — but maybe your management needs convincing. You may need to make a business case for data literacy skills training at your organization. Fortunately, the benefits of data literacy in business are clear and well-supported. It’s easy to find reputable sources to build the case for investing in data literacy tools and training.

• Forbes: The importance of data literacy and data storytelling
• Harvard Business Review: Coding isn’t a necessary leadership skill — but digital literacy is lack of access can be a major barrier to data literacy in an organization, even when the value of data literacy is understood
• Lack of access to data analytics tools — if data is inaccessible or if using data is too cumbersome, employees won’t be motivated to build data skills
• Lack of access to enterprise datasets — without the right tools to access and share data across teams, employees can’t develop a shared data culture data

McKinsey advises organizations to modernize data infrastructure to ensure data is ready to use in flexible, integrated data stores. Watch this video to see demonstrations of how Talendians use Talend’s own products to access enterprise data and build data literacy skills.

Conclusion and further resources

Analysts and leaders agree, we should prioritize data literacy skills. Big data is here to stay, and we would all do well to learn its language. So develop a baseline data literacy, and expect to continue learning data literacy skills throughout your life and career.

Data literacy is important in daily life, too. We all need to understand data, question data sources, and reject bad or biased data. Improving your data literacy skills will help you make better health decisions, invest wisely, and even compare sports teams.

There are endless advantages to growing your data literacy skills — and there is no disadvantage to investing in data literacy skills for your team. In fact, companies have an opportunity to lean into data literacy skills to improve job satisfaction. Employee training is a recognized retention strategy, especially when it’s framed as a career-long employee development program.

According to a recent Forrester survey, employees across all departments identify basic data skills as the most important skills for success in their roles. Only 40% of employees said their employer had provided training on the data skills they’re expected to have.

If you’re thinking of building or expanding a data literacy program at your company, get advice from a Talend expert today.

FAQ

How do you develop data literacy skills?

What makes data literacy assessment and training so complex is that data literacy isn’t a one-size-fits-all skill set. Data literacy requires different data competencies for different people. This will depend on the data someone works with, since every industry and every field has its own relationship with data.

First, identify what types of data are important for your current role and your career goals. Perhaps you should focus on learning every aspect of Microsoft Excel, or maybe it would give you a boost to learn a programming language like Ror to improve your infographic design skills.

Once you have a direction, look for learning opportunities that will keep you interested and motivated. Many data literacy skills can be self-taught or learned with the help of software tutorials and free online training videos. If you learn better in a classroom setting, look for more traditional courses.

For more information and links to training resources, read our ultimate guide to data literacy training.

Which set of skills is included in data literacy skills?

The key component skills for data literacy are knowing how to read and communicate about data with numbers (numeracy), with words (verbal literacy), and with charts and graphs (graphical literacy). You’ll also need analytical skills to ask the right questions and understand how data can answer them.

Beyond that, data literacy skills vary as much as language skills. English-speakers can be literate whether or not they’re poets, and businesspeople can be data literate without becoming data scientists.