Data literacy: What is it? And why is it important?
What is data literacy?
Data literacy has become a hot buzzword as organisations across industries adopt big data — but data literacy isn’t just a trending term. When you look behind the buzz, the push to become more data literate makes a lot of sense.
To understand how fundamental data literacy is, consider the meaning of literacy itself: the ability to read and write. Data literacy is the ability to read and write in the language of data.
Literacy is often used to describe deeper subject-based competencies. For example, cultural literacy is critical for working effectively with partners around the world. Financial literacy is necessary for managing budgets. In the same way, we can define data literacy as the skillset needed for understanding data and making data-driven decisions.
Achieving data literacy can be tricky, because as MIT Sloan senior lecturer Miro Kazakoff points out, being data literate involves other competencies. “You have to be verbally literate, numerically literate, and graphically literate.”
Data literacy examples
What does being data literate look like in practise? Data literacy is less about specific technical knowledge, and more about having the general skills to use data. That means it can look different for everyone.
Some everyday business examples of data literacy skills include:
- Creating spreadsheets and databases in tools like Microsoft Excel or Airtable
- Understanding and filtering web traffic reports with web analytics tools like Google Analytics
- Reading business intelligence dashboards from BI tools like Tableau and Looker
- Choosing the right type of chart for data visualisations in your LinkedIn post
- Knowing how to spot misleading graphs and cherry-picked data
- Building data-driven reports to help decision-makers make better decisions
- Communicating data with effective storytelling
If these skills are missing from your workforce, your organisation has data literacy skills gaps to address. Fortunately, upskilling employees with limited data literacy is possible.
Good data literacy initiatives focus on building data culture. This helps everyone at an organisation, regardless of their role, use data as a common language.
What does it mean to be “data literate”?
Data literacy training is valuable at every point in a learner’s journey. That's because developing your data literacy can help drive success in any field.
What does being data literate mean for your average person across their career?
- For students: Learning the language of data means learning how to record data, interpret data, and communicate data with others. Data analysis is not only for science, technology, engineering, and math (STEM) classes. Computer science, social studies, health, geography, and more all rely on data. Data literacy will give students an easier time finding success in a big data economy.
- For educators: Being data literate helps teachers become better educators. Every assessment is an opportunity to compare the growth of students’ understanding. Student data ranging from attendance to notes on student motivations and behaviour add context. This all helps teachers understand and support each student’s success.
- For professionals: Being data literate helps employees advance their careers with data-driven decision-making. Data fluency gives professionals the ability to assess situations based on available datasets. Data communication skills also reduce errors and improve collaboration with colleagues.
Why is data literacy important?
Data now seems as common as the written word, which makes data literacy as important as literacy itself. Today, literacy — reading and writing — is a fundamental, day-to-day skill. But consider that according to the World Economic Forum, the global literacy rate just 200 years ago was only 12%. Today, most developed countries have a 99% literacy rate.
Talend’s 2022 Data Health Barometer shows that 99% of companies recognise that data is crucial for success, but 97% face challenges with their data. Being data literate means being able to make better assessments and data-driven decisions. It’s no wonder, then, that 65% of companies report that they’ve started a data literacy program.
Understanding data opens the door to working efficiently, finding new opportunities, and reducing risk.
What are the three key aspects of data literacy?
For a more detailed look at what data literacy means, we can turn to Gartner’s data literacy definition. Gartner says, “Data literacy is the ability to read, write, and communicate data in context,” according to Gartner’s IT glossary, “with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case applications and resulting business value or outcome.” Based on Gartner’s definition of data literacy, we can identify three key aspects of data literacy. Data literacy means understanding:
- Data sources and data constructs — where is the data from, and how is it organised?
- Analytical methods and techniques — this includes machine learning algorithms, but can be as simple as deriving a mean from a set
- Use-case applications — being able to apply data-driven insights to create value is the driving principle of data health
Data literacy framework
An investment in data literacy skills is an investment in organisation-wide success. A data literacy framework should be the skeletal plan for upskilling your workforce. Just as data literacy skills vary from person to person, data literacy projects will look different from business to business.
What does data literacy mean at your organisation? What data-related tools and data skill sets are necessary to create a common language around data? What skills gaps need to be addressed? Answer these questions to know where to invest in upskilling your employees.
Get started with our guide to developing a data literacy framework.
Data literacy training
In 2022, 65% of companies we surveyed reported having a data literacy program. While data literacy isn’t a one-size-fits-all skill set, many resources already exist to support your program.
These are the basic steps necessary for a successful data literacy training program:
- Use assessments to identify skills gaps
- Develop your organization’s data literacy framework
- Secure data management tools that support your data literacy needs
- Give employees data literacy tools with the right level of access
- Provide onboarding, ideally provided in partnership with your software vendor
- Maintain employees’ data literacy skills with online courses
- Continue gathering metrics and iterating on your program modules
For more details on building data literacy, read our Ultimate Guide to Data Literacy Training.
Data literacy tools
Data literacy can mean something different for each person at your organisation. But building common language is necessary for creating and maintaining a data culture. Data literacy tools break down data silos and improve access to valuable data.
Most business professionals will use one of these types of data literacy tools in their daily work:
- Data analytics tools and dashboards — business users use all kinds of software to analyse and report data. Think data visualisation tools, web analytics platforms, and business intelligence tools or product intelligence tools. Give users the right tools for their needs and help them learn best practices to get the most out of your software spend.
- Business glossary or data glossary — sharing definitions of all business terms and acronyms. This ensures that people across your organisation literally use the same terms to discuss data. If nothing like this exists, you can start a glossary with something as simple as an internal wiki page. Eventually, you'll want a robust glossary built into your data management software.
- Data integration tools — data comes from all kinds of sources. Business users often need data from multiple sources to answer questions and make solid business decisions.
- Data inventory — a complete, descriptive record of data organisation-wide. The data inventory gives central IT visibility across the data landscape. It also helps data users find and access relevant datasets.
- Data catalog — a data catalog is a step beyond a data inventory. A data catalog isn't just documentation of where data is. It's a cross-referenced catalog with rich metadata. A data catalog should have a user-friendly interface. A complete data catalog also provides full data lineage and usage tracking to support your data governance strategy.
Of course, the tools above can be hard to use without training. Data literacy training resources are also data literacy tools.
For a more in-depth look at the three major types of data literacy tools, follow this link to Data literacy tools: understanding how they work together.
Data literacy skills
While it’s natural to associate data fluency with data science roles, all business employees need some level of data literacy. Data literacy skills are wide-ranging:
- Data-literate HR professionals leverage people analytics, for example identifying reasons behind attrition
- Data-literate managers use metrics to evaluate team performance and determine fair bonuses
- Data-literate graphic designers communicate complex ideas effectively with data visualisations
- Data-literate marketers target audiences, evaluate campaign outcomes, and assess return on investment (ROI)
- Data-literate salespeople use data to understand customer problems and sell relevant solutions
- Data-literate product teams understand why and how their customers use products
Go behind the buzzword with our article Mastering data literacy: developing data literacy skills.
Data literacy benefits
Data literacy is vital in any job, as well as in everyday life. Consider these real-world advantages of building data literacy:
- Shift from reactivity to proactivity, and start making data-driven plans
- Take action based on real-time data, instead of waiting for others to process data for you
- Avoid being influenced or led astray by misleading graphs — at work or in news and advertising
To know more about the advantages or disadvantages of data literacy, read our in-depth article about data literacy benefits.
Data literacy in education
As the big data economy grows, expect to see more emphasis on data literacy skills in education.
That said, data literacy has always been part of education. Educational activities like learning how to read a graph or how to record measurements with units are all part of becoming data literate.
Data literacy in business
Data literacy skills aren’t limited to the IT department anymore. Yes, a chief data officer, data scientist, or data analyst must be highly data literate. But all business professionals today need to make data-driven decisions.
That’s because in today’s data-driven economy, algorithms encourage analytical decision-making in every department. Data touches all roles now, even if “data” isn’t in the job title.
Learn why the language of data is vital for the future of work in our article on developing and applying data literacy in business.
Data literacy learning paths
Each individual's data literacy learning path will be unique. That’s because:
- Competencies needed for data literacy vary across industries and organisations
- Existing skill sets will vary across a workforce
- Every role uses data differently
Make sure your data literacy project supports individualised learning paths.
Talend’s workforce uses Talend products to grow data literacy through metadata management, data stewardship, and data governance. To get expert advice in establishing your data literacy program, request a consultation with Talend.
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More related articles
- Data literacy benefits: Understanding the advantages and disadvantages
- Data literacy tools: Understanding how they work together
- Developing and applying data literacy in business
- Mastering data literacy: Developing data literacy skills
- The ultimate guide to data literacy training
- Data literacy framework: A guide to creating an effective data literacy programme
- Data literacy in education: Understanding the benefits, advantages, and barriers