Data Literacy: Definition, Importance, Examples, Skills

Data is changing every aspect of our business and personal lives. And data literacy is what can unlock the potential of data and make it valuable.

However, not enough people have the needed data literacy skills to use data effectively.

As data is everywhere and it is growing, people must have at least a basic ability to understand and work with data.

On this post:

  • What is data literacy? Definition and meaning.
  • Why is data literacy important?
  • Data literacy examples and key skills
  • Infographics in PDF

What is data literacy? Definition and meaning

Let’s define it.

Data literacy is all about the ability to read, write, use, and communicate data.

Whatever you are a businessman, student, or manager, you need to have good data literacy to gain the value that data provide.

Data literacy can encompass a wide spectrum of skills.

However, you don’t need advanced statistical analysis knowledge or data scientist skills like programming in Python or R.

To be data literate, you should be able to analyze and interpret a standard data table or chart.

You should be able to read, understand, and use common graphs like the line graph, bar chart, pie chart, and scatterplot that are found in most dashboards and news reports today.

You also need to know how to produce your own charts and perform your own analysis.

For the purpose, there are many data visualization tools (like Visme and Chartio) that will allow you to create fascinating graphs, diagrams, and maps in no time.

Why is data literacy important for every worker?

Each professional industry and every person benefits from using data. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPIs.

We need to constantly analyze and share data with our team or customers.

Having data literacy skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Data Literacy Skills And Examples

Fundamentally, to be data literature, you should have skills in the following 4 areas:

1) Data Sources

Before start working with data, you need to collect it.

But how to collect the data? What are the data sources you can use? What insights can each data source bring to you?

There are two main types of ways to collect data for your needs:

1. Internal sources of data – the information gathered within your company or organization. It is completely free to use.

Examples of internal data include:

  • Sales data such as sales reports, revenue, profitability, price, distribution channels, buyer personas, etc.
  • Customer data from orders such as contact details, names, transaction history, payment methods, etc.
  • Your social media profiles on networks like Facebook, Twitter, Linkedin. The data you can collect from them include likes, shares, mentions, impressions, new followers, comments, URL clicks.
  • Data from your transactional system such as purchasing behavior of your customers, buying habits, and shopping preferences.
  • Your business emails – they can provide you with data such as product reviews, opinions, feedback, and so on.

2. External sources of data – the data collected outside your organization. Some of the sources are free and others – paid.

Examples of external sources of marketing data include:

  • Data.gov provides over free 150,000 datasets available through federal, state, and local governments.  They are free, and accessible online.
  • World Bank Open Data offer free and open access to global development data. Datasets provide population demographics and a vast number of economic indicators from across the world.
  • Google Trends – allows you to examine and collect data on trending news stories around the world. It is an extremely useful data collection method when it comes to creating a marketing or SEO strategy.
  • Crunchbase -a great platform for finding business information about private and public companies.

The above data sources might look overwhelming to you. But it is not necessary to use all of them.

You need to know those of them that are specific to your industry and needs and to understand what type of insights they can bring to you.

Once, you define your data goals, you can choose the most appropriate data sources.

2) Data Terms And Data Knowledge

Each industry (retail, finance, marketing, etc.) has its own unique data terms and datasets.

The more you understand your company’s data from a business perspective, the better you can apply the data.

For example, let’s say you are working in the data-driven marketing area. Then, you should be familiar with data terms and metrics such as page views, traffic sources, unique visitors, and bounce rates.

Furthermore, you need some ability to work with numbers and understand simple statistical concepts such as:

3) Data Interpretation

After you’ve familiarized yourself with the data, you should be able to interpret it.

Data may be examined in many different ways depending on its types.

In general, you should be able to make the following types of observations in graphs and charts:

Key observations in graphs - a short infographic

  • Trends: What is the direction in which a metric is changing (up, down, flat)?
  • Patterns: What repeatable patterns the data show (e.g., seasonality)?
  • Gaps: Are there any obvious gaps in the dataset (such as incomplete, outdated, or inconsistent data)?
  • Outliers: Is there a data point that differs significantly from other data points? An outlier may indicate an error and cause serious problems in your analyses.
  • Focus: Has something in your chart or table been emphasized? Why this part of the data is highlighted?
  • Logical: Does the data help to answer a specific marketing or business question? Does the data support a hypothesis or assumption?

Example:

To see how data literature can work, let’s take a look at the following line graph.

Line graph example

A person with basic data literacy skills should be able to understand what the line graph shows.

It represents the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

You can see at a glance that the top-performing product over the years is product C, followed by Product B.

In other words, the above line graph shows trends for 3 product lines over the same period of time as well as illustrates a comparison between them.

4) Curiosity And A Passion For Data

It’s not enough to analyze and interpret data if you want to build your data literacy skillset. You need to be curious and think critically about it.

You need a passion for finding patterns, trends, and answers to different problems.

Aim to think outside the obvious borders and see the less obvious factors that may be influencing the results and its interpretation.

For example, pay attention to these areas:

  • Data collection methods and sources: Could the method or data source influence the results?
  • Credibility: How credible and reliable are the source of the data?
  • Quality: Are you able to recognize the helpful data and define which data has meaningless value for your research?
  • Bias: Is there a potential bias from the data producer or from you as the consumer?
  • Context: Is there additional background information that is missing and needed to correctly understand the data?

Download the following infographic in PDF

Basic data literacy skills - infographic

Conclusion

Data literacy can be challenging. It’s not just about using software and technology.

It’s mainly about using skills and mindset to find meaning in the numbers and make data effective.

In today’s business world, everyone from the C-level manager to an intern needs to be data literate.

Employees with data literacy skills are the ones that can act on data insights productively and create better results.

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