Why is data literacy so important?

Data literacy is vital because we can improve our outcomes by using data, even in a business context.

Many organizations don’t have these skills, even though data literacy training is becoming an increasingly important skill in the workplace. Data literate employees are more likely than others to report that they are performing well. This assessment can help you determine how your skills stack up.

Many people want to be able to visualize and communicate data (including info graphics) and data literacy is crucial to achieving this. This report on financial projections includes data in raw numbers, tables, and graphs. Understanding the data will help you highlight key takeaways that your audience will appreciate.

You might have begun visualizing survey data and want to find out what other data is available. Maybe you’re looking at data only about customers you already have and want to explore data that can help you target customers.

Do you think data literacy skills are difficult to learn?

It is so vital, why then does it not exist in the workplace?

These skills may not be available to leaders. This is because data-literate workers are often isolated within IT or BI teams. It doesn’t encourage colleagues to share their knowledge.

Yes, some people haven’t recovered from their terrible math class in high school. They might believe they are bad at math.

Some people may be good at math and others might not. However, mistakes can have serious consequences that can lead to a loss of reputation, as well as potentially disastrous consequences.

Things beginners need to learn about data literacy


Uncertainty will always be there

This is important, especially when we communicate data. Visuals such as info graphics are useful in communicating data. They can mask reality and make it seem more accurate. Responsible communicators can understand the data and explain what we don’t know.


There are several types of data.

Data collection is a complicated, laborious process due to the many uncertainties. It doesn’t matter if you collect the data yourself. Many types of data are available, each suitable for different situations.

Most people think of data as qualitative data. These are based on precise measurements that can often be analyzed using statistical methods.

Quantitative data also called numerically, may come in many forms.

Discrete data may be counted and broken down into smaller groups like the number of people present in a crowd. continuous is another option. It can be used to measure length, temperature, and other variables. There are two types of Interval data that have no “zero”, such as temperature. Ratio data, however, has true “zero”, such as weight. Inline graphs such as profit growth reports, continuous data can be visualized.

Qualitative data is descriptive. It is based upon observations that cannot easily be measured like gender and language are spoken. Analyzing this type of data can often involve categorizing it into themes or patterns based upon characteristics. categorical can be used to describe qualitative data.

Two types of categorical information are available. Nominal is used to measure frequency and percentages. This data can be displayed in either a bar or pie chart. Ordinal Data means that the data can be arranged logically (e.g., breakfast, lunch, dinner).

Sometimes, you may need to analyze both qualitative and quantitative data. This is a mixed method. These data can be mixed and interpreted in many different ways so it is worth considering if you have this type.

This is how it works: Understanding the data you are working with will help determine the best way to communicate it.


It is vital to organize your data

You may be thinking wow, that’s a lot. It is not easy to manage data.

If you only have a limited data set like survey responses, you may be able to use one spreadsheet or several.

Public data sets such as Census figures can be more than you need. Some data sets might even require cleaning, meaning that duplicates or incorrect data must be deleted. Sometimes data needs to be rearranged. If numbers are being read by analytic software, then calculations may not be possible. Text and numbers are different fields.

Data management can be a team effort. You don’t have to know everything.