Here is a brief overview of data literacy. It includes the key concepts that you need to master in order to be successful in a data-driven world.


Data literacy refers to an individual’s ability read, understand and use data in a variety of ways. This doesn’t mean that an individual must be an expert, as a data scientist, analyst, or other type of data analyst, but rather they should be able to understand basic concepts such as:

  • Different data types
  • Common data sources
  • Different types of analysis
  • Hygiene of data
  • Frameworks, tools, and techniques

Non-data professionals can learn data literacy to help them understand and read data, and then use that information to guide their decision-making. Data literacy is becoming more important for executives and managers as well as employees looking to improve the value of their organizations.


  1. Data Analysis

Data analysis is the process of reading and understanding data in order to extract insights. Analysing data can be done using complex algorithms and statistical models. However, it is also possible to simply review the data and draw conclusions.

There are many types of data analysis that you can do. The most popular are:

  • Descriptive analysis is a way to describe or explain what happened.
  • Diagnostic analysis is a method of diagnosing or explaining why something happened.
  • Predictive analysis is a method of predicting what might happen.
  • Prescriptive analysis is a method of recommending a course for action that will achieve a desired outcome.


  1. Data Wrangling

Data wrangling refers to the process of turning data from raw state into something that can be used more easily. Data cleaning or data munging is another name for the practice. Data wrangling may take many forms. The most common are removing data errors and filling in gaps.

Data wrangling is crucial in reducing errors in analysis that usually follows. Many organizations clean data automatically using various algorithms and other tools. However, every employee who generates, captures, or uploads data plays an important role in ensuring that it meets organizational requirements.


  1. Data Visualization

Data visualization is the art of creating visual or graphical representations of data. It is often an essential part of communicating insights effectively. A chart or graph that aids investors in understanding a company’s quarterly earnings report is an example of data visualization.


  1. The Data Ecosystem

The data ecosystem is a collection of all the components that an organization uses to store, analyze, and collect data. This includes both physical infrastructure such as server space or cloud storage, as well as non-physical components such as data sources, programming languages and code packages.

Every organization has a unique data ecosystem. However, some may overlap if they use the same data sources and third-party tools. Understanding the data ecosystem of your company can help you understand its components and possibly uncover optimization opportunities.


  1. Data Governance

Data governance is a set of processes and practices that an organization uses in order to manage its data assets. This concept can be compared to a set of rules that is specifically created to protect an organization’s data. Many organizations give a “data policy” to their new employees along with the employee handbook.


  1. Data Team

It is important to know who the key players in your data team are and what their roles are, regardless of whether or not you work directly with them.

Depending on the size of your company and how much data is being used in daily activities, there are many ways to structure data teams. Most data teams will include:

Data scientists are data analysts who use advanced mathematics, programming and tools to manage large-scale analyses

Data engineers are responsible for creating and maintaining data that can be used in data projects.

Data analysts are the ones who perform the bulk of the analysis an organization needs.


The demand for data literacy training will increase as businesses and organizations become increasingly data-driven. It is a smart decision to invest in your data literacy skills.

There are many ways to become more data-literate. Volunteering for data-related projects is one way to become more data proficient. You will gain a solid understanding of how data is used within your company over time. You can also enroll in an online course in data science and analytics that will help you gain the skills necessary to succeed in your job.


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