Data has proved to be a major driver for innovation. Using quality data, companies have created informed business strategies and core decisions for growth and sales. It has led to enhanced performance in comparison to less data-driven businesses.

Just making data accessible sets your business on the journey towards success. It even needs employees that can understand and talk about data. Therefore, professionals with data literacy training are in high demand.

What’s data literacy?

It is a term that defines a person’s capability to read, understand and use data wisely in a variety of ways. There is no need for a person to be a data analyst or data scientist but the knowledge of basic concepts is great. It includes –

• Different data types
• Analysis types
• Common data sources
• Techniques, tools, and frameworks
• Data hygiene

With training in data literacy, non-data professionals can read, decipher, and use it in their decision-making. Data literacy is crucial for executive leaders, managers, and employees alike. It increases the value they will bring to the company.

Data literacy skills and concepts

Data analysis

It means to gain insight from the interpreted data. The analysis is conducted using algorithms, statistical models, complex tools, and other frameworks. People even review the data and conclude! Some of the common data analysis types are –

• Diagnostic analysis answers ‘WHY’.
• The descriptive analysis explains ‘WHAT’.
• Prescriptive analysis prescribes an action that will accompany desired results.
• The predictive analysis forecasts what may happen.

Data wrangling

It means the transformation of raw data into a usable format. It is even popular as data cleaning or data munging. It commonly involves filling the gaps and eliminating errors. Various algorithms and tools are used to clean the data. However, every staff member is responsible for generating, capturing, and uploading data that fulfill the company's needs.

Data visualization

In this process, visual or graphical data representations are created to effectively share the insights. For example, charts can help investors recognize the company’s monthly earnings report.

Data ecosystem

This concept refers to every component leveraged to gather, store and analyze the data. It includes physical infrastructure like the cloud storage solution and server space. Non-physical components like code packages, programming languages, data sources, software, and algorithms. 

Each business has a unique data ecosystem even if some can overlap because of third-party tools. 

Data governance

The concept refers to the processes and policies a company uses for data asset management. It is similar to a rulebook designed specifically to make sure that the company data stays complete, accurate, and secure. Many businesses hand out a business’s data policy with recruits along with an employee handbook. 

Data team

Structure a data team to leverage data in daily activities. The data team will include data scientists, data engineers, and data analysts.

You can get enrolled at an online data literacy or analytic course designed specifically for sharpening the foundational skills necessary to excel in the role.