Data Literacy for Business: Your 2024 Guide

Written by Coursera • Updated on

Data literacy is an in-demand skill across various sectors. Discover how data literacy can drive business outcomes and how you can build a foundation of data literacy within your organization.

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Much like generative AI, data is important to more than just data scientists. Data is important to more than just data scientists. Given the massive amounts of data created every second, businesses and their employees across various industries must understand how to approach data to make smart decisions. To have a greater understanding of data and how to make business decisions, an organizational-wide data literacy plan creates better business analytics. This article will examine what data literacy is, what it means for your organization, its fundamental concepts, and how organizations implement data literacy initiatives.

What is data literacy?

Professor Catherine D’Ignazio, now of MIT, and Professor Rahul Bhargava, now of Northeastern University, break data literacy into four parts. Using this four-part definition, a data-literate business or individual will have the ability “to read, work with, analyze, and argue using data [1].” Let’s take a closer look at these four components:

  • Reading data is understanding where it comes from and what it represents.

  • Working with data means creating, cleaning, and managing it. 

  • Analyzing data is to perform analytic tests such as sorting, aggregating, and comparing.

  • Arguing with data supports your findings with data backing you up. 

Being data literate in business goes beyond simply knowing the four parts of the practice. True data literacy allows you to communicate that data with everyone within your organization. Not everyone needs to be a data scientist, but being data literate helps executives, managers, and employees make business decisions concerning data. 

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Why is data literacy important for business?

Big data, characterized by its size, speed, and broad scope, is now everywhere in business, creating an increasing need for individuals within an organization to be data literate. Doing so empowers them to know how to leverage this massive amount of data to make intelligent business decisions. Businesses and individuals must know how to interpret and protect this data. Since these decisions now affect every aspect of an organization, everyone needs to understand how data functions relative to their role in the company. 

The importance of big data literacy 

The existence of big data requires further literacy so that companies weigh the ethical impacts related to passive data collection based on their actions and those of their users when making data-driven decisions. Data science finds patterns within big data collections, and businesses must choose how to utilize that data. This leads to the importance of everyone in an organization having foundational data and analytics skills to understand the ethical impacts of big data.

Data literacy concepts

Below are some key data literacy concepts your business should understand while on your path to data literacy, according to the Harvard Business School Online [2]:

  • Data analysis

  • Data cleaning

  • Data visualization

  • Data ecosystems

  • Data governance

  • Data teams

Let’s take a closer look at each data literacy concept and what it means. 

Data analysis

Data analysis uses statistical methods and algorithms to model data and find patterns. Businesses can use analysis to forecast risks in medical procedures or how product demand will affect the supply chain, allowing them to be proactive. Data analytics use four different types to do this:

  • Descriptive analysis: This uses data to generate reporting on things such as quantities, locations, and timing. 

  • Diagnostic analysis: This type uses past data to examine why and how something occurred; instead of predicting the future, it discovers past trends. 

  • Predictive analysis: This uses trends and correlations to help predict future outcomes. 

  • Prescriptive analysis: This form of data analysis uses big data and machine learning to find answers to questions and identify future actions using multiple variables. 

Data analytics gives insight into business making informed business decisions. 

Data cleaning

Sometimes referred to as “data wrangling,” this step is essential in preparing data for analysis. It organizes, normalizes, and helpfully refines data. Understanding how data cleaning operates is critical for every employee within an organization so that they can use best practices to prepare data for analysis.

Data visualization

A primary function of data visualization is exploring data trends and patterns using graphical representations. This helps non-data science professionals within an organization better understand what data means and its applications. Data visualizations use charts, maps, and graphs to represent the data. This is an important step in explaining data to those within an organization who may need more data literacy. 

Data ecosystems

Data ecosystems are everything an organization uses as infrastructure to collect, package, create algorithms, store, analyze, and use data. These ecosystems extend to databases, spreadsheets, analysis software, programming languages, servers, and cloud computing providers. Every organization has different data needs and processes; part of being data literate is understanding how your organization's data infrastructure operates to use data efficiently. 

Data governance

Data governance is a set of institutional policies that dictates the security and availability of data for use within an organization. While data management refers to the macromanagement and organization of data, data governance focuses on the security and distribution of potentially sensitive data within an organization. Effective data governance improves data literacy, analytics, security, and quality of data usage.  

Data teams

While data literacy is essential for everyone within an organization, some specific roles within data analytics make up a functional data team for organizations at various scales. These roles vary from data scientists to management positions. The following positions commonly belong within an organization’s data team:

  • Data scientists

  • Data engineers

  • Data analysts

  • Data manager

  • Data director

  • Chief data officer (CDO)

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Who uses data literacy?

Employees who already work in the data team understand data, so those outside the traditional data team must develop data literacy. It allows those employees to work closer with data analysis teams by asking questions that inform their business decisions in their area of expertise. These workers must understand the importance of security, governance, and trust in the data they use to make business decisions. Gearing data literacy toward an employee's specific role within the organization will maximize the effectiveness of having a data literacy company. 

How to establish data literacy in your business

You will need a data literacy plan to establish data literacy in your business. Before you enact it, you must assess your employees’ current understanding of data and communicate your expectations and reasons why data literacy matters. After agreed-upon goals for literacy within your organization, enact a plan. Below is a brief outline of the actions you can implement:

  1. Find the current understanding of data within your organization.

  2. Focus on continuous learning.

  3. Create a variety of learning resources.

  4. Define success and know the limits of data literacy.

Let’s examine each step more closely. 

1. Find the current understanding of data within your organization.

Only some positions need a high level of data literacy. Still, it's important to determine your company’s current understanding. Once you identify knowledge gaps, upskill workers to the level of literacy you think is necessary for their role. 

2. Focus on continuous learning.

With the advent of big data, the world of data is constantly changing. Create plans that focus on continuous learning within your organization. Focus on the rewards that becoming data literate provides so everyone in the organization understands data. 

3. Create a variety of learning resources.

Developing a variety of learning resources is vital when creating a company-wide program. Long training sections tend to lead to low memory retention, so leveraging tactics like microlearning helps foster continuous learning within organizations. 

4. Define success and know the limits of data literacy.

With a definition of success or metrics to follow, knowing if your company is becoming more data literate is possible. These goals should be tied to business outcomes and specific to your organization's functions. Potential measurements to consider include the impact of data literacy on self-reported employee productivity, the time to close deals for sales team members, and time-savings from specialist data teams due to other employees being able to accomplish data-related “self-serve” tasks independently. Having a place or project to ground a data literacy initiative helps create measurable goals. Understanding that data literacy has limits sets expectations for how data literacy impacts an organization. 

Getting started with Coursera

Having an organization that is data literate is becoming more and more vital. If you want to find ways to upskill your employees in data literacy, try the Foundations: Data, Data, Everywhere course offered by Google or try a Guided Project like the Introduction to Data Analysis using Microsoft Excel, both found on Coursera. 

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Article sources


D’Ignazio and Bhargava. “Approaches to Building Big Data Literacy,'Ignazio_52.pdf.” Accessed June 13, 2024.

Written by Coursera • Updated on

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