What is Data Literacy and Why It Matters?

Modern company success requires data literacy skills. Business users now have more data than ever before, but unless they can understand and interpret it, they won't be able to realize its full potential for generating revenue. Poor data literacy impedes an organization’s digital transformation and its ability to compete in an increasingly digital-first business environment.
May 24, 2022

Data literacy has become critical for everyone. More employees with the capacity to understand data, develop conclusions, and ask the relevant questions are needed in businesses. Data literacy may help organizations improve data quality, collaborative communication, creativity, and work process efficiencies. In effect, data literacy implies more profits with fewer drawbacks.

What is Data Literacy?

The capacity to read, write, analyze, and interact with data is known as data literacy. You don't have to be Shakespeare to be deemed literate, and you don't have to be a data scientist to be data literate. Learning every programming language or mastering the most advanced data science skills is not required for data literacy. It is instead about comprehending and making decisions based on evidence.

Data literacy is an important step on a company's path to becoming data fluent. It specifically assists employees in making data-driven decisions, critically interacting with data, forming effective data governance, and making ethical data judgments. 

Businesses must overcome hurdles to data literacy and engage in a data democratization program if they are to realize the value of the data accessible to them. Understanding what data literacy is and what skills are required to achieve an appropriate level of data literacy is the first step.

Data literacy is defined as the capacity to read, write, and communicate data in context, which includes a comprehension of data sources and constructs, as well as the ability to express the use case, application, and resulting value.

In practice, this implies that business users must be aware of the data that is available to them, how it may be used, and any limits that may exist. They must understand how data from various sources can be merged or augmented with reliable information from third parties. They should know how to use geospatial context to produce deeper insights and make better decisions. The need for proactive data quality management should be understood by business users. To put it another way, today's decision makers must grasp how to turn raw data into meaningful commercial value.

Why Does It Matters?

Employees With Low Data Literacy Will Not Understand the Power of Analytics and Artificial Intelligence

Data and AI must be democratized to solve a shortage of data literacy. By having a no-code AI tool like Obviously AI, you can easily understand what your data is telling you. Not only that, but we can use the platform to view your data, create shareable reports, export results, and take action based on the platform's predictions. Everyone has the confidence to ask the correct questions and make better decisions when they have access to their data and understand how to extract insights from it.

Employees who do not understand this potential will certainly experience difficulties in using AI, especially in the connectedness of using data analytics. On the other hand, employees who do not have good enough data literacy will experience difficulties in utilizing the great potential in using data.

Making Data-Driven Decisions Requires Data Literacy

Employees are more likely to buy into a change if they understand it, according to McKinsey. Employees that are knowledgeable about both data science and its commercial applications can lead the development of end-to-end use cases and encourage others to make data-driven decisions.

The Houston Astros, for example, exploited analytics to gain a competitive advantage in a zero-sum sector, allowing them to win the Major League Baseline in 2017. What's their secret? Executives who promote data literacy and make choices based on data. They hired data-literate coaches (who can program in SQL) to convert data findings into a language that players could understand. Furthermore, the Astros established a strong data culture that pervades every aspect of the team's game plan, from player selection prior to the game through player position during the game. Without a data-literate team, such data-driven judgments would not have been achievable.

Employees Must Be Data Literate in Order to Interact with Data Insights in a Meaningful and Crucial Way

Employees who are data literate may create and analyze data visualizations that are used in the company's decision-making process. PepsiCo is an example of a corporation that utilizes Tableau and Hadoop to visualize massive amounts of data and make million-dollar sales choices.

Furthermore, being data literate is a must for critically evaluating the authenticity of the data utilized to produce visualizations.

These individuals can potentially catch costly mistakes by questioning the data sources' dependability, accuracy, and consistency, boosting the company's confidence and efficacy in making data-driven decisions.

Effective Data Governance is Built on the Foundation of Data Literacy

The set of policies, methods, and organizational structure that describe how an organization's data is managed is known as data governance. It guarantees that data is easily accessible, relevant to the company's value creation, of high quality, and consistent with current regulations. To establish robust data governance, executives must have a fundamental understanding of the data context and needs of the firm before establishing data regulations.

According to McKinsey, the cornerstone for good data governance is a three-part data organizational structure:

  1. Domain leaders who set and execute domain-specific strategies 
  2. The data management office (DMO) is in charge of defining policies and standards.
  3. The data council that brings domain leaders and DMOs together

Ideally, the leaders of each of these components should have enough knowledge of data processes to create clear and equitable data governance roadmaps. Domain leaders who are data literate and understand the value of data, for example, may work effectively with the DMO to build and deploy an appropriate corporate data lake for their domain.

Building Data Literacy in Organizations

Clearly, instilling good data literacy in all staff has numerous advantages. Existing skills efforts, e-learning courses, and specialized classroom training are all examples of data literacy programs. Designing in-house data literacy programs is a complex and time-consuming process that necessitates meticulous planning. 

How can you create a culture that encourages and supports data literacy in your organization now that the business case for data literacy is clear? Here are some suggestions to get you started:

1. Determine Your Current Data Literacy Level

To get you started, Gartner proposes asking the following questions:

  • How many individuals in your company do you believe can interpret basic statistical procedures like correlations and averages?
  • How many executives can build a business case based on concrete, precise, and relevant data?
  • How many managers can articulate how their systems or processes produce results?
  • How many data scientists can explain how their machine learning algorithms produce results?
  • How many of your customers understand and internalize the substance of the product?

2. Set Goals and Targets for Yourself Based on Your Present and Desired Data Literacy Skills

Prioritize areas of the business where greater data utilization can have a substantial impact and focus on them first. Describe the desired skills, competencies, and level of data literacy for each employee's specific function.

3. Implement a Data Literacy Program that Provides All Employees With the Necessary Tools and Training. 

Ascertain that employees have access to the tools and training they require to achieve the necessary degree of data literacy for their position. Allow employees the time they need to develop data skills and become skilled at utilizing data to make daily business choices by incorporating flexibility into your processes. To verify that your literacy program is running effectively, include enough follow-up measures to track and monitor progress toward data literacy on an individual and organizational level.


The majority of attention in most talks regarding data literacy is focused on users. After all, the word indicates that particular abilities within your team must be developed. However, in order to foster data literacy within your organization, you must first establish a conducive climate.

The ultimate purpose of data literacy is to give your company a foundation for data-driven decision-making. As a result, it stands to reason that your data must first be accurate, consistent, and contextual. Otherwise, your decisions will be skewed poorly, regardless of how data literate your workforce is. Staff, business intelligence (BI) tools, and AI or analytics models can only make good decisions if they have access to data that is reliable, consistent, and contextualized.

Written by Denny Fardian
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