What is data literacy? How to Build Your Data Literacy in 3 Steps

Elice

07/09/2023

In the digital age, data is the second language of business. It’s safe to say that data application is now a must for all members of an organization. To excel as a data-driven company, all members must become proficient in the language of data. Deriving insights hidden within data requires you to have the literacy to read, understand, create, and deliver data. In this module, we will learn about the meaning of data literacy and how to develop relevant capabilities.


What are data analytics? Learn that your data matters!


Defining data literacy

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Data literacy refers to the ability to use data to make decisions and communicate effectively. To explain ‘data utilization’ in detail, it refers to understanding and using various forms of data such as language, figures, and graphics for communication.

As the meaning of data literacy suggests, data capabilities are dependent on your logical thinking and problem-solving skills.In other words, a person with data competence can properly consider the purpose and background of a data request and deduce which data is relevant through reasoning. They will also possess the ability to effectively share data analysis results with others. More will be explained in detail in the ‘How to Build Data Literacy’ part below.


Importance of data literacy

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Data literacy is critical for companies wishing to build a data-driven decision-making structure. Recently, companies’ interest in data and data utilization technologies has risen. As data assets grow, data application is becoming important for businesses and their members. The better data literacy you have, the better your job performance and the better your decision-making. Hence, organizations now demand individuals who can make quicker and more effective decisions using data, making data literacy an indispensable competency for all members of the organization.


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Who needs data literacy

Until now, data capabilities could be found in only a handful of data analytics experts. So far, management and working-level officials made decisions informed by analytics reports by data analysts. However, there are limitations to this method. Data analytics professionals may find it difficult to catch changes in the business environment more acutely than the actual workers themselves. They may also be less aware of the company’s strategy and decision-making criteria than management are. As a result, analysis results may not be utilized properly in some situations.

Today’s digital economy is volatile, and companies need to be able to respond to customer needs right away. They need to be able to detect market changes in real-time and produce results. The longer the process for making data-driven decisions is, the less meaningful data analysis results may be. Therefore, data capabilities will be essential for all members, from management to practitioners. If all executives and employees can make faster and better decisions based on data analytics, they can greatly increase corporate productivity.


How to develop data literacy capabilities

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Handling data analytics tools is not the end-all-be-all for data competency. As we infer from the meaning of data literacy, you need to be able to properly lay out what data will be used for which purposes. This requires, in particular, logical thinking and problem-solving skills. These capabilities can be developed by repeating the process of diagnosing and troubleshooting problems based on data. Below, we will discuss the three steps to approaching data analytics correctly and increasing data capabilities.


Define data usage goals

In order to make the right use of data, it is necessary to clarify the “Goal of data usage.” You will also have to decide what data to use based on the set goal. Data is used to support logical communication conducive to making the right decision. Therefore, data collection or crude interpretation should not be an objective in itself. You will need to define the right purpose and go through the process of collecting, interpreting, validating results, and drawing conclusions accordingly.
Therefore, before you look into the data, you need to affirm what you want to know from the data and what problem you are looking to solve. It is important to specify the problem you are trying to solve and decide what data to gather to solve this problem.


Deliberately collecting data

Collect data according to your clear definitions of your purposes and the problems you wish to solve. It is important to collect specific data so that the user can make more deliberate judgments. However, there are cases where there is no desirable data during the process. When data is difficult to find, replacing it with similar data or collecting additional data can be an alternative. The process of collecting additional data must also align with the ‘purpose’.


Derive conclusions from data analysis

Once all the data needed for your purposes has been collected, process and analyze the data so as to present insights. At this time, you should be wary of merely presenting interesting insights or discoveries. This is because it can confuse the listener in regards to what the core takeaway is. It is once again important to present insights that suit the purpose.

Finding insights amounts to simply identifying the status quo, so you will need to draw conclusions about what actions are needed. You will need to use the data to make decisions, such as devising specific action plans or developing solutions to problems. When drawing conclusions, you can add interpretations along with aggregating information. However, your individual interpretation must stay within the scope of facts that can be logically inferred from the data. Being imaginative beyond what is necessary can come back to bite you.


Conclusion: Goal-Oriented Data Reasoning

Summarizing the process so far, you can see that the ‘goal-oriented data mindset’ is important. To increase your data capabilities, data must first be recognized as a means to an end. In other words, the data utilization process is the process of defining discoveries in regard to specific purposes and problems, determining the corresponding data, identifying the status quo, drawing conclusions, and seeking solutions.

So far, we’ve looked at the meaning of data literacy and how to develop data literacy capabilities. Data literacy refers to the ability to self-define objectives and problems and draw reasonable conclusions informed by data. In practice, however, many companies and experts that recognize the importance of data capabilities and introduce data training courses often fail to deliver tangible results. This is mostly because it teaches only data analysis tools. There is bound to be a limit to improving data literacy by simply learning data analytics tools.

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Elice offers training courses to help you systematically develop your data literacy capabilities. We can help you understand the data analysis process, determine the quality of the data, and even improve the reliability of the data analysis so that the data can be used as a means to your ends. If you want to increase your employees’ data literacy skills, consider starting with Elice.

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