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Abstract

In the era of digital transformation, data literacy has emerged as a critical competency for organisations, driving a demand for skilled professionals. Despite a shortage of data-literate talent, universities struggle to align their curricula with industry needs, prompting a call for improved data literacy education. Recognising the contextual nuances of this skill set, a one-size-fits-all approach falls short. To address this gap, the authors advocate for a comprehensive exploration of perspectives from key stakeholders such as business advisors, students, teachers, and researchers. Understanding diverse needs and expectations of stakeholders is crucial in identifying deficiencies in data literacy education, paving the way for enhancements in university programmes. The reported study is the preliminary phase of a larger ongoing project in which grounded theory methodology is used to explore the question of ‘how can data literacy education be improved?’. The authors report on a small-scale study (eight interviews) aimed at exploring the perspectives on data literacy definition, competencies, and challenges with two representatives of each of four groups of stakeholders in data literacy education: students, business advisors, educators, and researchers. One common challenge identified among participants is the lack of data literacy and critical thinking skills, as well as a lack of awareness of the importance of data analysis. Although participants were aware that different businesses may need different data literacy skills, they were not able to articulate what those differences might be. The study underscores the need for the development of frameworks to help guide and advance data literacy education.

Keywords

Data literacy education challenges students educators business advisors researchers

Introduction

Data literacy is a critical capability for any organisation, but there is a shortfall in data literate talent. The ongoing global digital transformation of economies is driving the need for data literacy in organisations. Data literacy is therefore fundamental to organisations being able to realise the full potential of digital infrastructure for their economic growth. It is with urgency that organisations are seeking to fulfil their data literacy needs.

Googling ‘data literacy’ immediately provides evidence of the sense of urgency in business domains for increasing data literacy. Two examples of search results on the first of many pages of results are given below:

‘Champion data literacy and teach data as a second language to enable data-driven business’ (Panetta, 2021, P. 1)

‘Boost your Team’s Data literacy . . . But while we have all this data, and it’s becoming more influential than ever, there’s still a big problem at hand: Most of us are not very good at interpreting and making sense of it’ (Bersin and Zao-Sanders, 2020: 1)

Unarguably, data literacy is critical in business and must become the ‘second language’ of business organisations, otherwise organisations will not be able to achieve competitive advantage (Logan, 2018). Creating a culture of data literacy is imperative to business value creation (Johnson, 2019).

It is apparent that although the volume of data is increasing, the required levels of data literacy are not ‘keeping up’. In Forbes magazine it is noted that:

The growing prevalence of technology such as automation, robotics, artificial intelligence (AI) and machine learning means ‘data’ is becoming a universal language across all industries. However, not enough people currently speak this language. In fact, as our collective volume of data increase, so too does our data literacy gap (Forbes Councils, 2019).

Gartner, high-profile research and advisory for organisations, echoed the concerns expressed in Forbes, ‘By 2020, 50% of organisations will lack sufficient AI and data literacy skills to deliver business value’ (Gorden, n.d.). While the data literacy gap has consequences across all aspects of society, it is in business and industry that some of the greatest effects are felt.

According to a New Vantage Partners (NVP) report in 2019, among a group of Fortune 1000 business and technology executives, only 31.0% mentioned they are data driven. This number has declined from 37.1% in 2017 and 32.4% in 2018 (NewVantage Partners LLC, 2019), and is clearly moving in the wrong direction.

Organisations are apparently struggling to become data-literate (Damuri, et al., 2022: 33). Universities, in their capacity to shape the workforce and society of the future, have an obligation to develop data-literate graduates but this is challenging (Jones, 2019). ‘How can data literacy education be improved?’ is a question that must be asked. Motivated by the urgency of creating data literate organisations, the authors are undertaking a study to explore the perspectives of different stakeholders on data literacy, data literacy competencies and the challenges of data literacy in organisations.

Context: Data literacy in organisations

The imperative to grow data literate capability extends beyond its importance for organisations. Immersed in a digital world, data literacy is now an essential skill for any society (Radhakrishnan, 2023). This viewpoint is captured in the perspectives outlined in the ‘G20 Toolkit for Measuring Digital Skills and Digital Literacy: Framework and Approach’, as well as in documents like the UNESCO 2018 Digital Literacy Global Framework and the Digital Literacy Index published by the Indonesian Communications and Information Ministry in 2020 (Damuri et al., 2022).

In organisations, it is commonly the task of those in business function roles such as finance analyst, sales analyst, product managers, support analyst, and marketing analyst that have responsibility to utilise data literacy skills to support decision making (Anderson, 2015). Many university graduates, from business programmes, can be found in these roles and many other university graduates from diverse disciplines are employed in various roles in organisations. Yet, despite no shortage of graduates in business and other disciplines. According to research from Gartner the gap between data education needs and implementation is growing (Finerty, 2023). It revealed a gap between the need for data initiatives and implementation with less than half of workers responding that they’ve been offered data training. The research found:

• 79% of leaders say they equip employees with the data skills they need, but only. . .

• 40% of employees say they’re provided the data skills they’re expected to have.

• 93% of decision makers believe that data literacy is relevant to their industry.

• 63% of large businesses plan on increasing the data literacy of their employees.

• Only 47% of employees say they have been offered data training by their organisation.

• Only 34% of firms currently provide data literacy training (Finerty, 2023).

Data Literacy can be defined simply as the ‘ability to read, write and communicate data in context’. (Panetta, 2021: 1). The definition is deceptively simple. The contextual nature of data literacy complicates data literacy education. To make inroads in closing the data literacy gap, it is imperative to recognise data literacy as a construct that deeply contextual in both its understanding and implementation. Understanding of the contextual nature of data literacy is beginning to emerge with some literature suggesting that data literacy education can be achieved successfully if:

• New ways of using data build on students’ prior experience.

• Learning to use data occurs at the same time as learning about a disciplinary subject.

• Learning results in students becoming aware of new ways of using data as well as developing new understandings of the subject being studied (Maybee and Zilinski, 2015).

However, while there is some beginning recognition of the role of discipline in shaping data literacy (Mandinach and Gummer, 2016; Pothier, 2019; Ridsdale et al, 2015), educational approaches remain predominantly a ‘one size fits all approach’. Providing the same education for different industries and possibly for different roles will not be useful. Because data literacy is deeply embedded in context (Panetta, 2021), the ‘one size fits all’ approach to teaching data literacy is evidently unsuitable. The existing gap between organisational needs in data literacy and the data literacy skills of their talent pool may be indicative of disconnect between education and organisational needs. Hence, the authors propose that good starting point for improving data literacy education so that it meets industry needs is to explore the perspectives of key stakeholder groups (industry, students, teachers, researchers) in relation to understanding of

• What is data literacy?

• What competencies are important in data literacy?

• What are the challenges in achieving data literacy skills?

Exploring perspectives will help to identify the needs of the various stakeholders. Comparing and contrasting the different perspectives may help identify gaps in the data literacy education process and provide some insights contributing to efforts in improving data literacy education in university programmes. The research reported in the present article is a preliminary investigation for a larger research project, using grounded theory and aimed at providing a framework to improve data literacy education. In this preliminary investigation the authors sought to expose the perspectives of different stakeholders on data literacy, competencies, and challenges.

Methodology

The study involved purposive sampling of participants who possess diverse backgrounds and expertise in data literacy. The selection criteria for participants was those individuals with experience and knowledge in the subjects related to data skills. Based on the systematic literature review conducted by the author (Ghodoosi et al. 2023) four groups of stakeholders who have an interest in data literacy in the public and academic sector were identified: students, educators, business advisors, and researchers. An effort has been made to choose participants from these four groups.

The participants for the present study consisted of two students currently enrolled in university programmes of study related to data, two business advisors each from two different industry sectors (health and police departments), two educators each from two different Australian universities, from with teaching experience in data analysing and big data, and two researchers specialising in data and information literacy. This diverse group of participants allowed for an exploration of the research questions from multiple viewpoints. As part of the broader grounded theory study, this initial sample, though small, provides a foundation for deeper exploration as the study progresses. Ethical approval was obtained from the relevant institutional review board prior to data collection. Informed consent was obtained from all participants, ensuring their voluntary participation and confidentiality.

Semi-structured (30–45 minutes) interviews were conducted with each participant individually. The interview protocol was designed to address the three main research questions: (1) What is data literacy? (2) What competencies are considered important in data literacy? and (3) What are the challenges associated with data literacy? The interview questions were developed based on the existing literature on data literacy and were reviewed to check for clarity by a business school educator and a business advisor.

To analyse the interview data, a comparative method was used. The comparative method involves a cyclical process of coding through NVIVO, comparing data, and identifying patterns and themes across the dataset. This iterative process facilitates the identification of concepts, enabling the development of a theoretical framework.

Results

In presentation of results, to preserve anonymity, individual participants are referred to as E1 and E2 (educators), S1 and S2 (students), R1 and R2 (Researchers), and A1 and A2 (Advisors).

What is data literacy?

The key themes found in participants’ responses to the question of ‘What is data literacy?’ are summarised in Table 1. Common among students, educators and business advisors was that they had not heard of the term ‘data literacy’.

I did not know data literacy, so when I received your request to talk about data literacy, I started to Google about this and tried to understand what this mean. The basic level of understanding data is exactly the main definition for data literacy. The basic levels for all people to understand the data (E1).

Table 1.

Responses to the question ‘What is Data Literacy’.

Students	Educators	Business advisors	Researcher
✓ Students did not hear about data literacy before. ✓ Students getting wrong the data literacy with data analysing. ✓ Different understanding of DL exists because of different backgrounds.	✓ Educators did not hear about data literacy. ✓ The educator assumed data literacy is a skill or any skill to access data, not a very complicated skill, just a basic one. ✓ Students are not aware of DL role in Businesses. ✓ Different understanding of DL exists because of different backgrounds. ✓ There is no awareness of the need for DL in the field of business.	✓ Data literacy is converting data to information and then converting information to insights. ✓ Staff’s educational backgrounds matter in working with data. ✓ Staff are not familiar with data literacy concept. ✓ There is no awareness of DL importance. ✓ There is no awareness of the need for DL in businesses.	✓ Different definitions exist for different majors and different roles. ✓ The importance of data literacy is not recognised by people.

The researchers knew about data literacy and stressed that awareness of data literacy is important. Among the interviewed educators and students’ emphasis was on technical aspects such as data analysis and processing. Students’ skills in working with data are limited to gathering, storing, and optimising the data. There was no acknowledgement of other skills to make sense of data and use it meaningfully to support decision-making. In other words, there was no awareness of data literacy’s role in businesses. In contrast, the business advisors articulated the necessity of being able to articulate or make decisions based on the analysis of the data.

Courses offered revolved around techniques and tools. Educators mentioned that courses like ‘Big data analytics and social media’ are the most relevant. Educators identified an orientation toward artificial intelligence and machine learning, in educational centres, but not about data literacy.

The fact that is common between all different players is a lack of awareness regarding the importance of data literacy, both in general and within the context of businesses.

Unlike educators where their focus was on teaching technical aspects and how to use tools, business advisors’ understanding of data literacy is the process of transforming raw data into meaningful information and then deriving valuable insights from that information:

Data literacy is how to get information into the data lakes then how to get information out of the data lakes and then how to interpret that information (A1).

They believe the educational backgrounds of their staff members play a significant role in their ability to work effectively with data.

Among the interviewed researchers the prevailing view was different people in different disciplines have different needs in working with data, and at different organisational levels, different data literacy skills are needed. Therefore, researchers believe that aiming to provide one definition for data literacy will not be useful. The researchers believed that there should then be data literacy education tailored for different disciplines.

What competencies are important in data literacy?

After defining data literacy, participants in this survey were asked about the skills they know in working with data and which skills they think they need to learn. Table 2 includes the opinions of students, educators, business advisors and researchers about data-related competencies.

Table 2.

Data literacy competencies.

Students	Educators	Business advisors	Researcher
✓ Critical thinking is not considered in DL education. ✓ No method for data communication. ✓ The most important skill now is how to clean and organise data. ✓ Different skills needed for different businesses.	✓ Visualization, and interaction with data are in the focus of data skills education. ✓ Data storage and accessing the data is the basic skill for students. ✓ Cleaning the data is very important. ✓ Businesspeople need to know data analysis for writing reports. ✓ Business staff must know how to convert data to intelligence.	✓ The process for solving a problem is based on data but not on specific processes: (I would certainly use data to shape the conversations that are helping other people). ✓ We are looking at trends and more dashboard information to make decisions about services and where to send resources and things like that. ✓ Data cleaning and data organising is required.	✓ Different skills are needed for different majors and different roles. ✓ People don’t know how to visualise the data analysis. ✓ Data storytelling is one of the important skills of data literacy. ✓ Data locating, data evaluation, data analysis and making critical decisions based on data are competencies of data literacy.

When each of the participants were asked to talk about data-related skills there was confusion across all the groups of participants. The first skill that all participants in this survey mentioned was data analysis which is a broad concept. But the reason for analysing and the process of analysing the data was not recognised. The next skill that has been taught to the student was data organising, creating the tables and basic level of working with database management systems (DBMS). Students who participated in this survey admitted there is a lack of education about critical thinking leading to weaknesses in developing analytical and problem-solving skills.

All participants agreed on the importance of data cleaning as the first step in working with data. However, students mentioned in data-related courses they were not taught this skill.

Educators who were interviewed think the focus of education in data-related subjects are on visualisation. However, researchers and business advisors pointed out that knowing how to work with different visualisation tools doesn’t necessarily mean people can communicate together based on the data. The students did not hear about data storytelling which from the point of view of researchers and business advisors is the necessary skill for data communication.

Although, all participants agreed upon that different businesses require different skills, they did not have a clear idea about what is the relationship between different skills and different businesses or the differences among different roles within a business.

Finally, all participants agreed that all efforts in data literacy activities must lead to data-driven decision-making. They all mentioned that the goal of different data-related subjects must be critical thinking.

What are the challenges in achieving data literacy skills?

The last questions of this survey were allocated to challenges of working with data. Four groups of participants provided their opinions which are summarised in Table 3.

Table 3.

Challenges in working with data.

Students	Educators	Business advisors	Researcher
✓ In university, in 2 years, it is impossible to learn everything. ✓ In university, there are few insights into business. ✓ Data skills are applied from company to company differently. ✓ Decisions in business are being made only by guessing, and there is no connection between strategies and data analysis	✓ We still don’t have maturity in working with data. ✓ Different people with different roles, have different interests. ✓ Data storage, accessing the data, data visualisation and analysis is very challenging for students. ✓ There are courses about introducing big data, not about DL. ✓ Providing one subject for all students with different backgrounds is challenging.	✓ Organisational staff, who are not data analysts, are not thinking about working with data because they don’t realise its importance. ✓ Skills for working with data is not considered when hiring people except for specific data officer role. ✓ Data entries are not trustworthy as people are not aware of data cleaning importance. ✓ Employees have different understandings of data because they have different backgrounds. ✓ Employees previous experience with data is affecting their data-related skills. ✓ There is so much data, but they are not organised to help make decisions.	✓ Courses are very content-based, and we are not teaching critical thinking. ✓ The first challenge is the awareness of the need for data literacy. ✓ The enthusiasm for working with raw data is decreasing. ✓ Companies are not mature enough yet to use the data to have insights. ✓ Skills for working with data is not considered for hiring people except for specific data officer role.

The first challenge, which also refers to the first step of data literacy, was cleaning the data. All four groups of participants in this survey mentioned data cleaning is one of the most challenging parts of working with data as it is directly affecting the outcomes of data analysis.

Data entries are not trustworthy as people are not aware of data cleaning importance (A1 and A2).

Students mentioned they must deal with an overwhelming amount of information which is hard to clean and organise. They admitted that two years of education is not enough to learn data-related skills, and that the university curriculum may not adequately cover all the necessary aspects of data analysis within the limited time available.

Business advisors, researchers and the educators who were interviewed pointed out the lack of power to analyse the data purposefully or in other words critical thinking. Educators stress the importance of critical thinking in education, as current courses tend to focus more on content rather than fostering these skills.

As the final goal of working with data and the importance of data-related skills is not clear for employees, they are losing interest in working with data. Moreover, all participants agreed upon a lack of connection between business strategies and data analysis, with decisions often made without proper data analysis.

I was working in the bank, and I realized that there was a big lack of how to talk about data. It was much more about guessing the future and decisions were made only by gassing up (S1).

There are some people that technically can analyze the data, but still, they cannot connect the outcomes of their analysis with their job (S1 and S2).

Another challenge is providing a subject which is suitable for students with diverse backgrounds. All participants emphasised on the relationship between students’ background and data-related skills education.

Employees previous experience with data is affecting their data-related skills (A1 and A2).

Providing one subject for all students with different backgrounds is challenging (E1 and E2).

Finally, business advisors and researchers admitted that still skills for working with data is not considering for hiring people except for specific data officer roles which are causing extra expenses for hiring more data scientists.

I don’t think my entire department would ever look at the data! I think they might have the skills, but they rely on their manager to give them an update or highlight them (A2).

And:

Analyzing the data in traditional way by data scientists is costly. You cannot be successful anymore if you cannot get result on real time (A1 and A2).

Overall, these opinions collectively underscore the challenges and gaps in data analysis skills, education, business practices, and data utilisation. They highlight the importance of addressing these issues to bridge the gap between the growing demand for data analysis and the existing capabilities and understanding in various domains.

Discussion

What is data literacy?

The perspectives provided highlight different data literacy (DL) understandings from the viewpoints of students, educators, business advisors, and researchers (Figure 1).

Figure 1.

Data literacy understanding from the viewpoints of students, educators, business advisors and researchers.

There is a gap between simple tasks that students know in working with data and very professional skills that data scientists are applying. Higher levels of data literacy in converting information to knowledge and insights have been neglected. Educators acknowledge that in any teaching and learning, it is important for students to develop critical thinking, but it was not linked to data literacy education; it was more about academic skills. One evident issue in existing conceptualisations of data literacy is the over-emphasis on technical requirements and little regard for the competencies related to the application and use of data (Bhargava et al., 2015). Educating only technical skills in working with data results in students’ incapability to apply their knowledge to real-world scenarios.

From the point of view of researchers and business advisors, companies may not be mature enough yet to use data to have insights. And it is another evidence of not having the necessary infrastructure or skills to work with data effectively.

The suggestion by educators and students for addressing this issue is to involve more business case studies in courses related to data. There is a need to connect data-related courses and businesses insights.

DL education is being shared between universities and organizations. There must be a cooperation. However, the basic education about data literacy should come from the early educational years (R1 and R2).

The perspectives of educators, business advisors, and researchers highlight the need to raise awareness about the importance of data literacy. Without awareness, students and staff may not understand the relevance of data literacy and its benefits to their future career, which is leading to a lack of interest in the subject. Employees and their managers should understand the benefits of data literacy. They should have clear answers to ‘What’s in it for me?’ and ‘How does the training data literacy relate to my current or future role?’ (Panetta, 2021).

Considering that many employers feel recent graduates are not coming out of higher education with data literacy skills, integrating data literacy competencies into different disciplines of schools’ education would help solve the workforce issue of poorly skilled talent to fill jobs that require data-related skills (Pothier and Condon, 2020). Educational centres need to focus more on providing students with practical skills related to working with data from the real world and based on students’ opinions of this survey only one or two courses are not enough. It is believed that working with data should be woven into all courses.

Data literacy competencies

Regarding the data literacy competencies there are similarities and differences between different interviewees as is shown in Figure 2.

Figure 2.

Data literacy skills from the viewpoints of students, educators, business advisors and researchers.

Data cleaning is an indispensable step in the data analysis process. It lays the foundation for reliable, accurate, and meaningful results. Without it, the outcomes of data analysis can be compromised, leading to incorrect decisions, unreliable insights, and wasted resources. Therefore, data cleaning is an essential process to ensure the trustworthiness and integrity of your data analysis. Based on the business advisor’s experience data cleaning is more sensitive among different data literacy competencies:

Because it still relies on people doing good data entry, and sometimes people’s ability to do good data entry or enter their statistics accurately is not good enough, so you cannot really trust what comes through on the dashboard (A1).

Data visualisation is the next data literacy competency that has been emphasised by educators and researchers.

Different people have different interests. So, in work with data, people interaction is very important (E1 and E2).

Noting a lack of knowledge in visualising data analysis points to a potential gap in effectively communicating findings through visual representations. Therefore, researchers and business advisors consider data storytelling as an important skill that highlights the ability to effectively convey insights derived from data. More recently, data storytelling has been given increasing attention to empower learners, personal and critical data literacies, as well as the use of open data as open educational resources to promote both technical skills as well as civic education (Raffaghelli, 2019).

It is argued that one of the most important goals of data literacy should be fostering critical thinking to keep people realistic and asking questions, not only accepting information at face value, as well as the skills for understanding data’s underlying meaning (Koltay, 2015; Ridsdale et al., 2015). The student perspective raises the concern that critical thinking is not adequately considered in data literacy education. The lack of emphasis on critical thinking can lead to students not being able to apply their knowledge in real-world scenarios, and educators may not be able to effectively teach data literacy concepts. Therefore, it is essential to incorporate critical thinking into DL education. Converting data to intelligence highlights the desire to move beyond data analysis and derive actionable insights.

The emphasis on data-driven problem-solving indicates a recognition of the value of using data to guide business decision-making processes. The business advisor from the health thinks the use of trends and dashboard information for decision-making suggests a reliance on data visualisation of data to inform business strategies. Purposeful data analysis and using data to inform decision-making processes were considered very important by all groups of participants. Data literacy researchers such also emphasised on data-driven decision-making as the goal of applying different data literacy competencies (Deahl, 2014; Mandinach et al., 2013; Vahey et al., 2012; Wolff et al., 2016). In the era of big data, businesses are trying to become more data-driven and increase their decision-making efficacy (Jia et al., 2015). However, interviewed students mentioned data-driven-decision-making (DDDM) has been neglected in data-related courses.

Finally, students, educators, business advisors and researchers agree that different skills are required based on the specific context, such as different businesses, majors and roles. The same opinion has been provided from the research about the concept and nature of data literacy. Panetta (2021) in Gartner emphasised, data literacy is deeply contextual (Panetta, 2021). Therefore, one general categorisation of data literacy competencies is not possible or not useful. Therefore, further research is needed to explore these differences based on the needs of different disciplines and application contexts.

What are the challenges in achieving data literacy skills?

The opinions from students, educators, business advisors, and researchers provide valuable insights into the challenges surrounding data literacy. Five themes have been recognised in challenges:

- Difficulty in working with data (high volume of data).

- Challenges in learning and education.

- Business Perspective in working with data.

- Data Quality and Trustworthiness.

- Maturity and Insight Generation.

Table 4 summarised the challenges in working with data from students, educators, business advisors and researchers’ perspectives.

Table 4.

Data literacy challenges from the viewpoints of students, educators, business advisors and researchers.

Data literacy key stakeholders	Difficulty in working with data (high volume of data)	Challenges in learning and education:	Business perspective in working with data:	Data quality and trustworthiness:	Maturity and insight generation
Students	Data analysis is difficult to develop, and dealing with a large volume of information is challenging.	Students feel it is impossible to learn all aspects of data analysis quickly.	Business decisions are often made without data analysis, and there is little connection between business strategies and data analysis.	NA	NA
Educators	Students find data storage, access, and visualisation challenging due to the volume of data.	Providing a subject suitable for students with diverse backgrounds is challenging.	People in business roles struggle to use data for analysis and reporting purposes.	NA	NA
Business advisors	Data are abundant, but it is not organised effectively for decision-making.	NA	Employees in organisations, except for specific data officer roles, lack skills and interest in working with data.	Employees have different understandings of data, and data cleaning is not given enough importance. Data accuracy can sometimes be questionable.	Organisations lack maturity in utilising data for generating insights.
Researchers		Courses need to focus on critical thinking and creating awareness about the importance of data literacy.			Companies are not mature enough to effectively utilise data for gaining insights.

One common challenge identified among participants is the lack of data literacy and critical thinking skills, as well as a lack of awareness of the importance of data analysis.

Both students and educators point out that DL concepts can be challenging to grasp due to different backgrounds. Students with different backgrounds have different understanding and different maturity levels when working with data. Wolff et al. (2016) identified four categories of data-literate citizens which convey the varying depths of skill required by individuals based on the extent to which they engage with data (Wolff et al., 2016).

There are different tools for working with data. But for working with them, basic level of IT skills is needed. And it is challenging for business students with no IT background (E1 and E2).

Providing a general course about data analysis for all students results in some students struggling with the material or being unable to see how DL applies in real-world scenarios. Educators must be mindful of this issue and design their courses to accommodate different backgrounds and learning styles based on the different organisational roles.

As the educator mentioned during the interview, data literacy’s basic concepts must be taught in educational centres, related to discipline, and based on students’ backgrounds. More education can be provided later during the job based on organisational roles:

Education about data literacy is better to start from the high school with basic skills. Professional skills can be learnt later based on the jobs’ requirements during the work. It is important for students to have a concept when they start working. Later they can go deeper (R1).

On-the-job data literacy training programmes also can help create the environment where learning data and analytics skills and acquiring data literacy knowledge is a part of organisational culture (Panetta, 2021). It is necessary to provide staff members with adequate training based on their backgrounds and natural differentiation in the specific roles to ensure they can work with data effectively (Voulgaris, 2014).

Students who were interviewed for this survey did not pass any courses related data communication and shaping insights from data. They thought they are not ready to work with data as needed in the job market, as companies are demanding staff with skills for data visualisation and data communication methods such as data storytelling (Boldosova and Luoto, 2019).

Another challenge for students is inconsistency between different courses related to data analysis. Students mentioned there are very practical courses that need working with raw data. Whereas other courses focus only on theory and ignore practical experience.

Despite these challenges, there are some positive developments, such as the promotion and support of data analytics teams within organisations and the availability of courses on big data and data analysis. Educators and business advisors are also finding ways to provide guidance and support for their students and employees to improve their data skills. Meetings and discussions are being held to address barriers to accessing and utilising data effectively. However, based on researchers’ and business advisors’ comments, it appears that there is still much work to be done to improve data literacy. Basic levels of data literacy skills must be taught in educational centres. The awareness about data literacy must be created by weaving the data literacy into the body of all courses.

Conclusion

Although various data-related courses are being provided in higher education, organisations are struggling to hire talent who can fill the roles that progresses the organisation toward becoming data-centric (Pothier, 2020). There is therefore a need for improving data literacy education so that the capabilities of graduates better align with the data literacy needs of organisations.

On the premise that a foundational step to improving data literacy education is exploring the data literacy perspectives of key groups of stakeholders, the authors conducted in-depth, open-ended interviews with eight participants, from a convenient sampling from business, education (students and educators), and research. The sampling was randomly based on existing connections with Business school, health and police departments. Through interviews, the perspectives of the stakeholders were explored with respect to:

(1) What is data literacy? (2) What competencies are important in data literacy? and (3) What are the challenges in achieving data literacy skills.

The comparison between different perspectives is helping to root the existing gap between employers’ expectations and graduates’ data-related skills. Findings of our present research emphasised the importance of promoting awareness about the importance of data literacy among policymakers, educators, and the public to garner support for data literacy initiatives. Although the research undertaken is small-scale because the small number of participants from a few specific organisations and institutions, the results reinforce the notion that an interdisciplinary approach to data literacy must be considered and students must be provided with opportunities to work with real-world datasets, engage in data-driven projects, and use data visualisation tools to learn how to make data-driven decisions. Developing critical thinking skills alongside data analysis skills must be considered in data literacy education. Adapting these recommendations to the specific needs and contexts of different educational institutions and regions can contribute to a more data-literate workforce and society.

Future research will be needed to investigate the relationship between data literacy competencies and organisational roles in specific domains.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Bahareh Ghodoosi

Author biographies

Bahareh Ghodoosi is a passionate advocate for data literacy education, currently pursuing her Ph.D. at Griffith University. With a focus on empowering individuals with the skills to navigate the data-driven world, Bahareh’s research delves into innovative approaches to enhance data literacy among diverse populations. Her dedication to bridging the gap between data and understanding has led to impactful contributions in the field, Through her work, Bahareh aims to equip individuals with the knowledge and tools necessary to make informed decisions and thrive in today’s data-centric society.

Geraldine Torrisi-Steele, PhD, is an associate professor in the School of Information and Communication Technology at Griffith University, Queensland, Australia. She is a Senior Fellow of the Higher Education Academy (UK). Her interests in learning and teaching, curriculum design in higher education, technology in learning contexts, human computer interaction, as well as educational leadership distil from her ‘pre-academia’experiences in high school maths and science teaching and her experiences the design, development, and project management of digital interactive media for learning in higher education. Cutting across her interests is a strong belief in designs with ‘the human at the centre’.

Tracey West is known for her research expertise in financial literacy and gender issues facing retirement savings policy and financial education, as evidenced by 18 published articles. Her research has analysed the gender differences in financial literacy measurement, the nexus of life events and financial literacy for resilience and wealth outcomes, and financial risk taking. Her PhD was on household financial decision-making, and she has numerous publications on household wealth, financial literacy, financial socialisation and financial planning issues. Recent work has been published in the Australian Journal of Management, Economic Notes, Financial Counselling and Planning, Financial Planning Research Journal, Journal of Family and Economic Issues, JASSA, and the Consumer Interests Annual. This work contributes to knowledge on consumer behaviour, informing national financial literacy strategy development, financial education curriculum and guidance for industry. She currently teaches Behavioural Finance and Wealth Management at Griffith University, Australia.

Maryam Heidari is known as a researcher and lecturer in the field of Information Systems. Holding a PhD from Griffith University, her expertise lies in IT services within higher education. Dr Heidari’s research focuses on IT value co-creation, co-destruction and service ecosystem well-being, bridging academia and industry for impactful insights.

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Perceptions of data literacy and data literacy education

Abstract

Keywords

Introduction

Context: Data literacy in organisations

Methodology

Results

What is data literacy?

What competencies are important in data literacy?

What are the challenges in achieving data literacy skills?

Discussion

What is data literacy?

Data literacy competencies

What are the challenges in achieving data literacy skills?

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Author biographies

References