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Building an Inclusive Data Literacy Community

Published onJan 30, 2025
Building an Inclusive Data Literacy Community
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Abstract

This article outlines the essential elements of an inclusive data literacy community to address the data literacy gap in organizations. After delving into the opportunity presented by improving data literacy, it elaborates on the foundational concept of community in relation to culture and concludes by offering practical insights into establishing an inclusive and sustainable data literacy community.

Keywords: data literacy, community, culture, role definition


1. Introduction

With the rapid growth of data in today’s digital landscape, data literacy is becoming essential for a broad range of roles across industries. The International Data Corporation (IDC) projects that global data production and storage will surge from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025 (Reinsel et al., 2018). For reference, a zettabyte of data is roughly equivalent to about 250 billion DVDs (Cisco, n.d.). By 2025, it is estimated that 70% of employees will rely heavily on data in their roles, an increase from 40% in 2018 (Forrester, 2022). Moreover, 82% of surveyed decision-makers now expect all team members to have foundational data literacy, highlighting the growing demand for these skills across the workforce (Forrester, 2022). As data continues to shape the future of work, building a data literate workforce is increasingly critical for organizations to stay competitive.

To effectively promote data literacy in an organization beyond a discrete, individual pursuit, it is crucial to think critically and strategically about how to build an inclusive data literacy community focused on helping individuals improve their ability to understand, use, and communicate with data (Compton, 2020; Neville, 2022). An inclusive data literacy community embraces growth, accommodates varying skill levels and perspectives, welcomes individuals in all roles and levels, and encourages open interactions among its members. Such a community makes it possible for data literacy to become a shared endeavor between the individuals and the organization, benefiting both parties. Regardless of their role, data and analytics skills, frequency of data use, and depth of their interaction with data, each member of an inclusive data community understands and acknowledges that their voice and perspective is valued. While not every employee will (or should) attain the same level of data and analytics skills, each is given the opportunity to reach the level that is appropriate for their job, allowing room for professional growth and continued learning.

Moreover, an inclusive data literacy community facilitates activities that enable members to share experiences, ask questions, learn new information and skills, and propose solutions in a safe and welcoming environment. These activities accommodate diverse learning styles, incorporating elements such as arts, audio, visual aids, technology, tactile objects, and creativity. Given the significance of data in business settings for tracking performance and evaluating effectiveness, the community must communicate the benefits of data and establish a threat-free environment. Embracing growth and welcoming uncertainty fosters a genuinely inclusive community. Additionally, a thriving community facilitates open interactions not only during meetings but also asynchronously outside of them. Members engage in group or one-on-one discussion for various purposes, including seeking feedback, collaborating on special interest projects, or sharing lighthearted content to foster camaraderie.

The aim of this article is to explore the essentials of building an inclusive data literacy community as part of a holistic solution to address the data literacy gap in organizations. After delving into the opportunity presented by improving data literacy, it proceeds to elaborate on the foundational concept of community in relation to culture, and concludes by offering practical insights into establishing an inclusive data literacy community.

2. The Data Literacy Opportunity

Before going further, it is important to offer clarity on the terms ‘data’ and ‘data literacy.’1 We define data broadly, inclusive of qualitative and quantitative data in any format, as “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation (…) and must be processed to be meaningful” (Merriam-Webster, n.d.). While acknowledging that the details of what constitutes being data literate may vary by position or role, we understand data literacy itself to be “the ability to access, critically assess, interpret, manipulate, manage, summarize, handle, present, and ethically use data” (Okamoto, 2017, p. 120). This definition goes beyond other definitions focused on ‘read-write-speak,’ emphasizing an action-oriented approach and the ethical use of data, which are essential components of data literacy. With its more encompassing definition, Okamoto’s concept also lends itself well to inclusive data literacy; it is something for everyone.

The growing recognition of data’s pivotal role in informing decision-making and business strategies increases the need for improved data literacy in organizations. However, a gap exists between expectations for data literacy and the current reality of a data literate workforce (Ghodoosi et al., 2024). While leaders want employees to be data literate, there is frequently a lack of support in developing these skills among the workforce. According to The State of Data Literacy report by DataCamp, which surveyed 558 business leaders in the United Kingdom and the United States, 54% acknowledged a data literacy skill gap in their organizations (2023, p. 14). In a global survey of 6,000 employees and 1,200 C-level executives, 85% of C-level executives emphasized the importance of data literacy for the future workforce (Qlik, 2022); yet only 11% of employees felt fully confident, and 31% somewhat confident in their data skills. Moreover, just 21% believed their employer was doing enough to help prepare them for a data-centric workplace (Qlik, 2022).

While efforts to foster data literacy are growing, many fall short of comprehensively addressing the existing gap. Data literacy education for undergraduate students falls short of workplace needs, so many enter the workforce lacking essential data literacy skills (Ghodoosi et al., 2023; Pothier & Condon, 2023). On the job, often training is prioritized for employees in data-specific roles, neglecting a broader, enterprise-wide approach (Qlik, 2022). The State of Data Literacy report highlights that "enterprise-wide data upskilling has yet to become a priority for most organizations today," in part due to challenges such as lack of budget, inadequate training resources, lack of executive support, lack of ownership of the training program, and employee resistance (2023, p.24). Closing the data literacy gap requires a strategic commitment to cultivating a data-empowered workforce, promoting an environment for learning, and building the capacity of individuals at all levels to work effectively with data.

It is essential to recognize that data literacy is a vital skill for everyone in the organization, across all sectors and departments. This is what is meant by the term ‘inclusive data literacy.’ Gartner asserts that data is at “the heart of digital transformation and requires all employees to be information workers who can ‘speak data’” (Duncan et al., 2023, p. 1). The State of Data Literacy report by DataCamp elucidates the opportunity inherent in the present data literacy gap: “Companies today are collecting an ever-expanding amount of data. Yet, the value of the data remains untapped without the necessary data skills, culture, and mindset” (2023, p. 4). To put concrete numbers to the opportunity here, a survey by IDC found “a 46.2% improvement in financial, customer, and employee metrics at data-leading organizations,” which shows the tangible benefits that strong data literacy practices can yield (p. 15). Data literacy presents both individual and collective opportunity.

However, achieving widespread data literacy is not a one-size-fits-all process, requiring both investment and modeling by leadership, among other factors. A key step in overcoming this challenge is identifying how data literacy can enhance the work of every employee within the company, including its leaders. This inclusive approach not only democratizes data usage but also empowers every team member to contribute more effectively to the organization's goals. For instance, human resources professionals can use data to refine employee performance metrics, track market trends, analyze recruitment efforts, and optimize resources. Similarly, marketing and communication teams can leverage data to evaluate campaign success, gain deeper insights into their audiences, and assess the impact of their messaging. Facilities staff use data to manage maintenance schedules, track repairs, and ensure that buildings and equipment are operating efficiently. Identifying the specific opportunities presented by increased data literacy across sectors and departments is helpful in generating engagement and buy-in across the organization.

3. Data Communities and Cultures

While it is commonplace to hear of the pursuit of a data-driven ‘culture,’ the intentional use of the word ‘community’ over ‘culture’ in this article emphasizes the vital role everyone plays in advancing data literacy and the agency afforded to every individual, regardless of their position as a leader, data analyst or practitioner, data consumer, or even someone whose role appears to have little to do with data. Further, the development of a data community is often a building block and required step toward achieving a wider data culture.

Although culture and community are closely linked, they remain distinct concepts. Culture is commonly understood as the beliefs, attitudes, rules, customs, practices, and social behavior of a large or overarching group or society (Coleman, 1988; Faulkner et al., 2005; Kroeber & Kluckhohn, 1952). Community, on the other hand, typically refers to a group of people united by a common interest, goal, or characteristic, fostering a sense of belonging and connection based on these shared attributes or objectives. McMillan and Chavis (1986) emphasize that “communities organize around needs, and people associate with communities in which their needs can be met” (p. 15–16). Culture provides the overarching context within which specific communities, with their unique interests, are situated (Geertz, 1973). Components of culture, such as social norms and trust, facilitate the formation and sustainability of communities (Coleman, 1988) by influencing the interpersonal interactions and highlighting the interconnection between these two fundamental concepts (Faulkner et al., 2005).

Similar distinctions exist in the context of cultures and communities related to data literacy. ‘Data culture’ and ‘data-driven culture’ are frequently employed to describe the collective behaviors, values, and beliefs that shape how an organization views and uses data. This encompasses how data is integrated into the daily workflow, decision-making processes, and overall strategy of an organization (Anton et al., 2023). Organizations with a strong data culture are more effective and efficient, leading to revenue, profit, and growth (Southekal, 2022). As with any community, a data community may exist within a larger culture where data use is integral to the organization’s ethos or within a culture where data is not highly valued. However, a data community may struggle to persist in a weak or nonexistent data culture (Murray, 2022). Nevertheless, the presence of a vibrant data community can strengthen data literacy within an organization by providing specific, often grassroots, support and learning on a personal or departmental level.

Different yet overlapping models define what constitutes a strong data or data-driven culture in an organization. For example, Millan (2020) identifies technological and human resources as foundational for a strong data culture. These include, “data collection, management, analysis, reporting and interpretation”—essentially, data literacy. Kesari (2021) outlines four “key signals” of a data culture: executive ownership and modeling of data use; data champions who evangelize the use of data within and across teams; freely shared, trusted, accessible data; and the recognition that data literacy is essential to all employees. Aryng (2020) identifies “4 D’s” of data culture: data maturity, data-driven leadership, data literacy, and a data-informed decision-making process. Birkett (2020) defines “7 pillars” of a data-driven culture: precise, accessible, trustworthy data; an investment in data literacy for everyone; defined key metrics; acknowledgement of failure; comfort with uncertainty; investment in technology; and experimentation and autonomy. A common component among these models for data or data-driven culture is data literacy. Data culture faces a threat from limited data literacy, and overcoming this challenge is key to fostering a successful data culture (Crabtree, 2023). In other words, inclusive data literacy—data literacy for all members of the organization—is critical to a data or data-driven culture.

A ‘data literacy community’ refers to a group of individuals (within a larger culture) who share a common interest in improving their ability to understand, use, and communicate with data (Compton, 2020; Neville, 2022). These communities are often initiated by those with a preexisting passion for data, with members ranging in skill from novice to expert. These communities engage in various activities, including lunch-and-learns, office hours hosted by skilled employees, workshops and training, collaborative projects, and internal user groups. They provide a platform for knowledge sharing and practical, hands-on learning experiences in data literacy and a place where social learning can freely and purposefully occur (Wenger, 1998). Organized and structured with processes, scheduled events, and members eager to grow in data literacy (Murray, 2022), data communities are seen as a potential solution to the data literacy gap and growing a larger data culture (Compton, 2020).

4. Essentials of an Inclusive Data Literacy Community

Having provided an initial understanding of an inclusive data literacy community and the opportunity presented by data literacy, we shift focus to providing insights on the essentials of transitioning to an inclusive data literacy community. Our approach defining these essentials is informed by insights from extant models (e.g., Team Onion, n.d.), the literature (e.g., Wenger-Trayner et al., 2023), and our shared experiences in the pursuit of data literacy. These essential building blocks (see Figure 1) include: role definition, shared language and objectives, thoughtful implementation, and sustainability.

Figure 1. Essentials in an inclusive data literacy community.

4.1. Role Definition

For the work toward establishing an inclusive data literacy community to begin, there first needs to be at least one champion invested in and motivated by the possibility of creating an inclusive data literacy community. That champion can be someone in any role, but what matters most is their commitment to fostering inclusive data literacy and a willingness to initiate and lead efforts toward achieving this goal. This champion serves as the catalyst for these efforts, but it is crucial to recognize that these efforts must be augmented by the collaboration and commitment of others to succeed. In the workplace, interaction with data is often misconstrued as a linear process, where decision-makers pose a question or problem, technical workers engage with data and present it to decision-makers, and action is taken. In successful data organizations, however, this process is iterative, rather than linear, and is a collaborative process where everyone plays a role. The iterative, collaborative nature of successful data efforts within the organization also characterize the roles and activities of individuals in an inclusive data literacy community.

As part of building the foundation for an inclusive data literacy community, it is vital to identify and define roles for understanding how each member contributes, at the same time maintaining awareness that roles are not static and evolve over time. Interaction across roles is a defining characteristic of an inclusive data literacy community, with all members recognizing the interdependence of their roles, engaging in continuous cycles of feedback and collaboration, and learning from one another. In essence, this models the way people should interact around data in larger organizations. Within the community, this fosters openness and transparency, facilitates communal growth through shared experience, contributes to a sense of belonging crucial for inclusivity, and maximizes the possibility of the data-driven achievement of organizational objectives.

In an inclusive data literacy community, members may take on a variety of responsibilities (e.g., Communities Reinvented, 2021; Wenger-Trayner et al., 2023). In addition to the champion who serves as the catalyst, we highlight three key roles (depicted in Figure 2) that reflect concepts from the Team Onion model:

  • Community Sponsor. As key members in an inclusive community, sponsors are advocates for data literacy and promote its importance within the organization. Their leadership helps drive the momentum and sustainability for an inclusive data literacy community. The sponsor does not have to be the same champion identified above; however, it is likely that the champion identifies the sponsor (or sponsors) as part of catalyzing this effort. Once identified, sponsors lead the strategy and vision of the inclusive data literacy community. They support the community by funding events or activities and by encouraging their teams to take time to engage in community activities. They may not attend many meetings, but they understand the importance of an inclusive data literacy community and are invested in its success.

  • Community Facilitator. Every community member should have the opportunity to step into the role of facilitator. Based on their own experience and expertise, a community facilitator plans activities and agendas, helps lead discussions, stewards community meetings, answers community questions, presents data literacy stories and skills from their perspective, and purposefully broadens the collective group’s understanding of data literacy. There is no formal organizational business title required of community facilitators; nor is the community facilitator role a static one. In fact, a general long-term goal of an inclusive data literacy community is that each member has an opportunity to serve as facilitator for the community, regardless of past data experience, title, or degree.

  • Community Participant. The bulk of a community will fall into the category of participant. Community participants take part in community meetings and collaborate with others in the community. While they may not hold formal leadership roles or responsibility in setting meeting agendas, they contribute to the community by sharing their ideas, data challenges, successes, and questions. Active, engaged participants are the lifeblood of an inclusive data literacy community and sustain its energy and purpose through such interactions.

Figure 2. Roles in an inclusive data literacy community.

An inclusive data literacy community offers fluidity in these roles, especially in moving between community facilitator and participant. This flexibility leaves room for individual growth, as well as diversity in and expansion of community voice, discussion topics, and perspective. It also seeds the broader organizational culture mindset shift of understanding the importance of data for each employee.

4.2. Shared Language and Objectives

At the outset, those seeking to establish a data literacy community must define terms and set goals to create a foundation for shared language and mutual understanding around data and data literacy. For example, it is essential to collectively define the terms ‘data’ and ‘data literacy’ for that specific community, to establish a shared understanding of what is meant and included by these terms. From here, subsequent issues relevant to your particular context will emerge and will include collectively defining and clarifying other terms, aligning on the goals and objectives of the community, and codifying member expectations around the purpose and organization of the data literacy community. As Cadiz asserts, “even though the expertise will vary (…) as long as a member has a grasp on the common jargon of the community, interaction and learning can take place” (Cadiz, 2009, p. 1041). Efforts to establish alignment and shared language must be inclusive, soliciting and incorporating feedback and perspectives from all community members. The aim is to involve everyone in the pursuit of data literacy and the language used in this endeavor is instrumental in fostering inclusivity within that community.

As a case study around the importance of shared language in supporting the sustained pursuit of data literacy, in 2021 Teach for America launched a major initiative to align on detailed, organizational data fluency2 standards (E. Hilty, personal communication, April 13, 2024). These standards provided a strong foundation to grow and define their existing data fluency efforts, allowing participants to specifically name their current skills, as well as identify the places they needed to grow and what resources existed to help them do so. This effort also supported the expansion of data fluency efforts to additional data literacy communities of staff members by connecting the pursuit of data fluency explicitly to organizational objectives, as well as providing a framework for connecting individual and team responsibilities to specific data fluency domains and associated skills.

4.3. Thoughtful Implementation

When shifting from conception to implementation, it is crucial to thoughtfully consider how best to bring a community together in a specific context. Research highlights the need to intentionally build and support organizational communities of practice to increase value and create purposeful learning environments (Nithitha Chinnapat et al., 2016). Understanding the organization’s current state is essential to mapping a clear path toward creating an inclusive data literacy community. This can be accomplished by exploring the current levels of data literacy within the community, potentially via a survey or needs assessment administered to community members and soliciting input on their learning needs or knowledge and skills gaps (Morrow, 2021). To illustrate, at an international finance firm, the identification of the opportunity presented by prioritizing data literacy was actually completed by an external consultant, an example of an outside force being the catalyst, leading to the hiring of a learning and development professional who used that landscape analysis and needs assessment to thoughtfully curate data literacy tools and establish intentional data literacy communities (E. Hilty, personal communication, April 13, 2024).

Another consideration is whether there are existing leaders who can advocate for the community and help identify resources and contributors. Are there individuals or groups within the organization that excel in areas identified in the needs assessment who can share their work and expertise? For example, in the initial data literacy program at Teach for America mentioned above, the program made liberal use of the regional staff members who were already doing amazing data work for their teams, providing the opportunity to share those practices with other teams and think about broader applications and knowledge sharing. As another example, at Northeastern University, community members were asked to recommend individuals within the organization whom they recognized as doing interesting data work and from whom they would like to learn (S. Gracia, personal communication, April 29, 2024). This generated a rich list of internal experts who were invited to share their work and expertise with the community.

It is also helpful to reflect on past initiatives from which to draw lessons and insights to help develop a clear path forward. At Northeastern University, community members were surveyed about past initiatives and asked to identify what had been most and least successful and why, as well as what they hoped to get out of a new data community. This was instrumental in establishing priorities and future plans (S. Gracia, personal communication, April 29, 2024). Once an initial plan is in place, communication and engagement across all roles should be prioritized to ensure that everyone in the data community is on board for implementation. At the finance firm referenced above, the data literacy lead engages in regular evaluation of the efficacy of the programs to support continuous improvement, as well as uplifting successes and continuing to message about the connection of the pursuit of data literacy to organizational progress (E. Hilty, personal communication, April 13, 2024).

By way of example, in a large global manufacturing company, the data literacy community was launched following a companywide data and analytics event (V. Vilski, personal communication, April 28, 2024). Before the companywide event, the community champions and a sponsor came together and agreed on the mission of the community: to help employees find an internal home base for broadening their mindset, language, and skills around data literacy. The community champions identified key leaders at the data and analytics event who exhibited strong data literacy skills, and those individuals received a special invitation to be the first members (facilitators and participants) of the community. As these key community facilitators and participants continued their involvement in the data literacy community, they shared its mission and successes with those around them. This data literacy community grew rapidly through word of mouth and by facilitating engaging, crowdsourced presentations from colleagues.

4.4. Sustainability

To foster a thriving inclusive data literacy community with long-term viability, it is essential to periodically revisit and update foundational principles, roles, and plans. It is important to analyze both successes and failures and share lessons learned within the community. This process inherently involves a certain vulnerability. At the community level, this openness and vulnerability sets an example for individual growth and success. Each member should recognize that there is always room to enhance their approach to data, engage more effectively with audiences, and become more informed and critical consumers of data. No member’s contribution, regardless of role or expertise, should be minimized or overlooked, as each person plays a role in the collective journey toward data literacy.

As community members grow more confident and invested, they may step into facilitator roles, allowing current facilitators to take on other roles in the community. This rotation of responsibilities is yet another way that the community becomes self-sustaining and continues to thrive, despite the reality of employees leaving the community or organization. New facilitators bring fresh perspectives and add to the community’s diversity and depth of knowledge, enabling more members to feel capable and motivated to work with data in their roles.

Ultimately, as this inclusive data literacy community matures and extends its impact, it can evolve into an organizational culture where data literacy is embedded across all functions. Here, the distinctions between community and culture blur, and the goals of the data literacy community extend beyond the initial intention of promoting basic data literacy to interested individuals to the comprehensive, data-driven pursuit of broader organizational objectives. Measuring the success of an inclusive data literacy community involves evaluating how well it aligns with business objectives, meets community and individual goals, and fosters inclusivity among members. While established methods of assessing data literacy initiatives are important for an organization to measure impact (Ghosh, 2023; Morrow, 2022), a nuanced approach that evaluates “how a community has contributed to a difference that people and their organizations care about” (Wenger-Trayner et al., 2023, p. 212) more realistically measures the qualitative impact of an inclusive data literacy community. The evolution of an inclusive data literacy community into a broader data culture marks the success of the community-building efforts and represents a significant milestone whereby data becomes an integral part of everyone's work life.

4.5. Additional Types of Data Literacy Communities

This article focuses on the creation of thoughtful, inclusive data literacy communities within organizations. However, it is important to recognize that data literacy communities can exist in other contexts, such as across organizations (for example, through conferences, workshops, or other virtual and in-person convenings) and as part of an engaged society of data literate citizens. While champions and sponsors may have less control over the creation and implementation of these communities than what is described here, these types of communities are also essential in the larger pursuit of inclusive data literacy and can hopefully utilize many of the concepts shared here.

5. Conclusion

In conclusion, an inclusive data literacy community exhibits key markers that advance individual data literacy as well as contribute to organizational success. Building an inclusive data literacy community fosters a supportive ecosystem where every member, irrespective of their role, can contribute to and benefit from a collective understanding and application of data. The journey from recognizing the opportunity presented by data literacy to establishing a thriving community necessitates clear communication, role definition, intentional actions, and a commitment to long-term value and inclusivity. Through defined roles, shared language and objectives, thoughtful implementation, and a focus on sustainability, inclusive data communities help transform the abstract idea of data literacy into a tangible, dynamic force that propels both personal growth and business success. By prioritizing community building alongside discrete skill development, the importance of individuals and the larger, collective endeavor of fostering data literacy are both highlighted.


Acknowledgments

The authors are exceedingly grateful for the initial thought partnership of Aleksandar Lazarevic, Emma Marty, Christian Pinedo, Judi Spaletto, and Michael Workman, as well as the Institute for Experiential AI at Northeastern for convening us all at the Data Literacy at Scale conference in December 2023.

Disclosure Statement

Emily Cross Hilty, Veronica Vilski, Sanjay Mishra, Patricia Condon, and Susan Metzger Gracia have no financial or nonfinancial disclosures to share for this article.


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Appendix

Table A1. Definitions of key terms.

Term

Definition

Community

A group of people united by a common interest, goal, or characteristic, fostering a sense of belonging and connection based on these shared attributes or objectives.

Culture

The beliefs, attitudes, rules, customs, practices, and social behavior of a large or overarching group or society (Coleman, 1988; Faulkner et al., 2005; Kroeber & Kluckhohn, 1952).

Data

Factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation (…) and must be processed to be meaningful” (Merriam-Webster, n.d.).

Data culture or data-driven culture

The collective behaviors, values, and beliefs that shape how an organization views and uses data.

Data literacy

“The ability to access, critically assess, interpret, manipulate, manage, summarize, handle, present, and ethically use data” (Okamoto, 2017).

Data literacy community

A group of individuals (within a larger culture) who share a common interest in improving their ability to understand, use, and communicate with data (Compton, 2020; Neville, 2022).

Inclusive data literacy community

Embraces growth, values varying skill levels and perspectives, welcomes individuals in all roles and levels, and encourages open interactions among its members; ensures that data literacy becomes a shared endeavor, benefiting both individuals and the organization.


©2025 Emily Hilty, Veronica Vilski, Sanjay Mishra, Patricia Condon, and Susan Metzger Gracia. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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