The Struggle for Memory: Historical Erasure, Whitewashing, and Narrative Authority

 

By Lilian H. Hill

 

Historical erasure refers to the deliberate or unintentional exclusion of certain events, people, or perspectives from the historical record. Authoritarian politicians engage in historical erasure to avoid confronting past, ongoing, and future injustices. Assaults on historical truth serve a purpose. They prevent people from understanding that discrimination, especially sexism and racism, is systemic and has been maintained through centuries of law, policy, and violence (Walk, 2025). The erasures also deny people access to models of courage and organized resistance, such as Harriet Tubman and the Underground Railroad. Stanley (2024) states that authoritarian regimes often view historical accuracy as threatening because it challenges the cultural narratives they promote to maintain control and legitimacy.

 

Historical erasure results in systematic neglect of marginalized voices, including Indigenous peoples, enslaved individuals, religious minorities, women, LGBTQ+ communities, and colonized populations in textbooks, public monuments, and official histories. For example, history education may omit the contributions of Black Americans to the civil rights movement or the widespread violence against Indigenous populations in settler colonial states. Women’s contributions are diminished or attributed to men. Similarly, working-class uprisings and labor movements are often excluded from mainstream historical narratives.

 

Historical whitewashing and erasure are interconnected processes that involve the distortion, omission, or manipulation of historical facts, often to uphold dominant narratives while marginalizing or silencing others. Whitewashing is a specific form of distortion in which troubling aspects of history, particularly those involving white or colonial powers, are sanitized or reinterpreted to make them appear more acceptable or less violent. Historical whitewashing sends a clear message that only white men are recognized as belonging. This can include downplaying the brutality of slavery or even suggesting it benefited those enslaved, portraying Christopher Columbus as a heroic explorer while omitting his role in the exploitation of Indigenous peoples, or reframing colonialism as a ‘civilizing mission’ rather than a system of exploitation and oppression. Contemporary examples of “rooting a current policy in a made-up history” (Cox Richardson, 2025) include efforts to ban discussions of systemic racism in schools by framing the United States as having always been a perfectly just society. Another example is attempts to justify voter suppression laws by referencing a false narrative of widespread election fraud in American history. Supporters argue that historical erasure and whitewashing preserve history, while critics view it as an attempt to glorify a divisive and oppressive past.

 

Scholars and activists warn that narratives of nonwhite history are being erased at an alarming pace, pointing to examples like the painting over of the Black Lives Matter mural in Washington, D.C., and the temporary removal of Navajo Code Talkers' stories from federal websites (Kwong, 2025). Historical whitewashing sends a clear message that only white men are recognized as belonging. Recent actions include the removal of portrayals of African Americans, women, and LGBTQ individuals from public venues and the terminations of high-profile military leaders who are non-white, LGBTQ, or female. Controversial efforts to bring back public displays like monuments related to the Civil War and to rename military installations honoring Confederate leaders have sparked debate because such figures are associated with defending slavery and opposing the U.S. government. Supporters argue that historical erasure and whitewashing preserve history, while critics view it as an attempt to glorify a divisive and oppressive past. 

 

Historical erasure and whitewashing are not restricted to the United States. Stanley (2024) notes that authoritarian regimes often discourage citizens from challenging idealized versions of national history and impose severe consequences on those who resist. It is no coincidence that educational institutions, both locally and globally, are contested spaces, where efforts to challenge entrenched hierarchies may be silenced through intimidation or force. To maintain control, authoritarian movements seize control of educational institutions in their attempt to erase unflattering history, and with it, the culture of critical inquiry that fuels social and political advancement. In contrast, democracies rely on schools and universities to safeguard collective memory, particularly of progress driven by protests, social movements, and uprisings.

 

Confronting Historical Erasure and Whitewashing

Historical literacy encompasses a set of skills that enable individuals to analyze and comprehend the past critically. When histories of marginalized individuals are omitted from educational curricula, public records, or institutional narratives, it contributes to a broader culture of silence and invisibility. This erasure reinforces systems of discrimination that persist in contemporary society. Such discrimination affects individuals’ mental health, career advancement, and sense of belonging, while also undermining organizational culture, inclusivity, and productivity. Addressing historical erasure and current inequities is essential to fostering a more equitable and truthful society.

 

Both historical erasure and whitewashing have profound consequences. They shape collective memory and identity, influence public policy, and contribute to the continued marginalization of already oppressed communities. When the truth is hidden or distorted, injustices are perpetuated, critical perspectives are suppressed, and the public’s ability to engage thoughtfully with the past is compromised. As Haitian anthropologist and historian Michel-Rolph Trouillot argued in Silencing the Past (1995), history is inseparable from power; those who control historical narratives often shape how societies understand themselves and others. Addressing these practices requires recovering suppressed narratives, teaching multiple perspectives, confronting uncomfortable truths, and critically engaging with historical sources. This is not about rewriting history, but rather about telling a more complete, honest, and inclusive version of it.

 

Confronting historical erasure and whitewashing requires a deliberate effort to acknowledge and preserve marginalized histories that have long been silenced or distorted. Historical erasure often manifests in the exclusion of nonwhite, Indigenous, and women’s narratives from educational curricula, public memorials, and media portrayals. Whitewashing involves the reinterpretation or sanitization of history to favor dominant cultural perspectives, often minimizing or ignoring systemic injustices (Brown & Brown, 2010). These practices not only obscure the lived experiences and contributions of historically oppressed communities but also hinder our collective ability to understand and address contemporary social inequalities. Education plays a pivotal role in reversing these trends by promoting inclusive histories and encouraging critical inquiry that challenges dominant narratives (King et al., 2021).

 

Efforts to confront historical erasure must extend beyond the classroom to encompass broader societal commitments, including public policy, museum representation, and media accountability. For instance, community-led initiatives to rename buildings, revise school curricula, or commission public art that reflects diverse histories are essential in reshaping public memory and identity (Tuck & Yang, 2012). Additionally, resisting whitewashing means engaging with uncomfortable truths, including colonization, slavery, and racial violence, rather than erasing or downplaying them for the sake of national unity or convenience. Through active remembrance and inclusive storytelling, societies can strive toward a more equitable and truthful historical record, one that honors all voices, fosters social healing, and promotes civic engagement.

 

References

Brown, K. D., & Brown, A. L. (2010). Silenced memories: An examination of the sociocultural knowledge on race and racial violence in official school curriculum. Equity & Excellence in Education, 43(2), 139–154. https://doi.org/10.1080/10665681003719590

Cox Richardson, H. (2025, June 27). Blogpost. https://www.facebook.com/heathercoxrichardson

King, L. J., Swartz, E. E., & Campbell, A. (2021). Teaching Black history as Black liberation. Theory & Research in Social Education, 49(4), 526–553. https://doi.org/10.1080/00933104.2021.1946365

Kwong, E. (2025, March 29). Scholars say Trump administration is trying to erase America's non-white history. https://www.npr.org/2025/03/29/nx-s1-5333846/scholars-say-trump-administration-is-trying-to-erase-americas-non-white-history

Stanley, J. (2024). Erasing history: How Fascists Rewrite the Past to Control the Future. Atria/One Signal Publishing.

Tuck, E., & Yang, K. W. (2012). Decolonization is not a metaphor. Decolonization: Indigeneity, Education & Society, 1(1), 1–40. https://jps.library.utoronto.ca/index.php/des/article/view/18630

Top of Form

Bottom of Form

Trouillot, M.-R. (1995). Silencing the past: Power and the production of history. Beacon Press.

VanSledright, B. A. (2008). Narratives of nation-state, historical knowledge, and school history education. Review of Research in Education, 32(1), 109–146. https://doi.org/10.3102/0091732X07311065

Walk, T. (2025, April 6). The Trump Administration’s assaults on Black history:

Curtailing truth obscures racism’s historic legacy. Human Rights Watch. https://www.hrw.org/news/2025/04/10/trump-administrations-assaults-black-history

 

Historical Literacy and Civic Responsibility

 

By Lilian H. Hill

"Those who cannot remember the past are condemned to repeat it."
— George Santayana, The Life of Reason: The Phases of Human Progress (1905)

This quote is often paraphrased as, “Those who do not learn history are doomed to repeat it.” It emphasizes the idea that historical understanding is essential to learn from past errors to prevent their recurrence, a central argument for promoting historical literacy in civic education. Historical literacy is more than memorizing names, dates, and events. It involves understanding how knowledge about the past is constructed, how evidence is used to support interpretations, and how history influences the present and future. This form of literacy is essential for developing critical, informed, and empathetic citizens in a democratic society.

 

Core Components of Historical Literacy

Historical literacy encompasses a range of skills that enable individuals to think critically about the past. Chronological thinking involves understanding historical time, sequencing events, and recognizing patterns of change and continuity over time (National Center for History in the Schools [NCHS], 1996). Historical comprehension refers to the ability to interpret primary and secondary sources, distinguish between fact and interpretation, and identify the perspective and context of each source (Wineburg, 2001). Historical analysis and interpretation include comparing different historical narratives, analyzing cause and effect, and considering multiple perspectives and contingencies (Seixas & Morton, 2013). Historical empathy involves understanding the beliefs, values, and motivations of people in the past while avoiding presentism, the tendency to interpret past events through the lens of contemporary norms and values (Endacott & Brooks, 2013).

 

The use of historical evidence requires evaluating sources for credibility, reliability, bias, and corroboration to construct informed interpretations (Wineburg, 1991). Historical significance refers to judging which events, people, or developments are important and understanding the reasons why they matter within broader historical contexts (Seixas, 1994). Lastly, historical revisionism entails the reinterpretation of past events, often to incorporate new evidence or perspectives and develop more nuanced understandings of the past. While this process is a legitimate part of historical scholarship, the term is sometimes misused to describe intentional distortions or denial of historical facts (Evans, 1997).

 

Historical Literacy as a Civic Tool

Historical literacy empowers individuals to navigate contemporary issues by drawing on historical precedents and insights. For example, understanding the history of voting rights and immigration policy in the United States provides a critical lens through which to evaluate current debates on these topics. Scholars such as Sam Wineburg (2001) emphasize that historical literacy is crucial for countering simplistic or nationalistic narratives. He argues that to be historically literate means learning to question and critically examine the past, rather than simply accepting it at face value. This interrogative stance is foundational for civic engagement, especially in pluralistic societies where multiple, sometimes conflicting, histories coexist.

 

In the 21st century, digital technology has transformed the way historical information is accessed and disseminated. While this democratizes access, it also creates challenges, such as the proliferation of disinformation, historical revisionism, and algorithm-driven echo chambers. Historian and media literacy scholars alike emphasize the importance of lateral reading, a practice that involves verifying sources by consulting multiple credible references across various platforms (Wineburg & McGrew, 2017). Thus, historical literacy now includes digital historical literacy, the ability to critically evaluate online historical content, including images, videos, and interactive platforms, which may be edited or decontextualized to promote specific agendas.

 

Implications for Education and Society

Historical literacy is not about revering the past, but rather about understanding it with nuance, critical thinking, and empathy (Wineburg, 2001). It equips individuals to think independently, recognize how the past informs the present, and act with greater awareness in the public sphere. A historically literate public is essential for a functioning democracy. Historical literacy fosters acceptance and understanding by exposing individuals to diverse narratives and cultural histories, encouraging empathy and respect across differences (Barton & Levstik, 2004; Seixas & Morton, 2013). Historical literacy builds resistance to propaganda by teaching people how historical narratives can be manipulated to serve political agendas (Wineburg, 2001).

 

Historical literacy is deeply connected to civic literacy, as understanding the past enables individuals to engage more thoughtfully in democratic processes, recognize patterns of injustice, and make informed decisions about civic life. Social responsibility is promoted by helping individuals connect past injustices to contemporary inequities (VanSledright, 2008). Promoting historical literacy involves encouraging inquiry-based learning, facilitating debates over primary source documents, and fostering critical engagement with public history as represented in museums, monuments, and media portrayals (Levstik & Barton, 2011; Epstein, 2009). Through these practices, students and citizens develop the tools to question dominant narratives and actively participate in democratic life. In a world saturated with conflicting narratives and contested memories, historical literacy is more than an academic skill; it is a civic imperative (Seixas, 2000; Noddings, 2013).

 

References

Barton, K. C., & Levstik, L. S. (2004). Teaching history for the common good. Lawrence Erlbaum Associates.

Endacott, J. L., & Brooks, S. (2013). An updated theoretical and practical model for promoting historical empathy. Social Studies Research and Practice, 8(1), 41–58. https://doi.org/10.1108/SSRP-01-2013-B0004

Epstein, T. L. (2009). Interpreting national history: Race, identity, and pedagogy in classrooms and communities. Routledge.

Evans, R. J. (1997). In defense of history. W. W. Norton & Company.

Levstik, L. S., & Barton, K. C. (2011). Doing history: Investigating with children in elementary and middle schools (4th ed.). Routledge.

National Center for History in the Schools. (1996). National standards for history: Basic edition. University of California, Los Angeles.

Noddings, N. (2013). Education and democracy in the 21st century. Teachers College Press.

Santayana, G. (1905, 2018). The life of reason: The phases of human progress. Pantianos Classics.

Seixas, P. (1994). Students’ understanding of historical significance. Theory and Research in Social Education, 22(3), 281–304. https://doi.org/10.1080/00933104.1994.10505746

Seixas, P. (2000). Schweigen! die Kinder! Or, does postmodern history have a place in the schools? In P. N. Stearns, P. Seixas, & S. Wineburg (Eds.), Knowing, teaching, and learning history: National and international perspectives (pp. 19–37). New York University Press.

Seixas, P., & Morton, T. (2013). The big six: Historical thinking concepts. Nelson Education.

Wineburg, S. (1991). Historical problem solving: A study of the cognitive processes used in the evaluation of documentary and pictorial evidence. Journal of Educational Psychology, 83(1), 73–87. https://doi.org/10.1037/0022-0663.83.1.73

Wineburg, S. (2001). Historical thinking and other unnatural acts: Charting the future of teaching the past. Temple University Press.

Wineburg, S., & McGrew, S. (2017). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record, 119(13), 1–40.

 

Information Warfare, Virtual Politics, and Narrative Dominance

 

By Lilian H. Hill

As the Internet becomes more advanced, it is giving rise to new challenges for democracy. Social media platforms sort users into like-minded groups, forming echo chambers that reinforce existing beliefs. Pariser (2011) states that in a world shaped by personalization, we are shown news that aligns with our preferences and reinforces our existing beliefs. Because these filters operate invisibly, we may remain unaware of what information is excluded. This dynamic contributes to the growing disconnect between individuals with differing political views, making mutual understanding more difficult. It also enables extremist groups to harness these platforms for harmful purposes. While diverse opinions are inherent to politics, social media has created a fast-paced, ever-evolving space where political discord is continuously generated (De’Alba, 2024).

Information warfare is the strategic use of information to influence, disrupt, or manipulate public opinion, decision-making, or infrastructure, often in service of political, military, or economic goals. Instead of physical force, information warfare targets the cognitive and informational environments of adversaries. Pai (2024) comments that information warfare has become central to international politics in the Information Age in which society is shaped by the creation, use, and impact of information. According to Rid (2020), information warfare aims to undermine trust between individuals and institutions. It includes tactics like propaganda, disinformation, cyberattacks, and psychological operations. In today’s digital era, state and non-state actors use social media, news platforms, and digital technologies to conduct disinformation campaigns, often blurring the lines between truth and manipulation (Pomerantsev, 2019).

Virtual politics refers to the strategic use of digital technologies, including social media, artificial intelligence, and data analytics, to manipulate political perceptions, simulate democratic engagement, and manipulate public opinion. Originally coined in the post-Soviet context, the term captured how political elites created fake parties, opposition figures, and civil society groups to manufacture the illusion of pluralism and democratic process (Krastev, 2006). Contemporary virtual politics functions through multiple mechanisms. One tactic is the creation of simulated political actors and events, where governments or interest groups establish fake NGOs, social movements, or social media accounts to fragment opposition or feign civic engagement. These simulations create an illusion of public discourse while neutralizing dissent (Krastev, 2006). A contemporary example is Russia’s promotion of fake social media accounts and organizations during the 2016 U.S. presidential election. Russian operatives created false personas, Facebook pages, Twitter accounts, and even staged events that appeared to be organized by grassroots American groups (Mueller, 2019).

Another core feature is the widespread use of disinformation and memetic warfare. Ascott (2020) notes that while internet memes may appear harmless, memetic warfare involves the deliberate circulation of false or misleading content to polarize populations or erode trust in institutions (Marwick & Lewis, 2017). A popular meme, Pepe the Frog is a green anthropomorphic frog usually portrayed with a humanoid body wearing a blue T-shirt. Originally apolitical, it expressed simple emotions like sadness and joy. The symbol was appropriated by the alt-right (alternate-right), a far-right white nationalist movement. During the 2016 U.S. presidential election, some alt-right and white nationalist groups co-opted Pepe for propaganda, using edited versions to spread hateful or extremist messages. Another common meme, the NPC Wojak is an expressionless, grey-headed figure with a blank stare, a triangular nose, and a neutral mouth. NPC is an acronym for non-player characters, a term derived from video games. The NPC Wojak meme first appeared in 2018 to mock groups seen as conformist. The NPC meme gained traction before the 2018 U.S. midterm elections amid right-wing outrage over alleged social media censorship. Conservatives used it to portray liberals as unthinking “bots,” meaning individuals who lack internal monologue, unquestioningly accept authority, engage in groupthink, or adopt positions that reflect conformity and obedience.

The most insidious aspect of virtual politics lies in data-driven psychological manipulation. Social media and other platforms collect vast amounts of personal data that is used for targeted marketing and psychological persuasion. This shift from persuasion to manipulation erodes the foundation of informed democratic decision-making. Moreover, the performative nature of online political engagement often reduces participation to reactive, emotionally charged interactions, such as likes, shares, and outrage, instead of reasoned deliberation or civic dialogue (Sunstein, 2017).

 

Narrative Dominance and Virtual Politics

Narrative dominance refers to the phenomenon in which a particular storyline, interpretation, or framework becomes the prevailing lens through which events and realities are understood and perceived. It reflects the power to shape meaning, frame discourse, and control the perceived legitimacy of knowledge or truth. A contemporary example of narrative dominance is China’s global media campaign to reshape global perception of its handling of the COVID-19 pandemic, deflect blame, criticize Western failures, spread alternative origin theories, and suppress dissenting domestic narratives (Zhou & Zhang, 2021).

 

In media, politics, and culture, dominant narratives can marginalize alternative viewpoints and solidify ideological control. In the digital age, virtual politics is a key arena in which narrative dominance is exercised and contested. Virtual politics involves the creation and circulation of curated realities that prioritize perception over policy or truth and thrive on controlling emotional responses and engagement.

 

Virtual Politics and Democracy

The consequences of information warfare, virtual politics, and narrative dominance for democracy are profound. Together, they result in diminished trust in public institutions and blur distinctions between reality and fiction. As digital platforms become the dominant venue for political communication, traditional forms of accountability —such as investigative journalism, public debate, and civic literacy —are weakened. In authoritarian regimes, virtual politics serve as a tool for controlling dissent while projecting a false image of openness. Even in democratic societies, the same tools sway elections, fragment publics, and distort political will (Bennett & Livingston, 2018). The challenge for democratic societies, then, is to develop regulatory, technological, and civic strategies to counteract the manipulative aspects of virtual politics without undermining legitimate political speech.

 

Narrative dominance in virtual politics involves creating an environment in which alternative realities are delegitimized or neglected. Narrative dominance reflects a shift from a politics of substance to a politics of spectacle and emotional resonance. Understanding this dynamic is essential for analyzing contemporary media landscapes, political behavior, and the challenges of democratic resilience in the digital era. Virtual politics is not merely about politics taking place online; it represents a fundamental transformation in how political reality is constructed, experienced, and contested. Because public life is mediated by screens, algorithms, and data, understanding the mechanics of virtual politics is critical to preserving democratic integrity and fostering genuine political engagement.

 

References

Ascott, T. (2020, February 16). How memes are becoming the new frontier of information warfare. The Strategist. https://www.aspistrategist.org.au/how-memes-are-becoming-the-new-frontier-of-information-warfare/

Bennett, W. L., & Livingston, S. (2018). The disinformation order: Disruptive communication and the decline of democratic institutions. European Journal of Communication, 33(2), 122–139. https://doi.org/10.1177/0267323118760317

De’Alba, L. M. (2024, April 15). The virtual realities of politics: Entrenched narratives and political entertainment in the age of social media. Uttryck Magazine. https://www.uttryckmagazine.com/2024/04/15/the-virtual-realities-of-politics-entrenched-narratives-and-political-entertainment-in-the-age-of-social-media/

Gerbaudo, P. (2018). The digital party: Political organisation and online democracy. Pluto Press.

Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56–59. https://doi.org/10.1109/MC.2018.3191268

Krastev, I. (2006). Virtual politics: Faking democracy in the post-Soviet world. In Post-Soviet Affairs, 22(1), 63–67.

Marwick, A., & Lewis, R. (2017). Media manipulation and disinformation online. Data & Society Research Institute. https://datasociety.net/library/media-manipulation-and-disinfo-online/

Mueller, R. S. (2019). Report on the investigation into Russian interference in the 2016 presidential election. U.S. Department of Justice.

Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin.

Pomerantsev, P. (2019). This is not propaganda: Adventures in the war against reality. PublicAffairs.

Rid, T. (2020). Active measures: The secret history of disinformation and political warfare. Farrar, Straus and Giroux.

Sunstein, C. R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.

Zhou, L., & Zhang, Y. (2021). China’s global propaganda push: COVID-19 and the strategic use of narrative. Journal of Contemporary China, 30(130), 611–628.

 

 

Data Literacy and Data Justice

 

 

By Lilian H. Hill

Data literacy is a fundamental skill set that entails the ability to read, write, understand, and communicate data in context effectively. It empowers individuals and organizations to derive meaning from data, make informed decisions, and solve problems. Data literacy is an interdisciplinary competency that integrates elements of mathematics, science, and information technology. Data literacy requires understanding data sources and constructs, analytical methods, and AI techniques (Stobierski, 2021). Data literacy is not about being a data scientist; it's about having a general understanding of data concepts and how to apply them effectively. 

The rapid expansion of digital information in today’s world has triggered a significant shift in how knowledge and skills are valued, making the ability to understand, interpret, and extract meaningful insights from data a vital competency. Schenck and Duschl (2024) comment that data increasingly drive decisions across all sectors of society, and promoting data literacy has become essential to preparing individuals to participate actively and thoughtfully in the digital age. In education, this changing environment calls for a reimagined approach that goes beyond conventional literacies, positioning data literacy as a core skill necessary for future success.

Skills of Data Literacy

Building data literacy skills is an essential process in today’s data-driven world. It begins with learning the fundamentals of data, including understanding different types such as quantitative versus qualitative data, and recognizing basic statistical concepts like mean, median, standard deviation, and correlation. Familiarity with common data formats (e.g., CSV, JSON, Excel files) lays the groundwork for deeper analytical work (Mandinach & Gummer, 2016). Introductory courses from platforms like Coursera or edX, as well as open-access tutorials and videos, offer accessible entry points for building this foundational knowledge.

To apply data literacy practically, individuals should become familiar with commonly used tools. Beginners might start with spreadsheets like Microsoft Excel or Google Sheets to learn basic data manipulation and chart creation. As comfort grows, they can explore more advanced platforms such as Tableau or Power BI for data visualization or learn coding languages like Python (using libraries such as Pandas) and SQL for deeper analysis. Practicing with real-world data available from open sources like government portals or World Bank Open Data helps bridge theory and application.

A crucial next step is learning to interpret data visualizations. Charts, graphs, and dashboards are the primary means of communicating data, and understanding how to read them critically is crucial for avoiding misinterpretation. Tools such as Gapminder or data stories from Our World in Data provide engaging ways to practice understanding patterns and trends visually (Knaflic, 2015).

Equally important is the development of critical thinking skills about data itself. This means asking questions such as: Where did the data come from? Is the sample size sufficient? Is there potential for bias or missing information? Cultivating skepticism and inquiry when reviewing data sources helps prevent the spread and influence of misinformation (Bhargava et al., 2021).

Communication is another fundamental part of data literacy. It’s not enough to understand data. The ability to clearly and ethically explain insights is equally important. This involves selecting appropriate visuals, simplifying complex ideas, and telling compelling data-driven stories (Knaflic, 2015). Platforms like Flourish or Datawrapper can help users experiment with design and narrative techniques that enhance data communication.

Ultimately, data literacy must be maintained and continually updated through ongoing learning. Schenk and Duschl (2024) call for a transformative change in educational practices, recommending a move away from formal, theory-first instruction toward contextual, inquiry-based learning. This change is viewed as crucial for equipping students with the practical skills necessary to apply data literacy effectively in real-world situations. Data literacy is not only a technical skill but also a civic and ethical one, enabling people to make informed decisions and engage in democratic processes.

Data Literacy and Social Justice

One of the core connections between big data analytics and data literacy lies in the ability to manage and critically evaluate the quality and relevance of data. Big data involves massive, unstructured datasets sourced from sensors, social media, transactional records, and more. This can introduce biases, inconsistencies, and privacy risks. Data-literate individuals are better equipped to ask critical questions: Where does the data come from? Is it representative? What algorithms are being applied? Who might be harmed by this analysis? These questions are especially important in fields like healthcare, criminal justice, education, and marketing, where big data can amplify existing societal inequities if not interpreted responsibly (boyd & Crawford, 2012).

Data justice aims to ensure that data practices do not perpetuate or exacerbate structural inequities and social injustices, but instead promote human rights, dignity, and democratic participation (Dencik & Sanchez-Monedero, 2022). The increasing dependence on data-driven technologies in all aspects of social life is a driving force behind major shifts in science, government, business, and civil society. While these changes are frequently promoted for their potential to improve efficiency and decision-making, they also introduce profound societal challenges. Data justice refers to the fair and equitable treatment of individuals and communities in the collection, analysis, use, and governance of data. It emphasizes that data are not neutral. How data are gathered, interpreted, and applied often reflect existing power structures, biases, and inequalities. Data justice has emerged as a critical framework for addressing these challenges through a lens centered on social justice. For example, if a predictive policing algorithm unfairly targets neighborhoods based on biased crime data, it may lead to over-policing in communities of color. A data justice approach would question the assumptions behind the data, advocate for community oversight, and explore alternative models that prioritize community safety without reinforcing systemic bias.

Finally, data literacy supports democratic participation in a big data society. As governments and corporations increasingly rely on data to guide decisions, including pandemic response, urban planning, and surveillance, citizens need the skills to engage with data-related policies, challenge unfair uses, and advocate for transparency and accountability. Without broad-based data literacy, power becomes concentrated in the hands of a few data-literate experts and institutions, potentially reinforcing social and economic inequalities (D’Ignazio & Klein, 2020).

References

Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & D’Ignazio, C. (2021). Data murals: Using the arts to build data literacy. The Journal of Community Informatics, 17(1), 1–15. https://doi.org/10.15353/joci.v17i1.4602

boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review, 11(1). https://doi.org/10.14763/2022.1.1615

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Jones, B. (2025). Data literacy fundamentals: Understanding the power and value of data (2^nd ed.). Data Literacy Press.

Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.

Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Press.

Schenck, K. E., & Duschl, R. A. (2024). Context, language, and technology in data literacy. Routledge Open Research, 3(19).

            (https://doi.org/10.12688/routledgeopenres.18160.1)

Stobierski, T. (2021). Data literacy: An introduction for business. Harvard Business Review Online. https://online.hbs.edu/blog/post/data-literacy

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717736335

 

Big Data and Job Opportunities

 

Image Credit: Alleksana on Pexels

By Lilian H. Hill

 

Big data refers to extremely large and complex datasets generated at high speed from a wide variety of sources, including social media, sensors, transactions, and mobile devices. These datasets are so vast and varied that traditional data processing tools cannot handle them efficiently; therefore, advanced technologies and analytics are required to extract meaningful insights. Due to its size and complexity, AI is being used to make sense of the data. However, Jones (2025) points out that we cannot abdicate our responsibility for making sense of data to machines. Instead, we need to identify the mistakes AI is making and the opportunities it is missing. Relating data literacy to big data underscores the importance of developing data analysis skills in today’s world. 

 

Big data is often characterized by the 5 Vs (Saeed & Husamaldin, 2021):

1. Volume: Refers to the massive amount of data generated every second from sensors, social media, transactions, and more that organizations must store, manage, and analyze.

2. Velocity: The speed at which data are generated, processed, and analyzed. Real-time or near-real-time data processing is crucial for making informed decisions promptly.

3. Variety: Describes the different types of data, including structured, semi-structured, and unstructured, such as text, images, videos, audio, and sensor data.

4. Veracity: Focuses on data quality, accuracy, and trustworthiness. Low veracity can lead to misleading insights if the data is incomplete, inconsistent, or biased.

5. Value: Emphasizes the importance of extracting meaningful and actionable insights from data to inform decisions and generate business or societal impact.

 

Some authors (Saeed & Husamaldin, 2021) refer to 8 or even 10 Vs and include:

6. Variability: Relates to data inconsistency and the changing meaning of data over time or across contexts. For example, the same word in different datasets may have different implications.

7. Visualization: Concerns how data are represented visually to enable human understanding and insight. Effective data visualization helps communicate complex patterns and support data-driven decisions.

8. Volatility: Refers to how long data remain relevant and how long it should be stored. Some data have a short shelf life and quickly lose value, requiring timely processing.

9. Validity: Refers to how accurately and appropriately data reflect what it is intended to measure or represent for a specific purpose. While it may seem like veracity, they are distinct concepts. A dataset can have high veracity, meaning it is trustworthy, yet still lack validity if it does not align with its intended application. Simply put, a dataset cannot be assumed to be suitable or reliable for decision-making without proper validation.

 

Wesson et al. (2022) propose an additional V relating to research ethics:

10. Virtuosity: Integrates frameworks of equity and justice. This includes analytical approaches to advancing equity, including social computational big data, fairness in machine learning algorithms, and data augmentation techniques. Wesson et al. (2022) emphasize the concept of data absenteeism, referring to who is left out of data collection and the role of positionality in shaping research outcomes. They further state that a fundamental aspect of any scientific endeavor is understanding both the methods used to collect or generate data and the disparities between the study population and the broader target population.

Big Data and Job Opportunities

Big Data presents both unprecedented opportunities and significant challenges. The demand for individuals who can critically and ethically navigate an information landscape characterized by its size and complexity is growing rapidly. The acceleration of digitalization has amplified the demand for digital competencies across various employment sectors. This trend is particularly evident in scientific fields, where employers increasingly seek candidates proficient in digital skills. A comprehensive analysis of 126,360 scientific job advertisements from Science Careers, spanning 2019 to 2023, highlights this shift (Zhang et al., 2024). The study reveals a consistent upward trajectory in the requirement for digital proficiencies, with higher-paying positions more frequently requiring such skills. Expertise in data analysis, statistics, and statistical software (e.g., Python, and R) has seen a growing demand, while traditional skills like data collection have become less critical.

This trend aligns with broader labor market projections. For instance, the U.S. Bureau of Labor Statistics (2025) anticipates a 36% growth in data scientist roles from 2023 to 2033, driven by the increasing reliance on data-driven decision-making across industries. Similarly, the World Economic Forum (2025) forecasts a 30-35% rise in demand for roles such as data analysts and scientists, propelled by advancements in frontier technologies. These projections underscore the crucial importance of integrating digital skills into educational curricula to equip the future workforce for the evolving demands of the scientific and technological sectors. 

Data analytics is integral to various aspects of business operations, including informed decision-making, operational efficiency, customer understanding, competitive advantage, risk management, personalization, and innovation. By aligning curricula with these industry demands, educational institutions can prepare graduates to make effective contributions to data-driven strategies and innovations in their respective fields.

 

Big Data and Job Skills

Big data amplifies the importance of statistical reasoning and computational thinking, which are essential components of advanced data literacy. Machine learning and AI techniques used to analyze big data require users to understand how models are trained, what features are prioritized, and how predictions are generated. Without this understanding, users may misinterpret automated outputs as objective truth when, in fact, they may reflect biased or flawed assumptions embedded in the data (O’Neil, 2016).

Data visualization and storytelling are essential skills when working with large datasets. Given the overwhelming volume of information, the ability to distill meaningful patterns, trends, and insights through clear visuals becomes a necessary skill for decision-making in business, policy, and research. Tools such as Tableau, Power BI, and Python libraries (e.g., Seaborn, Matplotlib) make this possible, but their effective use requires both technical proficiency and ethical awareness.

Organizations generate increasing volumes of data daily, making the roles of data analysis and analytics pivotal in effectively managing and leveraging this information. Consequently, educational programs in data analysis and analytics must evolve to align with the industry's dynamic needs and meet professional expectations (Booker et al., 2024). In conclusion, the rise of big data transforms data literacy from a helpful skill into a critical form of digital citizenship. It enables individuals not only to work with complex information but also to scrutinize how data are collected, analyzed, and used. In a world where algorithms and data models increasingly drive decisions, widespread data literacy is essential to ensure that big data serves the public good rather than undermining it.

 

References

Booker, Q. E., Rebman, C. M., Wimmer, H., Levkoff, S. B., Powell, P. & Breese, J. L. (2024). Data analytics position description analysis: Skills review and implications for data analytics curricula. Information Systems Education Journal, 22(3), 76–87.

Jones, B. (2025). Data literacy fundamentals: Understanding the power and value of data (2^nd ed.). Data Literacy Press.

Saeed, N. & Husamaldin, L. (2021). Big data characteristics (V’s) in industry. Iraqi Journal of Industrial Research, 8, 1-9. 10.53523/ijoirVol8I1ID52.

U.S. Bureau of Labor Statistics (2025, April 18). Fastest growing occupations. https://www.bls.gov/ooh/fastest-growing.htm

World Economic Forum (2025, January 7). Future of Jobs 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/

Zhang, G., Wang, L. Shang, F. & Wang, X. (2024): What are the digital skills sought by scientific employers in potential candidates? Journal of Higher Education Policy and Management, 47(1), 20-37. https://doi.org/10.1080/1360080X.2024.2374392