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Review

Harnessing Data Analytics for Enhanced Public Programming in Archives and Museums: A Scoping Review

by
Mthokozisi Masumbika Ncube
* and
Patrick Ngulube
Department of Interdisciplinary Research and Postgraduate Studies, University of South Africa, Pretoria 0003, South Africa
*
Author to whom correspondence should be addressed.
Heritage 2025, 8(5), 163; https://doi.org/10.3390/heritage8050163
Submission received: 4 April 2025 / Revised: 30 April 2025 / Accepted: 2 May 2025 / Published: 5 May 2025

Abstract

A notable lacuna exists in the extant research regarding the application of data analytics (DA) to augment public programming and cultivate robust connections between archives, museums, and their constituent communities. This scoping review aimed to address this gap by mapping the available literature at the intersection of data analytics, archives, and museums. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, a two-stage selection process was employed, utilising a comprehensive search strategy across four databases and seven specialised journals. This search identified 37 publications that met the pre-defined inclusion criteria. Findings revealed a growing interest in data-driven approaches, with nearly half of the reviewed studies explicitly linking data analytics to public programming. The review identified diverse data analytics techniques employed, ranging from traditional methods to cutting-edge artificial intelligence (AI) applications, and highlighted the various data sources utilised. Furthermore, this study examined the transformative potential of data analytics across several key dimensions of public programming, including access, archival management, user experience, public engagement, and research methodologies. The review noted ethical considerations, data quality issues, preservation challenges, and accessibility concerns associated with leveraging data analytics in archives and museums.

1. Introduction

Archives and museums, as cultural institutions, serve as vital repositories of historical information, preserving and safeguarding invaluable records that illuminate the past and enrich understanding of the present. In providing access to records, these cultural institutions facilitate research, education, and community engagement, promoting a deeper understanding of historical events and cultural heritage [1]. Moreover, these institutions play a critical role in promoting transparency, accountability, and social justice by preserving records that document the experiences and struggles of marginalised communities [2]. However, despite their crucial role in society, many archives and museums face significant challenges in reaching and engaging the public effectively [3]. Lack of resources, including financial, technological, infrastructural, human, and policy constraints, combined with a rapidly changing digital environment, often hinders their ability to communicate their value proposition and make their collections accessible to a wide audience. [4]. A primary concern is the low public awareness of resources and the services they offer. Many individuals are unaware of the wealth of information resources available within their local archives and museums, resulting in the underutilisation of these valuable resources [3]. This lack of awareness can be attributed to various factors, including limited outreach efforts, inadequate communication strategies, and a perception of these cultural institutions as inaccessible or irrelevant to contemporary society [4].
In addition, these cultural institutions are often perceived as dusty repositories of documents, inaccessible to the general public, and primarily serving the needs of professional historians. This perception hinders public engagement and limits the potential of these institutions to serve as vibrant centres for community learning and cultural exchange [3]. To address these challenges, various scholars [1,3,4,5,6] have advocated for efficacious public programming activities. Public programming encompasses a range of planned activities undertaken by cultural institutions to engage the public and foster meaningful connections [6,7]. These initiatives provide crucial platforms for community building, knowledge dissemination, and fostering critical discourse [4,8]. In line with this focus on public engagement, archives and museums implement a diverse array of tailored initiatives to connect with their respective communities, each designed to meet the specific needs and objectives of diverse audiences. For instance, traditional methods, such as curated exhibitions showcasing their materials, often themed around historical events or individuals, serve as foundational approaches [9]. Furthermore, expert lectures and presentations facilitate the dissemination of in-depth knowledge and promote intellectual discourse [10]. Complementing these approaches, hands-on workshops and classes, such as those focused on genealogy or document preservation, provide opportunities for direct interaction with archival and museum materials, thereby enhancing participant engagement [11]. In addition, guided tours offer personalised explorations of cultural institutions’ collections, led by knowledgeable staff or volunteers [12]. Moreover, outreach programmes extend beyond these institution’s walls, engaging the community through school visits, public demonstrations, and collaborative partnerships with local organisations, fostering a broader reach [13,14].
In the digital age, archives and museums have embraced innovative approaches. For example, online exhibitions utilise interactive technologies and multimedia elements to showcase their materials virtually, reaching a wider audience [15]. Digital archives also provide online access to digitised materials, often with search functions, transcriptions, and contextual information [16]. For instance, leveraging the power of social media, platforms like Twitter/X, Facebook, and Instagram serve as valuable tools for sharing archival and museum content, promoting events, and engaging with the public through interactive posts and discussions [17,18]. Also, webinars and online lectures offer virtual presentations that enhance accessibility and broaden audience participation [19]. Moving beyond digital initiatives, community-based approaches foster strong connections between these institutions and their local community [13,20]. For example, oral history projects collect and preserve valuable oral testimonies, creating a living record of community experiences [21]. Moreover, family history research days offer assistance and resources to individuals tracing their ancestry through archival materials [22]. Further fostering community engagement, collaborative projects, such as community archives or museum initiatives, involve local residents in the identification, collection, and preservation of local historical materials, thereby cultivating a sense of ownership and investment [12,13,23].
Beyond these core initiatives, these cultural institutions utilise a range of additional strategies, including publications such as newsletters and brochures, to disseminate information about their institutions and collections [7]. Involving the community further, volunteer programmes empower community members to contribute to the mission by assisting with research, outreach, and event planning [13]. Public programming varies greatly among archives and museums. This depends on their resources, mission, and community needs. However, data analytics can greatly improve how these programs are implemented and evaluated [24,25].
Data analytics entails the process of examining large datasets to uncover hidden patterns, trends, and insights that can be used to inform decision-making [26]. As such, by collecting, analysing, and interpreting data on audience demographics, programme attendance, engagement levels, and feedback, these cultural institutions can gain valuable insights into the effectiveness of their outreach efforts [27]. Arguably, public programming is essential for bridging the gap between archives, museums, and the public, fostering a deeper understanding of history, and promoting the value of archival or museum collections [4,6,7,8]. As such, data analytics can play a crucial role in supporting these objectives by providing evidence-based insights that inform programme development, resource allocation, and audience engagement strategies [26,27,28]. For example, by analysing visitor data, archives and museums can identify key audience segments, tailor programmes to specific interests, and measure the impact of different outreach initiatives [25]. This data-driven approach can help these cultural institutions to optimise their public programming efforts, maximise their impact, and ensure that their valuable resources remain accessible and relevant to diverse communities [15,25,29].
As such, data analytics in archival and museums’ public programming involves using data analysis techniques to enhance outreach and engagement strategies, promoting collections and services to the community by identifying trends and patterns in user behaviour [7,30]. This understanding is crucial, given the acknowledged importance of public programming in archives and museums [4,6,7,8]. However, a significant gap remains in the collective and systematic understanding of how data analytics can be effectively leveraged to support and enhance these efforts. Although research has highlighted the potential benefits of using data analytics [31], there is a lack of systematic understanding of the current state of knowledge in this area of study. The existing literature provides fragmented insights into the application of data analytics in archival and museum public programming, with studies focusing on specific aspects such as visitor data analysis [32], or the use of digital technologies to enhance audience engagement [15,29].
A review is, therefore, needed to synthesise scholarship, identify gaps, and establish a foundation for future research on data analytics and public programming in archival and museum settings. This scoping review addresses this knowledge gap by examining the extant literature concerning the application of data analytics in these cultural institutions’ public programming. This review synthesises knowledge to help archives and museums develop effective, data-driven public programming for better community engagement and collection promotion. To this end, the following research objectives guided this review:
  • To describe the current state of knowledge and research on the use of data analytics in archival and museum public programming.
  • To examine the challenges and limitations of using data analytics in archival and museum public programming.
These objectives guided the direction of the current review. Firstly, describing the current state of knowledge and research provides a foundational understanding of existing work, identifying gaps and opportunities for further investigation. This ensures the research builds upon existing knowledge and avoids unnecessary duplication of effort. Furthermore, this objective allows exploration of the diverse range of data collected and analysed by these cultural institutions. This is crucial for comprehending the full scope and potential impact of data analytics within these institutions’ domain. Investigating the challenges and limitations of using data analytics in these cultural institutions’ public programming is crucial for addressing practical concerns. Thus, in identifying and mitigating potential risks, individuals can develop practical solutions and best practices for overcoming these challenges, eventually enabling these institutions to make informed decisions about the effective and ethical use of data analytics in their public programming initiatives.

2. Methodology

To ensure the methodological rigour and transparency of this scoping review, the conduct and reporting of this study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist and guidelines [33]. Evidence of this adherence is provided by the completed PRISMA-ScR checklist, which is included as Supplementary Material. Thus, the PRISMA-ScR framework provided a systematic and standardised methodology for this scoping review, encompassing essential stages that are delineated in this Section. To optimise the review process, the researchers proactively preregistered the protocol for this scoping review on the Open Science Framework (OSF) “https://doi.org/10.17605/OSF.IO/WUJGB (accessed on 7 April 2025)”. This act of preregistration provides a time-stamped, publicly accessible record of the planned objectives, inclusion and exclusion criteria, search strategy, data charting methods, and proposed analysis, thereby minimising the potential for post hoc biases and promoting greater confidence in the integrity of the review findings.

2.1. Literature Search, Screening, and Selection Criteria

The researchers searched databases such as ERIC, JSTOR, PsycINFO, and PubMed; indexing services like Web of Science and Scopus; and journals including Archival Science, The American Archivist, and Digital Humanities Quarterly. To maximise retrieval, the researchers developed and iteratively refined the search strategies for each targeted platform [34]. These strategies combined keywords, controlled vocabulary (e.g., Archivists’ Toolkit Controlled Vocabulary, Library of Congress Subject Headings), and specific operators to leverage platform functionalities.
A multi-faceted search strategy was employed, structured around four distinct keyword categories. The first category, “Data Analytics Applications”, explored core concepts related to data utilisation within archival and museum settings, employing the following search string: (“data analytics” OR “data mining” OR “machine learning” OR “artificial intelligence”) AND (“archives” OR “museums” OR “galleries” OR “cultural heritage institutions”) AND (“metadata” OR “finding aids” OR “collection management” OR “exhibits” OR “visitor data”). The second category, “Public Programming”, focused on outreach, community engagement, and public programming activities in both archives and museums, using the following search string: (“public programming” OR “outreach” OR “community engagement” OR “exhibitions” OR “education” OR “visitor services”) AND (“archives” OR “museums” OR “galleries” OR “cultural heritage institutions”) AND (“community partnerships” OR “audience engagement” OR “public access” OR “interpretive programmes”). The third category, “Research Methods”, encompassed a range of approaches commonly employed in archival and museum research, using the following search string: (“archival research” OR “museum research”) AND (“qualitative research” OR “quantitative research” OR “mixed methods” OR “case studies” OR “user studies” OR “ethnographic research” OR “historical research”) AND (“archives” OR “museums” OR “galleries” OR “cultural heritage institutions”). Boolean operators were also used to refine the search and eliminate irrelevant studies. Specifically, studies focusing on literature reviews, systematic reviews, or meta-analyses were omitted: NOT (AB = (“literature review” OR “systematic review” OR “meta-analysis”)). Additionally, studies primarily focused on library science, and lacking any intersection with archival or museum studies, were also excluded: NOT (AB = (“library science” AND NOT (“archives” OR “museums”))). This refined keyword strategy was designed to comprehensively capture the multifaceted nature of the research, ensuring a focused and relevant search for literature on leveraging data analytics within the context of archival and museum public programming.

2.2. Selection Process

A collaborative approach to study selection was employed, with two independent reviewers conducting a two-stage screening process to enhance inter-rater reliability and ensure consistent study inclusion:
Following a search across relevant databases and key journals, an initial pool of 432 articles was retrieved. After deduplication, a screening process was conducted using pre-defined eligibility criteria (detailed in Table 1). This process, designed to identify studies specifically focusing on the application of data analytics within the context of archival and museum public programming, resulted in the selection of 82 studies for inclusion in this scoping review.
Studies that satisfied the eligibility requirements were subjected to a thorough full-text evaluation after the initial screening. This step involved a thorough assessment of each study’s methodology, findings, and conclusions to ascertain their applicability, methodological soundness, and contribution to the goals of the review, particularly with regard to public programming in museums and archives. This study used the Population, Intervention, Comparison, Outcome (PICO) framework to determine eligibility criteria and structure the search approach in order to improve the relevance of the literature search [35]. In outlining relevant research parameters that applied to both archival and museum contexts, the PICO framework made it easier to select studies in a methodical manner. According to the PICO framework, the following eligibility requirements served as a guide for choosing the study:
  • Population (P): Research concentrating on individuals or entities interacting with museum or archival items in museum or archival environments (museums, archives, special collections, and institutions involved in cultural heritage). Studies where the archival or museum context was essential to the research were prioritised. Exclusion criteria included studies conducted in non-archival or non-museum contexts or in which the archival or museum context was incidental.
  • Intervention (I): Research looking at public programming efforts carried out in museum or archival contexts (e.g., exhibitions, educational programs, events, community outreach, internet involvement). The focus was on programs that encouraged learning, developed community collaborations, or involved users with museum or archival collections. Excluded were studies that had no significant public programming component and that only addressed internal archival or museum operations (such as collection management, preservation, or digitisation).
  • Comparison (C): Although it was not a requirement, research that included comparative components was given preference. Such studies included baseline data, studies comparing various types of initiatives, and studies comparing public programming initiatives in museums and archives with control groups or established practices. Strong outcome evidence was required for studies assessing program impact without direct comparison to be accepted.
  • Outcome (O): Research evaluating the success of public programming projects in museum and archival environments. Improved learning outcomes (e.g., historical context, research skills, and material knowledge); improved user engagement (e.g., visitation, participation, material use, and demonstrated interest); and strengthened community partnerships (e.g., collaborative projects, community involvement, improved relationships) were the main outcomes. Excluded studies lacked explicit outcome measurements or program impact assessments [35].
To optimise the search approach and guarantee a thorough evaluation, the following additional eligibility criteria were put into place:
  • Included in the study design were empirical studies using mixed-methods, quantitative, or qualitative approaches. Excluded were editorials, commentary, opinion articles, and research that were purely descriptive.
  • Publication Type: Reports, conference proceedings, dissertations, theses, and other types of grey literature were all eligible, as were peer-reviewed journal articles. Excluded were non-peer-reviewed works (except for certain grey literature).
  • Language: Included were studies that were published in English. Studies published in other languages were excluded.
  • Publication Date: Included were studies released from 2010 to 2025 (inclusive). Excluded studies were those released prior to 2010.
  • Grey Literature: The list only contained works that were available to the general audience. The excluded grey literature was inaccessible.
The PICO framework and additional eligibility criteria resulted in the selection of 37 studies for inclusion in the scoping review. Table 1 outlines the article search strategy conducted for this review.
Beyond the specifics of the article search strategy detailed in Table 1, Figure 1 provides a comprehensive flowchart illustrating the overarching literature search and study selection methodology undertaken for this scoping review.
The methodological rigour and clarity of the studies that were part of this scoping review were systematically assessed using the Critical Appraisal Skills Programme (CASP) checklist [36]. The reviewers specifically used CASP to evaluate each study’s methodology and results sections, paying particular attention to the authors’ proven comprehension and use of data analytics principles to improve public programming in museums and archives [36]. This required assessing how data analytics techniques were articulated, how they were specifically applied to public programming initiatives, the type and calibre of the data that were analysed, and the supporting documentation about how these analyses affected audience engagement, reach, or learning outcomes in these cultural heritage institutions. In using the CASP framework assertively, the review sought to offer a thorough and critically evaluated summary of the existing literature [36].
Following the two-stage selection process, the researchers convened to address any discrepancies and achieve consensus regarding study inclusion. This collaborative approach ensured a systematic and unbiased evaluation of each study’s relevance, minimising potential bias. Inter-rater reliability (IRR) was assessed using percentage agreement and kappa statistic (κ) to evaluate the consistency and reliability of the screening process [37]. The high level of agreement observed (80% agreement, κ = 0.75) provided strong evidence of inter-rater concordance, validating the robustness of the selection process [38].

2.3. Data Charting Process

In the data charting process, the study adopted a standardised approach aligned with the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist [33]. This involved a two-pronged strategy to maximise efficiency and accuracy.
Mendeley, a robust reference management software, served as the primary platform for organising and managing the retrieved studies [39]. Its functionalities facilitated efficient initial screening and selection based on pre-defined inclusion and exclusion criteria. Furthermore, Mendeley’s features, such as tagging and note-taking capabilities, streamlined the organisation process and enabled preliminary data extraction, including the capture of essential study characteristics like author information, publication year, and keywords [39].
A standardised data extraction form was developed using Microsoft Excel, guided by the PRISMA-ScR checklist [33]. A structured form was used to record key study characteristics. This form documented the study’s author(s), publication year, source, and design. Contextual information captured the type of archive or museum, its geographic location, and its size and scope. Details regarding data analytics applications included the specific techniques and data sources employed. The public programming focus section outlined the type of programming, target audience, and objectives. Also, key findings summarised the benefits and challenges of applying data analytics in archival or museum public programming, its impact on outcomes, and any ethical considerations identified. The combination of Mendeley for study management and a standardised data extraction form ensured the collection of high-quality and consistent data. This methodical approach subsequently facilitated the in-depth analysis and synthesis of the collected data.
In terms of results synthesis, a manual coding approach utilising Microsoft Excel spreadsheets was employed to extract and document critical study information, including author(s), study methodology, publication details, and key findings. The extracted data were organised and presented in Appendix A, providing a transparent record of the extracted studies [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76].

3. Results

This Section presents the findings of the study, structured in accordance with the core objectives. The analysis addresses two primary research aims: first, to delineate the current state of knowledge and research concerning the application of data analytics within archival and museum public programming; and second, to investigate the inherent challenges and limitations associated with the implementation of data analytics in this context. The findings presented offer an overview of the existing literature and practical considerations surrounding data-driven approaches to public engagement in these institutions.
To ascertain the current state of knowledge and research regarding the application of data analytics within archival and museum public programming, a chronological analysis of the included studies was conducted, focusing on publication trends over time. This temporal analysis, the results of which are presented in Figure 2, provided valuable insights into the evolution of scholarly attention to this emerging field. Examining publication trends allowed for the identification of key periods of growth, stagnation, or shifting research priorities, potentially reflecting broader trends in the archival and museum sectors, technological advancements, or evolving research agendas. Furthermore, this analysis helped to contextualise the current body of knowledge by highlighting the historical development of research on data-driven public programming in these institutions. A detailed examination of Figure 2 revealed the specific patterns and trends observed, contributing to a more nuanced understanding of the current research landscape.
The analysis of publication trends (Figure 2) reveals a growing interest in the application of data analytics within archival and museum contexts.
Beyond the chronological analysis of publication trends, this study also undertook an examination of the diverse data analytics techniques and their focus within archival and museum public programming. Table 2 shows the results of the analysis.
Table 2 reveals a diverse field of data analytics applications within archives and museums. These applications vary considerably; for instance, they range from enhancing access using semantic web technologies to analysing visitor behaviour patterns. The analytical techniques employed are equally varied. Alongside established methods like web analytics and statistical analysis, institutions are increasingly utilising advanced tools such as machine learning and deep learning. Crucially, these techniques are not applied in isolation. Instead, they serve several interconnected purposes, including improving online access, optimising archive management, facilitating a deeper understanding of visitors, and promoting effective public engagement.
Complementing the analysis of data analytics types, this study further investigated the specific applications of these diverse techniques within archival and museum public programming. The findings of this analysis are presented in Table 3.
The table illustrates the significant impact of data analytics across a spectrum of archival and museum functions, moving beyond traditional practices to embrace innovative approaches. Notably, the application of data analytics not only focuses on internal improvements like archival management but also extends to enhancing external interactions through improved access, user experiences, and public engagement. Furthermore, the emergence of new research methodologies signifies a transformative shift towards data-driven insights within the cultural heritage sector.
In addition to examining the application of data analytics within archival and museum public programming, this study also considered the diverse data sources employed in these data-driven initiatives. Understanding the origin and nature of the data used was crucial for evaluating the validity and reliability of analytical insights. The results of this analysis are shown in Table 4.
Table 4 highlights the diverse ways in which data analytics is employed, ranging from understanding textual content and user behaviour to enhancing metadata and analysing multimedia. As such, the table underscores the data-driven approaches being adopted to improve access, engagement, research, and operational efficiency within cultural heritage institutions. The second core objective of this study addressed the challenges and limitations inherent in the application of data analytics within archival and museum public programming. The findings of this analysis, presented in Table 5, provide a synopsis of the multifaceted challenges encountered by researchers and practitioners seeking to implement data analytics in public programming archival and museum initiatives.
Table 5 outlines key challenges associated with the increasing adoption of data analytics within archival and museum contexts. These challenges span ethical considerations around data privacy, issues of data quality and potential biases, the complexities of long-term digital preservation, and the critical need to ensure equitable accessibility for all users. Therefore, the table underscores the multifaceted hurdles that cultural heritage institutions must address to responsibly and effectively leverage the power of data analytics.

4. Discussion

The analysis of publication trends, as depicted in Figure 2, reveals an increasing scholarly and practical interest in the application of data analytics within archival and museum contexts. This trend is not occurring in a vacuum; rather, it is significantly propelled by technological advancements [4,77,78]. The proliferation of digitisation initiatives, the decreasing cost of data storage, and the development of increasingly sophisticated yet user-friendly analytical tools have made it feasible for archival and museum professionals and researchers to engage with large datasets in unprecedented ways [50,78]. This expanding availability of digital data [79], mirroring Lapszynski’s observations [15] regarding the opportunities it presents, fuels a concurrent desire to leverage its potential for enhanced research, analysis, and even public engagement. Furthermore, changes in funding priorities have likely played a crucial role in shaping this publication trend [44]. As funding bodies increasingly recognise the potential of digital technologies and data-driven approaches to enhance access [80], preservation, and interpretation of cultural heritage, resources have been directed towards projects that explicitly incorporate data analytics [51,81]. This shift in emphasis has incentivised researchers and institutions to explore and publish findings related to these applications [82,83].
The positive trajectory observed also signifies a growing recognition amongst researchers and practitioners of data analytics’ capacity to inform and potentially transform work in these fields [25,28,31]. This recognition is intertwined with shifts in research agendas within archival and museum studies [83]. As these fields grapple with the challenges and opportunities of the digital age [4], there is a growing impetus to move beyond traditional descriptive approaches towards more quantitative and analytical approaches [15,82]. The desire to uncover hidden patterns, understand user behaviour, and demonstrate the impact of cultural heritage through data-driven insights is driving scholarly inquiry in this area [4,81]. However, the modest rate of increase observed suggests the scope of research concerning data analytics applications within archival and museum contexts remains limited, indicating its nascent stage as a distinct area of scholarly inquiry [77,78].
The study findings presented in Table 2 furnish an overview of the diverse applications of data analytics within archival and museum contexts. Specifically, a key finding underscores the breadth of data analytics techniques currently employed [83]. Web analytics was also found to be a prominent technique (Table 2), emphasising the growing importance of online presence for archives and museums [48]. These studies employed analyses of user behaviour on websites and online platforms to enhance accessibility and engagement, thus demonstrating the critical role of user needs assessment within archival contexts [7,11]. Social media analysis was found to be another significant area of focus, indicating the importance of understanding how archives and museums interact with the public on these platforms [73,76]. The inclusion of studies using qualitative data analysis was crucial (Table 2), as it provided a deeper understanding of user experiences, perspectives, and the social context of archival and museum work [58,65]. Whilst quantitative methods provide valuable insights, qualitative approaches complement these findings and provide a more comprehensive understanding of the research questions [4,58,65]. Table 2 also showcases a diverse range of other techniques, including linked data, text mining, 3D modelling, digital forensics, and data envelopment analysis. This variety demonstrates the adaptability of data analytics to different research questions and archival/museum challenges [25]. The general picture presented by the study (Table 3) points towards a move towards data-driven decision-making within archives and museums [25]. These institutions are increasingly recognising the value of data in informing strategies, improving services, and demonstrating impact [31].
The implementation of machine learning and artificial intelligence signifies a trend towards advanced data analysis, demonstrating the emergent potential of these technologies within archival and museum domains [80]. These techniques can be used for tasks like image recognition, personalised recommendations, and automating archival processes [50,66,67]. However, while advanced technological applications like deep learning and AI are undeniably critical in offering varied benefits to archival and museum practices, it is imperative to acknowledge that they also present various limitations and challenges. These include, notably, data quality issues, algorithmic biases, ethical concerns surrounding data privacy and security, as well as the necessity for significant investment in infrastructure and expertise to ensure effective implementation, as detailed in Table 5. Conversely, the heterogeneity of these data-driven approaches, as illustrated in Table 2, signifies a field actively exploring the multifaceted potential of these tools, thereby affirming the increasing relevance and integration of data analytics within archival and museum settings [32,80].
The study’s findings, as synthesised in Table 3, demonstrated the transformative impact of data analytics not only on the internal operations of archival and museum institutions but also on their external interactions and public engagement strategies. Consequently, data analytics emerges as a pivotal force in revolutionising archival practice, offering unprecedented capabilities for understanding user behaviour, collection engagement patterns, and the broader impact of cultural heritage resources [29,74]. This analysis reveals five key dimensions through which this transformative influence is manifested (Table 3). Firstly, data analytics significantly facilitate enhanced access and discoverability of archival and museum holdings. This is achieved through mechanisms such as the strategic implementation of linked data principles and the development of refined search functionalities [40]. Linked data transcend traditional keyword-based retrieval by establishing semantic relationships between disparate data points, thereby unlocking the potential for novel and more intuitive modes of discovery and access to cultural heritage information [40,48]. Secondly, data analytics demonstrably facilitates substantial enhancements in archival management. This is realised through the strategic implementation of automation technologies and artificial intelligence (AI) systems. Specifically, AI possesses the inherent capacity to automate a range of routine archival processes, including metadata creation and enrichment, and to significantly elevate the overall quality and consistency of metadata [53]. This, in turn, directly contributes to improved operational efficiency, enhanced data integrity, and more effective long-term preservation strategies [43,52].
Thirdly, data analytics plays a crucial role in enhancing user experience within archival and museum settings. This is achieved through the provision of personalised content recommendations and the development of interactive and engaging exhibits, underscoring the increasing importance of user-centric design principles [48,49]. Empirical research consistently demonstrates the efficacy of data analytics in optimising visitor experiences, informing the design of impactful exhibits, enabling data-driven personalisation of content delivery, and fostering more meaningful and impactful engagement with cultural heritage resources [51,67]. Fourthly, data analytics significantly enhances public engagement by enabling the development and implementation of data-driven outreach strategies. In providing deeper insights into audience demographics, preferences, and engagement patterns, data analytics facilitates a more nuanced understanding of the public [46]. This, in turn, leads to the creation of more targeted, effective, and impactful engagement initiatives, ultimately broadening the reach and relevance of archival and museum collections [72,75]. Fifthly, and finally, data analytics enables the adoption of novel research methodologies within the humanities and cultural heritage fields. These innovative methodologies have the potential to reveal latent patterns, uncover hidden connections, and generate deeper insights into historical and cultural information contained within vast datasets [42,45]. Therefore, these multifaceted findings underscore the increasing necessity of cultivating data literacy amongst professionals within cultural heritage institutions and making strategic investments in robust data analytics infrastructure and specialised expertise. Only through such proactive measures can these institutions fully realise the transformative potential of data-driven approaches for the long-term preservation, enhanced access, and meaningful public engagement with our shared cultural heritage [11,13].
This study identified a diverse range of data sources that serve as crucial inputs for informing data-driven initiatives within archival and museum contexts (Table 4). Specifically, the analysis revealed that textual data, derived from sources such as web archives and collection descriptions, offer valuable insights into public perceptions, prevailing attitudes, and evolving discourse surrounding cultural heritage, thereby significantly enhancing narrative comprehension and contextual understanding of archival and museum collections [41]. Furthermore, meticulously curated metadata plays a pivotal role in augmenting the discoverability of archival materials and fostering enhanced resource access through improved interoperability across digital platforms [40,51]. User-generated data, particularly web analytics capturing user behaviour on institutional websites, empowers institutions to strategically optimise website design, refine information architecture, and enhance the overall user experience, ultimately leading to demonstrably increased user engagement and satisfaction [49].
Similarly, visitor data, encompassing the tracking of movements, interactions within physical spaces, and expressed preferences, facilitate the creation of personalised, deeply engaging, and interactive experiences within museum environments, catering to individual interests and learning styles [51,57]. The analysis of social media data furnishes invaluable, real-time insights into public opinions, prevailing attitudes towards collections and exhibitions, and broader trends in public discourse related to cultural heritage, enabling institutions to tailor outreach efforts and gauge public sentiment effectively [56,76]. Moreover, the integration and analysis of image and multimedia data significantly enrich digital content and substantially elevate the user experience by providing detailed visual information, contextual depth, and alternative modes of engagement with collections [68]. Consequently, these comprehensive findings underscore the critical imperative for archives and museums to strategically leverage this diverse spectrum of data sources. Thus, through effectively harnessing the analytical potential of textual data, metadata, user data, visitor data, social media data, and multimedia data, cultural heritage institutions can significantly enhance operational efficiencies, demonstrably improve public engagement strategies, and substantially enrich user experiences within the increasingly vital digital domain [25,32].
Despite the significant opportunities presented by data analytics for enhancing the operations and public engagement of archives and museums, this study identifies several key challenges that necessitate careful consideration for its effective and responsible implementation (Table 5). Paramount among these are ethical considerations surrounding data privacy and security [51,67]. These demand attention to the principles of informed consent, robust data security protocols, and a proactive mitigation of the potential for data misuse or discriminatory outcomes, particularly given the often-sensitive personal information held within these institutions [79]. Consequently, archives and museums bear a profound ethical and legal responsibility to rigorously safeguard the privacy and confidentiality of individuals represented, both directly and indirectly, within their collections [44]. Furthermore, issues pertaining to data quality and the presence of inherent biases represent another substantial challenge in the application of data analytics, including sophisticated techniques leveraging machine learning, deep learning, and artificial intelligence (AI) [52,66]. Data imperfections, such as inaccuracies, incompleteness, and inconsistencies, can arise from a multitude of factors, including initial data collection methodologies, sampling techniques, and the inherent biases that can be embedded within analytical algorithms, including those underpinning AI systems [41]. These biases, if left unaddressed, can have significant ramifications on the validity and fairness of analytical outcomes, underscoring the necessity for data cleaning, validation procedures, and evaluation of the outputs generated by machine learning, deep learning, and other analytical approaches [41,72].
Moreover, the long-term preservation of digital data constitutes a critical challenge for cultural heritage institutions increasingly reliant on digital assets and data-driven insights [43]. Given the inherent fragility and susceptibility of digital information to data loss, format obsolescence, and media degradation, the development and implementation of robust digital preservation strategies are imperative for ensuring the longevity and accessibility of valuable digital resources [44]. Thus, guaranteeing continued data access for future generations necessitates proactive and adaptive measures, encompassing systematic data migration, ongoing format conversion, and the establishment of resilient and well-managed digital archives capable of accommodating evolving technological landscapes [43]. Furthermore, ensuring accessibility for all users, including individuals with diverse abilities and disabilities, is an ethical and practical imperative in the deployment of data analytics [50]. Effective accessibility requires diligent adherence to established data analytics accessibility guidelines, the proactive implementation of inclusive design principles from the outset of project development, and a sustained commitment to the principles of universal design to ensure that data-driven services and resources are usable and equitable for everyone [79]. Therefore, addressing these multifaceted challenges is paramount for archives and museums to ethically and effectively harness the transformative potential of data analytics, including advanced techniques like AI and deep learning, to enhance their operations, accessibility, and engagement with the public.

5. Conclusions

This review highlights a dynamic evolution in applying data analytics within archives and museums. Diverse data sources and sophisticated techniques, including machine learning and AI, are increasingly used to enhance access, management, user experience, and public engagement, driving a shift towards data-driven decision-making. However, critical challenges remain concerning data privacy, quality, preservation, accessibility, bias, and the need for ethical frameworks and inclusivity.

6. Limitations and Future Directions

This study, while offering valuable insights into the application of data analytics within archival and museum contexts, is subject to certain limitations. The focus on published literature may have introduced publication bias. Nuances in data analytics techniques and sources could be more refined in categorisation. Also, prioritising explicit connections between data analytics and public programming may have underrepresented implicit applications. In addition, the small sample size limits generalisability. In that regard, the review primarily focused on studies that insinuate data analytics is being performed by single institutions, potentially overlooking the contributions of international or multi-institutional projects. Consequently, the analysis may not fully represent the diversity of research collaborations and may limit the generalisability of findings to contexts beyond single-institution studies. Furthermore, the exclusion of studies focused on libraries limits the scope of findings regarding broader cultural heritage institutions, reflecting the review’s specific focus on archives and museums.
Future research could address these limitations by expanding the review’s scope to encompass grey literature, project reports, and case studies, thus providing a more holistic understanding of data analytics practices. In-depth qualitative research, such as interviews with practitioners and researchers, could offer richer insights into the challenges and opportunities associated with data analytics implementation. In addition, a comparative analysis across different types of archives and museums could reveal variations in data usage and analytical approaches. Moreover, future studies could explore data analytics applications across a wider range of cultural heritage institutions, including libraries, to provide a more holistic understanding and to address the study’s focused scope. In addition, future research should investigate the prevalence and impact of collaborative, multi-institutional projects in data analytics within archives and museums’ public programming.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/heritage8050163/s1.

Author Contributions

P.N. and M.M.N. conceived the study and developed the methodology. M.M.N. handled software development, formal analysis, investigation, and data curation, also drafting the original manuscript and creating visualisations. P.N. and M.M.N. validated the results. P.N. secured resources and funding, oversaw the project, and reviewed/edited the writing. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by the National Research Foundation (SA) SRUG2205025721 and University of South Africa (Unisa).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analysed in this study.

Acknowledgments

The authors wish to acknowledge the support of two postdoctoral fellows from Unisa for their assistance in checking the data coding.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PRISMA-ScRPreferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
AIArtificial Intelligence
DAData Analytics
PICOPopulation, Intervention, Comparison, Outcome
IRRInter-Rater Reliability

Appendix A

Table A1. Data Extraction and Coding.
Table A1. Data Extraction and Coding.
Author(s)TitlePublisherFindings
[40]Archives, Linked Data, and the digital humanities: Increasing access to digitised and born-digital archives via the semantic webArchives and RecordsLinked Data can improve access to digital archives for humanities research. Current digitisation methods lack structure for digital humanities needs. Collaboration between archivists and digital humanities researchers is needed. AI and user-friendly tools can aid in creating Archival Linked Data.
[41]Web archive analytics: Blind spots and silences in distant readings of the archived webDigital Scholarship in the HumanitiesConcepts of ‘blind spots’ and ‘silences’ address limitations in web archive analysis. A new tool, warc2corpus, extracts granular data from web archives. Structural topic modelling (STM) helps analyse web archive content.
[42]An analysis of archive digitisation in the context of big dataMobile Information SystemsData mining can improve archive service capabilities and management. Proposed data mining model for archival information resources. Provides a framework for utilising data mining in archive management.
[43]DATA: Digital Archiving and Transformed AnalyticsIntelligent Information ManagementIntroduces the DATA framework for cloud-based archival management. Proposes using machine learning and AI for automation and analysis in archives. Emphasises user-centric design and information accessibility.
[44]Opening Digital Archives and Collections with Emerging Data Analytics Technology: A Research AgendaTidsskriftet ArkivBig data analytics (BDA) has the potential to improve access to digital archives. Proposed research agenda for using BDA in various aspects of archival practice. Framework outlines integrating BDA technologies with digital preservation.
[45]Analysis of the path of utilising big data to innovate archive management mode to enhance service capabilityWireless Communications and Mobile ComputingBig data offers opportunities for innovation in archive management. Data mining technology can be used to analyse and extract value from archival data. Proposes a big-data-based archive management structure.
[46]Towards a uniform strategy for taking archives to the people in South AfricaESARBICA JournalArchival institutions face challenges in outreach, access, and public programming. A uniform strategy is proposed for national and provincial archives to improve public engagement. Collaboration and consistent messaging are crucial for effective outreach.
[47]The contribution of records management and big data analytics to the growth of businesses: An analysis of Eastern and Southern Africa Regional Branch of the International Council on Archives (ESARBICA) 1 Region.Mousaion: South African Journal of Information StudiesBig data analytics is still in its infancy in the ESARBICA region. There is a need for improved records management infrastructure to support the integration of big data analytics. A framework is proposed to harmonise records management and big data analytics.
[48]Using Web analytics to improve online access to archival resources.The American ArchivistWeb analytics can be used to measure user actions and understand user behaviour on archival websites. In analysing web analytics data, archives can improve online services and enhance user experience.
[49]Using data analytics to understand visitors’ online search interests: The case of Côa Museum.Springer, SingaporeData analytics can be used to understand visitor search interests, such as keywords and nationalities, related to a specific tourism destination. This information can help tourism businesses better understand and meet the needs of their target audience.
[50]Machine learning and museum collections: A data conundrum.Springer, Cham.Recommender systems can personalise the museum experience by suggesting relevant items to visitors. Careful data curation is crucial for the effective and ethical application of machine learning in museum settings.
[51]A Holistic Approach for Enhancing Museum Performance and Visitor Experience.SensorsAn integrated approach combining ontologies, visitor tracking, personalised content delivery, and data analysis can enhance museum performance and visitor experience. 51
[52]Developing smart archives in society 5.0: Leveraging artificial intelligence for managing audiovisual archives in Africa.Information DevelopmentArtificial intelligence can play a crucial role in improving the preservation, management, and accessibility of audiovisual archives in Africa. A framework for implementing smart archives using AI in African contexts is proposed.
[53]Archives in the digital age: The use of AI and machine learning in the Swedish archival sector (Master’s thesis, Department of ALM).Uppsala UniversityAI and machine learning have the potential to transform archival practices. Swedish archival institutions are exploring the use of AI, but implementation is still in the early stages. Collaboration and knowledge exchange are crucial for successful AI adoption in the archival sector.
[54]Archives in action. The impact of digital technology on archaeological recording strategies and ensuing open research archivesDigital Applications in Archaeology and Cultural HeritageDigital-born research archives, data re-use, participation and the inclusion of academic and lay stakeholders in archaeological knowledge production are important topics that are increasingly addressed but often overlooked in the creative stages of archiving.
[55]Opening the archive: How free data has enabled the science and monitoring promise of LandsatRemote Sensing of EnvironmentThe foresighted acquisition and maintenance of a global image archive have proven to be of unmatched value, providing a window into the past and fuelling the monitoring and modelling of global land cover and ecological change.
[56]Reaching out, reaching in: A preliminary investigation into archives’ use of social media in CanadaArchivariaArchives were making minimal use of social media to attract users, user engagement was still relatively low, and the participants in the study had several concerns about contributing to social media.
[57]Taking archives to the people: The use of social media as a tool to promote public archives in South AfricaLibrary Hi TechFew public archives repositories are using Facebook, followed by Twitter/ X and LinkedIn to engage users. The public archives repositories rely mostly on social media platforms operated by their mother bodies as they are subsidiary units within arts and culture departments in government.
[58]Academic librarians’ varying experiences of archives: A phenomenographic studyThe Journal of Academic LibrarianshipThere is clear variation in meaning assigned to archives, the academic librarians’ focus of attention, and also the interpretations of the archives’ purpose, as well as the role of the technology and how the idea of the collection is constructed in each category.
[59]Cultural landscape visualisation: The use of non-photorealistic 3D rendering as an analytical tool to convey change at Statue of Liberty National MonumentJournal of Cultural HeritageThree-dimensional visualisation of heritage sites allows for a deeper understanding of historical context and also serves as a powerful tool for preservation.
[60]The pedagogy of the digital archive of literacy narratives: A surveyComputers and CompositionThe DALN offers opportunities to deepen and complicate pedagogical approaches to literacy narratives in composition, rhetoric, and literacy studies.
[61]Leveraging digital forensics and data exploration to understand the creative work of a filmmaker: A case study of Stephen Dwoskin’s digital archiveInformation Processing & ManagementDigital forensics is effective in extracting a timeline of hard drive activities, data that can be explored to reveal clues about the artist’s personal/professional history, stages of creative processes, and technical environment.
[62]Statistical analytics for managing archives at archival agenciesProceedings of the International Conference of Library, Archives, and Information Science (ICOLAIS) 2017Challenges and opportunities in utilising statistical analytics at archival agencies are (1) inaccuracy of data collection methods; (2) selecting and sorting of samples not described in detail; (3) low utilisation of comprehensive statistical analytics; and (4) un-uniformity in the use of units of measure and unit of numbers.
[63]An infrastructure and application of computational archival science to enrich and integrate big digital archival data: Using Taiwan Indigenous Peoples Open Research Data (TIPD) as an example2017 IEEE International Conference on Big Data (Big Data)TIPD utilises record linkage, geocoding, and high-performance in-memory computing technology to construct various dimensions of Taiwan Indigenous Peoples (TIPs) demographics and developments.
[64]The Archives Unleashed Project: Technology, process, and community to improve scholarly access to web archivesProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020The main contribution is a process model that decomposes scholarly inquiries into four main activities: filter, extract, aggregate, and visualise.
[65]Digital data archives as knowledge infrastructures: Mediating data sharing and reuseJournal of the Association for Information Science and TechnologyA few large contributors provide a steady flow of content, but most are academic researchers who submit datasets infrequently and often restrict access to their files. Consumers are a diverse group that overlaps minimally with contributors.
[66]Data-inspired co-design for museum and gallery visitor experiencesArtificial Intelligence for Engineering Design, Analysis and ManufacturingData can inspire design through the use of ambiguity, visualisation, and inter-personalisation; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how their use must negotiate the challenges of privacy, ownership, and transparency.
[67]Advanced Visitor Profiling for Personalised Museum Experiences Using Telemetry-Driven Smart BadgesElectronicsThe methodology integrates Bluetooth Low Energy (BLE) smart badges to collect telemetry data, enabling precise visitor localisation and dynamic group formation based on real-time proximity and shared interests.
[68]Enhancing museum experience through deep learning and multimedia technologyHeliyonThe crux of our investigation is the exploration of an adaptive convolutional neural network (CNN) to enrich the interactive engagement of museum visitors.
[69]Museum exhibition co-creation in the age of data: Emerging design strategy for enhanced visitor engagementConvergenceThis study delves into a novel design strategy for exhibition co-creation that acknowledges each interaction as a potential data point and involves connections between exhibition space, narrative, technology, interaction, and visitors.
[70]Data envelopment analysis—A key to the museums’ “Secret Chamber” of marketing?Communication TodayThe study is to use the Data Envelopment Analysis method to introduce a model for helpful assessment of the marketing communication efficiency within museums.
[71]Data-driven arts and cultural organisations: opportunity or chimera?European Planning StudiesData-driven metrics allow ACOs and policymakers to match patterns of consumption and eventually create value from harvesting and processing information.
[72]Centring Audiences: What Is the Value of Audience Mapping for Influencing Public Engagement with Cultural Heritage?The Historic Environment: Policy & PracticeThe audience mapping methodology offers a case study. Sixty datasets (including audience interviews, web analytics, observations, etc.) from 18 organisations catering to maritime heritage were compared to support the project in reaching three audiences: visually impaired people, cross-disciplinary researchers, and non-coastal communities.
[73]An analysis of Twitter and Facebook use by the archival communityArchivariaArchival organisations overwhelmingly use the services to promote content they have created themselves, whereas archivists promote information they find useful.
[74]Data-driven management and interoperable metrics for special collections and archives user servicesRBM: A Journal of Rare Books, Manuscripts, and Cultural HeritageThe importance of quantitative analysis of operational data for improving research services in special collections and archives; and the need for the profession to achieve consensus on definitions for quantitative metrics to facilitate comparisons between institutions.
[75]Altmetric and archivesJournal of Contemporary Archival StudiesAltmetric can also be used by archives to measure the impact of their diverse online holdings, including digitised and born-digital collections, digital exhibits, repository websites, and online finding aids.
[76]Archival information services based on social networking services in a mobile environment: a case study of South Korea.Library Hi-TechThe study proposes a mobile Social Networking Service (SNS) system tailored for archival information services (AIS) in the Republic of Korea, designed based on a case study of existing mobile SNS and their use in libraries and archives. The findings suggest that this system can improve AIS accessibility and foster greater user engagement and collaboration.

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Figure 1. Study Identification, Screening, Filtering, Eligibility, Selection (adapted from PRISMA [33]).
Figure 1. Study Identification, Screening, Filtering, Eligibility, Selection (adapted from PRISMA [33]).
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Figure 2. Time Series of Publications on Data Analytics in Archival Public Programming.
Figure 2. Time Series of Publications on Data Analytics in Archival Public Programming.
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Table 1. Article Selection.
Table 1. Article Selection.
StageSearch StrategyDatabasesDate RangeInitial ResultsInclusion/Exclusion CriteriaSelected for Full-Text ReviewFinal Inclusion
Identification Stage 1Multifaceted search strategy using the four key term categoriesThe identified multi-disciplinary databases, grey literature, and institutional repositoriesUnspecified186Inc.:
  • PICO framework and additional eligibility criteriaEcl.:
  • PICO framework and additional eligibility criteria
1212
Identification Stage 2Multifaceted search strategy using the four key term categoriesThe identified multi-disciplinary databases, grey literature, and institutional repositories2010–2024 219As above4716
Identification Stage 3Multifaceted search strategy using the four key term categoriesThe identified specialised databases in library and information science2010–2024 27As above239
Total 432 8237
Table 2. Data Analytics in Archival and Museums Public Programming Studies.
Table 2. Data Analytics in Archival and Museums Public Programming Studies.
General TopicData Analytics FocusTechnique(s)Example Studies
Digital Archives, Linked DataIncreasing Access via Semantic WebLinked Data, Semantic Web Technologies[40]
Web ArchivesAnalysis using Text and Topic ModellingText Mining, Topic Modelling[41]
Archive Digitisation, Big DataInnovating ManagementData Mining, Big Data Analytics[42,44,45,48]
Digital ArchivingAnalysis using Machine Learning and AIMachine Learning, Artificial Intelligence[43,50,52,53]
Archives, Public EngagementImproving Online Access, Data-Driven ManagementUser Data Analysis, Outreach Metrics, Operational Data Analysis, Performance Metrics, Altmetric Analysis[44,45,46,48,74,75]
Archival and Museum Visitors, Web AccessUnderstanding Online Search InterestsWeb Analytics, Search Engine Analysis[48,49,64]
Museum CollectionsMachine Learning Applications and ChallengesMachine Learning, Recommender Systems[50]
Museum Performance, Visitor ExperienceEnhancing Performance and ExperienceVisitor Tracking, Data Analysis[51]
Archaeological Archives, Digital TechnologyDigital Data and Data Management AnalysisDigital Data Analysis, Data Management Analysis[54]
Remote Sensing, Free DataAnalysis of Remote Sensing DataRemote Sensing Data Analysis[55]
Archives, Social Media, Archival Information Services, MobilePromoting ArchivesSocial Media Analysis, Social Networking Services[56,57,73,76]
Archives, Librarians’ Experiences, Digital Data Archives, Knowledge InfrastructuresData Sharing and Reuse MediationQualitative Data Analysis[58,65]
Cultural Landscape VisualisationConveying Change3D Modelling, Visual Data Analysis[59]
Digital Archive PedagogyAnalysis of Survey DataSurvey Data Analysis[60]
Digital Archives, Filmmaker AnalysisAnalysis using Digital Forensics and Data ExplorationDigital Forensics, Data Exploration[61]
Archival Agencies, ManagementStatistical AnalysisStatistical Analysis[62]
Computational Archival ScienceAnalysis using Computational Archival Science and Data IntegrationComputational Archival Science, Data Integration[63]
Museum Visitor Experiences, Co-design, Museum Exhibition Co-creationData-Inspired Co-design, Visitor EngagementUser Data Analysis, Behavioural Data Analysis[66,69]
Museum Visitor ProfilingPersonalised ExperiencesTelemetry Data Analysis, Machine Learning, User Experience Design[67]
Museum Experience, Multimedia TechnologyEnhancing ExperienceImage Recognition, Deep Learning[68]
Museum Marketing, Arts and Cultural Organisations, Cultural Heritage, Audience EngagementMarketing AnalysisData Envelopment Analysis, Marketing Analytics, Business Intelligence, Audience Research, Data Analysis[70,71,72]
Table 3. Application of Data Analytics to Public Programming in Archives and Museums.
Table 3. Application of Data Analytics to Public Programming in Archives and Museums.
DimensionDescriptionStudies
Enhanced Access and DiscoverabilityImproved user access through Linked Data, advanced search, and user-friendly interfaces[40,48,49]
Improved Archival ManagementAutomation and optimisation of archival processes via data mining, machine learning, and AI. Enhanced preservation, sharing, and efficiency[42,43,45,52,53]
Enhanced User ExperienceEngaging and tailored user experiences through web analytics, visitor tracking, and personalisation. Includes personalised recommendations, interactive exhibits, and improved accessibility[48,49,50,51,67]
Improved Public EngagementDevelopment of effective outreach strategies through analysis of social media and public engagement metrics[46,56,57,73]
New Research MethodologiesIntroduction of novel research methods like text mining, topic modelling, and computational archival science for deeper insights into historical and cultural information[41,63]
Table 4. Data Types for Data Analytics in Archival Public Programming.
Table 4. Data Types for Data Analytics in Archival Public Programming.
Data TypeFocusMethodsApplicationsStudies
Textual DataAnalysing web archives, extracting meaning and identifying patternsText mining, Topic modelling (Structural Topic Modelling)Understanding historical narratives, identifying trends, uncovering hidden patterns[41]
MetadataEnhancing discoverability and interoperability of archival materialsLinked Data, Semantic Web TechnologiesCreating interconnected datasets, improving search, facilitating knowledge sharing[40,51]
User Data (Web Analytics)Analysing user behaviour on websites (page visits, search queries, demographics)Web Analytics, Search Engine AnalysisImproving website usability, understanding user needs, optimising online services[48,49]
Visitor DataTracking visitor movements, interactions, and preferencesVisitor Tracking, Telemetry Data AnalysisPersonalising visitor experiences, optimising exhibition layouts, improving museum operations[51,67]
Social Media DataAnalysing social media interactions, user engagement, and public discourseSocial Media AnalysisUnderstanding public perceptions, identifying key audiences, improving outreach[56,57,73,76]
Image and Multimedia DataAnalysing visual and multimedia content within museum collectionsImage Recognition, Deep LearningEnhancing visitor engagement, providing informative displays, facilitating research[68]
Operational DataAnalysing operational data (user visits, requests, circulation statistics)Operational Data Analysis, Performance MetricsImproving user services, assessing resource allocation, evaluating effectiveness[74]
Table 5. Challenges and Considerations for Data Analytics in Archival Public Programming.
Table 5. Challenges and Considerations for Data Analytics in Archival Public Programming.
ChallengeDescriptionStudies
Data Privacy and EthicsConcerns related to the collection, use, and disclosure of personal and sensitive data. This includes issues such as informed consent, data security, and the potential for misuse or discrimination[51,67]
Data Quality and BiasIssues related to the accuracy, completeness, and reliability of data. This includes potential biases in data collection methods, sampling techniques, and the algorithms used for analysis[41]
Digital PreservationChallenges related to the long-term preservation of digital data, including issues such as data degradation, format obsolescence, and ensuring continued access to data over time[43]
AccessibilityEnsuring that data-driven services and resources are accessible to all users, including people with disabilities. This includes considerations such as accessibility for users with visual, auditory, motor, cognitive, and speech impairments[50]
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Ncube, M.M.; Ngulube, P. Harnessing Data Analytics for Enhanced Public Programming in Archives and Museums: A Scoping Review. Heritage 2025, 8, 163. https://doi.org/10.3390/heritage8050163

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Ncube MM, Ngulube P. Harnessing Data Analytics for Enhanced Public Programming in Archives and Museums: A Scoping Review. Heritage. 2025; 8(5):163. https://doi.org/10.3390/heritage8050163

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Ncube, Mthokozisi Masumbika, and Patrick Ngulube. 2025. "Harnessing Data Analytics for Enhanced Public Programming in Archives and Museums: A Scoping Review" Heritage 8, no. 5: 163. https://doi.org/10.3390/heritage8050163

APA Style

Ncube, M. M., & Ngulube, P. (2025). Harnessing Data Analytics for Enhanced Public Programming in Archives and Museums: A Scoping Review. Heritage, 8(5), 163. https://doi.org/10.3390/heritage8050163

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