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

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. Article Selection.

Stage	Search Strategy	Databases	Date Range	Initial Results	Inclusion/Exclusion Criteria	Selected for Full-Text Review	Final Inclusion
Identification Stage 1	Multifaceted search strategy using the four key term categories	The identified multi-disciplinary databases, grey literature, and institutional repositories	Unspecified	186	Inc.: PICO framework and additional eligibility criteriaEcl.: PICO framework and additional eligibility criteria	12	12
Identification Stage 2	Multifaceted search strategy using the four key term categories	The identified multi-disciplinary databases, grey literature, and institutional repositories	2010–2024	219	As above	47	16
Identification Stage 3	Multifaceted search strategy using the four key term categories	The identified specialised databases in library and information science	2010–2024	27	As above	23	9
Total				432		82	37

). 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. Data Analytics in Archival and Museums Public Programming Studies.

General Topic	Data Analytics Focus	Technique(s)	Example Studies
Digital Archives, Linked Data	Increasing Access via Semantic Web	Linked Data, Semantic Web Technologies	[40]
Web Archives	Analysis using Text and Topic Modelling	Text Mining, Topic Modelling	[41]
Archive Digitisation, Big Data	Innovating Management	Data Mining, Big Data Analytics	[42,44,45,48]
Digital Archiving	Analysis using Machine Learning and AI	Machine Learning, Artificial Intelligence	[43,50,52,53]
Archives, Public Engagement	Improving Online Access, Data-Driven Management	User Data Analysis, Outreach Metrics, Operational Data Analysis, Performance Metrics, Altmetric Analysis	[44,45,46,48,74,75]
Archival and Museum Visitors, Web Access	Understanding Online Search Interests	Web Analytics, Search Engine Analysis	[48,49,64]
Museum Collections	Machine Learning Applications and Challenges	Machine Learning, Recommender Systems	[50]
Museum Performance, Visitor Experience	Enhancing Performance and Experience	Visitor Tracking, Data Analysis	[51]
Archaeological Archives, Digital Technology	Digital Data and Data Management Analysis	Digital Data Analysis, Data Management Analysis	[54]
Remote Sensing, Free Data	Analysis of Remote Sensing Data	Remote Sensing Data Analysis	[55]
Archives, Social Media, Archival Information Services, Mobile	Promoting Archives	Social Media Analysis, Social Networking Services	[56,57,73,76]
Archives, Librarians’ Experiences, Digital Data Archives, Knowledge Infrastructures	Data Sharing and Reuse Mediation	Qualitative Data Analysis	[58,65]
Cultural Landscape Visualisation	Conveying Change	3D Modelling, Visual Data Analysis	[59]
Digital Archive Pedagogy	Analysis of Survey Data	Survey Data Analysis	[60]
Digital Archives, Filmmaker Analysis	Analysis using Digital Forensics and Data Exploration	Digital Forensics, Data Exploration	[61]
Archival Agencies, Management	Statistical Analysis	Statistical Analysis	[62]
Computational Archival Science	Analysis using Computational Archival Science and Data Integration	Computational Archival Science, Data Integration	[63]
Museum Visitor Experiences, Co-design, Museum Exhibition Co-creation	Data-Inspired Co-design, Visitor Engagement	User Data Analysis, Behavioural Data Analysis	[66,69]
Museum Visitor Profiling	Personalised Experiences	Telemetry Data Analysis, Machine Learning, User Experience Design	[67]
Museum Experience, Multimedia Technology	Enhancing Experience	Image Recognition, Deep Learning	[68]
Museum Marketing, Arts and Cultural Organisations, Cultural Heritage, Audience Engagement	Marketing Analysis	Data Envelopment Analysis, Marketing Analytics, Business Intelligence, Audience Research, Data Analysis	[70,71,72]

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. Application of Data Analytics to Public Programming in Archives and Museums.

Dimension	Description	Studies
Enhanced Access and Discoverability	Improved user access through Linked Data, advanced search, and user-friendly interfaces	[40,48,49]
Improved Archival Management	Automation and optimisation of archival processes via data mining, machine learning, and AI. Enhanced preservation, sharing, and efficiency	[42,43,45,52,53]
Enhanced User Experience	Engaging 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 Engagement	Development of effective outreach strategies through analysis of social media and public engagement metrics	[46,56,57,73]
New Research Methodologies	Introduction of novel research methods like text mining, topic modelling, and computational archival science for deeper insights into historical and cultural information	[41,63]

.

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. Data Types for Data Analytics in Archival Public Programming.

Data Type	Focus	Methods	Applications	Studies
Textual Data	Analysing web archives, extracting meaning and identifying patterns	Text mining, Topic modelling (Structural Topic Modelling)	Understanding historical narratives, identifying trends, uncovering hidden patterns	[41]
Metadata	Enhancing discoverability and interoperability of archival materials	Linked Data, Semantic Web Technologies	Creating 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 Analysis	Improving website usability, understanding user needs, optimising online services	[48,49]
Visitor Data	Tracking visitor movements, interactions, and preferences	Visitor Tracking, Telemetry Data Analysis	Personalising visitor experiences, optimising exhibition layouts, improving museum operations	[51,67]
Social Media Data	Analysing social media interactions, user engagement, and public discourse	Social Media Analysis	Understanding public perceptions, identifying key audiences, improving outreach	[56,57,73,76]
Image and Multimedia Data	Analysing visual and multimedia content within museum collections	Image Recognition, Deep Learning	Enhancing visitor engagement, providing informative displays, facilitating research	[68]
Operational Data	Analysing operational data (user visits, requests, circulation statistics)	Operational Data Analysis, Performance Metrics	Improving user services, assessing resource allocation, evaluating effectiveness	[74]

.

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. Challenges and Considerations for Data Analytics in Archival Public Programming.

Challenge	Description	Studies
Data Privacy and Ethics	Concerns 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 Bias	Issues 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 Preservation	Challenges 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]
Accessibility	Ensuring 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]

, 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.