Competitive Niche of Archival Data and Its Optimization Strategies

NIE Yunxia¹ ZHANG Wen¹ XU Weifeng²
(1 School of Sociology, Soochow University, Suzhou, 215123;
2 Zhangjiagang Free Trade Zone Archives, Zhangjiagang, 215634)

Abstract

[Purpose/Significance] In the era of big data, the value and importance of archival data are becoming increasingly apparent. However, the explicit or implicit competition between different types of archival data is detrimental to the full realization of the value of archival data elements. [Method/Process] Based on niche theory, this study examines the competitive "state," "trend," and "potential" of archival data by analyzing three fundamental dimensions—resources, functions, and spatio-temporal characteristics—using two core indicators: breadth and overlap. This approach enables a comprehensive assessment of the competitive ecological niche of archival data. [Results/Conclusions] Addressing current issues such as the need to expand archival data resources, high overlap in composite talent resources, weak value of production factors, uneven regional development, and barriers in the digital space, this study proposes that archival departments take the lead in systematically adjusting the ecological niche positioning of archival data across the three dimensions of resources, functions, and spatio-temporal factors. This will enhance the competitive and cooperative capabilities of archival data populations, thereby promoting the adaptation and evolution of archival data species at a higher level.

Keywords: Archival Data; Archival Data Niche; Co-opetitive Capacity; Niche Optimization

Archival data, as a critical component of the national public data resource system, embodies both archival attributes and data characteristics. Its development trajectory directly influences the positioning and goal attainment of archival undertakings. The 2025 Government Work Report positions data work as a core element for developing new-quality productive forces and promoting the digital economy, while archival work is incorporated into the domains of social governance, livelihood security, and spiritual civilization construction [1]. This highlights the dual role and inherent tension of archival data within the macro policy framework. Specifically, within the archival data ecosystem, resource competition exists among different populations, and ecological factors have yet to achieve full synergy. Externally, archival data must compete for survival space with other "data species" within the broader data resource system. Against this backdrop, the introduction of niche theory demonstrates significant relevance—the theory emphasizes responding to competition and achieving coexistence and development through continuous adjustment and optimization of interactions with the environment. Embedding this theory into the competitive analysis of archival data facilitates in-depth exploration of the dynamic adaptation mechanisms and co-evolutionary pathways of archival data in complex data environments.

[FIGURE:1] Key Subject Term Frequency Map

Niche theory and data ecology first emerged in foreign research, where they were applied to competitive analysis in fields such as education and business [2], but were rarely connected to archival studies. Scholar Brett Aho notes that China's treatment of data as a new type of production factor has made data increasingly resemble public goods, creating a unique data ecology distinct from other countries [3]. Archival data constitutes an indispensable part of China's public data resource system.

A search in the CNKI database using the criteria "Subject=Archives AND Subject=Niche" reveals that relevant research has existed since 2003 (as shown in Figure 1). Archives-related topics represent a hot spot in academic research. Scholars such as Kang Li were among the earliest to explore this field, examining fundamental principles of archival niche including niche status, dimensions, breadth, overlap, and dynamics [4], and subsequently designing indicators to measure comprehensive national archives in China [5][6]. Meanwhile, information niche has also attracted scholarly attention. For instance, Nie Yunxia et al. investigated archival resource security issues from the perspective of information actors' information niches, exploring information producers, information service institutions, and information user niches, and proposed corresponding construction measures [7]. Yu Yingxiang et al., in their discussion of archival data operation ecology, mentioned that archival departments should address the information niche breadth and competitiveness of archival data management [8].

In summary, both theoretical discussions on niche theory from abroad and explorations by domestic archival academia based on niche theory provide important references for this study. However, existing research has not taken archival data as the core research object under niche theory, and the content primarily involves interpretation of the ecological meaning and composition of archival data. The integration of archival data and niche theory requires further expansion and deepening. Therefore, this paper, grounded in niche theory, examines archival data in competitive environments from multiple levels and dimensions, aiming to clarify the internal and external situations of archival data development, explore future footholds for archival work, and facilitate the high-quality development of archival undertakings in the new era and new environment.

1.1 Archival Data and Its Niche

Archival data refers to "digitized archival information and data records with archival properties" [9]. It not only carries core archival functions such as the authenticity and integrity of historical memory, but also possesses data characteristics of shareability, analyzability, and value-added potential, making it an important component of the national data ecology that fulfills dual functions of cultural inheritance and factor circulation.

Niche refers to the position in time and space of a biological unit (species, population, or individual) within a specific ecosystem and its functional relationships with related biological units [10]. Biological units undergo long-term changes and development through competition and cooperation, and can adapt to the environment to gain certain survival advantages. Therefore, niche represents a combination of absolute dynamics and relative stability. The fundamental theories of niche include niche overlap and separation theory, niche expansion and compression theory, and niche status theory [11].

Archival data niche constitutes the sum of the dynamic position and functional relationships occupied by archival data within a specific ecosystem. Its connotation manifests at two levels: At the external environment level, archival data interacts with ecological factors such as politics, economy, and technology, forming a dynamic balance of "environmental adaptation—resource acquisition—value release." At the internal structure level, basic factors including archival data ontology (e.g., documents, audio-visual materials in different forms), archival data subjects (e.g., archival departments, enterprises, public actors), archival data technology (e.g., storage and analysis tools), and archival data institutions (e.g., policies and standards) collaboratively construct a multi-dimensional ecological space. Through value realization activities driven by archival data subjects and technology, this forms resource occupation and functional positioning. This niche exhibits hierarchical characteristics (such as three-level nesting of individual, population, and community), multi-dimensionality (encompassing spatio-temporal niche, resource niche, functional niche, etc.), and relative stability. Its extension can reach specific branches such as network archival information resource niche, archival metadata niche, and archival management data niche, reflecting the differential positioning and systematic correlation of archival data in various ecological scenarios.

1.2 Competitive Niche of Archival Data

Due to resource scarcity, biological units continuously face multiple forms of competition—internal and external, explicit and implicit. Competition represents both an interrelationship among biological units and a fundamental state for achieving ecological occupation. The competitive niche emphasizes elements such as niche breadth, niche overlap, competitive roles, and core resource capabilities during competition [12].

In the archival data domain, competition among populations manifests as differentiated competition among archives at different levels and regions in building characteristic resources. Simultaneously, competition exists between archival departments and data management departments for high-quality archival data talent. The resulting competitive niche of archival data does not passively accept environmental selection. It is also influenced by the "human" factor as archival data subjects (including organizations and individuals) who adapt to environmental changes, make active choices, and participate in competition, leading to continuous dynamic evolution alongside subject activities. Specifically, the competitive niche of archival data refers to the survival and development pattern formed through dynamic competition by proactive archival data subjects (including organizations and individuals) during the evolution of niches in archival science. Its core connotation manifests as: taking "status" (resource input and accumulation state), "trend" (environmental influence and resource utilization outcomes), and "capacity" (proactivity and ability in resource acquisition) as three-dimensional competitive elements; forming a diversified competitive pattern through two forms—interference competition (where stronger parties directly compress the niche of weaker parties) and resource utilization competition (where archival data subjects compete for limited resources); constructing complementary and cooperative "co-opetitive capacity" relationships at the population level; achieving dynamic adaptation with the external environment of "balance—imbalance—new balance" at the species level; and ultimately forming an ecosystem evolution mechanism that integrates viability, competitiveness, and developmental capacity.

It should be noted that in the ecological pattern involving multiple archival data subjects, archival departments must occupy a dominant position due to their irreplaceable role in maintaining data authority and public interest. However, emphasizing the leading role of archival departments does not exclude participation by multiple subjects. Rather, it advocates ensuring their "gatekeeper" role in the ecology through institutional design, ultimately achieving the unification of public interest, security, and innovation in archival data governance. This ecological pattern construction represents both an inevitable requirement for national governance modernization and a critical defense line for safeguarding human collective memory in the digital era. Therefore, in constructing the competitive niche of archival data, archival departments should actively participate in competition and, through means such as coordinating division of labor and optimizing resource allocation, exert counteractive influence on the data ecosystem to promote the optimization and upgrading of the competitive niche of archival data.

2 Underlying Framework of Archival Data Niche

Drawing on research approaches from the biological world regarding the measurability of biological unit niches, this study aims to construct an underlying analytical framework for archival data niches at the methodological level. To occupy an ideal niche, every archival data population must assess its spatio-temporal conditions, acquire certain resources, and develop distinctive functions [13]. Evidently, the three dimensions of spatio-temporal, resource, and function constitute the fundamental entry points for interpreting the essence and components of archival data niches. Given that niche separation and expansion form the basic driving forces for subject evolution and intensify competitive situations, this paper selects niche overlap (reflecting competition intensity) and breadth (reflecting the scope of resource utilization) as core measurement indicators for archival data to quantitatively characterize its competitive and adaptive capacities. Ultimately, this forms the underlying framework of archival data niche as shown in Figure 2 [FIGURE:2]. Examining basic dimensions through core indicators facilitates understanding the manifestations of the three-dimensional competitive elements of "status," "trend," and "capacity," thereby revealing the survival conditions of archival data populations and even species, providing a foundation for in-depth understanding of the competitive niche of archival data.

[FIGURE:2] Underlying Framework of Archival Data Niche

2.1.1 Resource Dimension of Archival Data Niche

The resource dimension constitutes the foundation for other dimensions of archival data niche. For archival data, resources are the prerequisite for survival and development. The abundance and breadth of resources occupied, along with adaptation to all resources, measure the comprehensive capability of archival data relative to competitors. Various resources that can be utilized in archival data value realization activities include archival data ontology, archival data subjects, and archival data technology equipment. Among these, archival data ontology forms the basis for all related activities and serves as the nutrient source for archival data to fulfill evidentiary value, historical-cultural value, and production factor value. The possession of archival data ontology determines the scope of activities that archival departments can undertake, while the quality of archival data directly affects the effectiveness of value realization activities. Archival data subjects, as one of the key resources in archival data niche, refer to various talents from enterprises, public institutions, or the general public who participate in archival data value realization activities. Archival data technology equipment comprises the technical means and supporting facilities that archival data subjects can employ, primarily involving databases, artificial intelligence, blockchain, and other technologies. The capacity level and proactivity of archival departments in cognition, activities, and decision-making influence the action orientations of multiple subjects such as enterprises and the public, which in turn determines the mastery of other resources like data acquisition and technology utilization. Meanwhile, the social, economic, political, institutional, and cultural environments closely related to archival data value realization activities, as well as the social demands and evaluations of archival departments, represent important factors affecting the resource niche of archival data and relate to the potential resources of archival data.

2.1.2 Functional Dimension of Archival Data Niche

The functional dimension represents the core dimension of archival data niche, referring to the multiple roles and service functions that archival data undertakes within the data ecosystem, as well as its synergistic empowerment value for both archival undertakings and the data ecosystem. This dimension manifests both as diversified functional forms developed through resource exploitation, such as cultural inheritance, policy decision support, public services, and data factor circulation, and as support effectiveness for macro objectives like data sovereignty maintenance, data governance optimization, and digital memory construction through functional output. The expansion of its functional boundaries is influenced by dual factors—the capacity of actors (i.e., the driving force of "capacity") and social demand orientation (i.e., the traction of environmental tendencies)—presenting dynamic adaptation characteristics.

From the species level analysis, the functional niche of archival data is rooted in the core responsibility positioning of archival institutions. The Archives Law of the People's Republic of China (2020 Revised Edition, hereinafter referred to as the "New Archives Law") provides a comprehensive and systematic strategic blueprint for the development and future direction of China's archival undertakings, explicitly stating the need to "uphold the leadership of the Communist Party of China over archival work," adhere to the political positioning of archival work, and establish and strengthen the political attribute that "archival work bears the Party's name" and the sacred duty of "managing archives for the Party, preserving history for the nation, and serving the people" [14]. This directly maps to archival data's evidentiary guarantee function in the government data ecology, memory inheritance function in the cultural data ecology, and knowledge service function in the public data ecology. From the population level observation, the differentiation of archival data functional niche is shaped by differences in data attributes and collaborative subject roles: First, different categories of archival data (e.g., government archives, livelihood archives, cultural heritage archives) focus on细分 functions such as administrative supervision support, livelihood rights protection, and cultural IP development due to variations in content characteristics and value density. Second, multiple actors (e.g., archives, data management institutions, enterprises, the public) undertake complementary functions such as data aggregation and integration, intelligent analysis and application, and scenario-based service provision based on differences in resource endowments. This functional stratification and collaboration mechanism not only avoids resource internal consumption caused by excessive niche overlap but also forms systematic service capacity through functional network weaving.

2.1.3 Spatio-Temporal Dimension of Archival Data Niche

The spatio-temporal niche of archival data comprises a combination of temporal and spatial dimensions, with both resource and functional dimension construction requiring consideration of the spatio-temporal dimension. The temporal dimension of archival data niche refers to the time periods occupied by multiple subjects in archival data value realization activities. From the internal environment of archival data, the richer the resources such as relevant technology, talent, and funding, the more frequent the activities of subjects across various time periods. From the external environment, the clearer the data governance responsibilities of archival departments and the greater the recognition of archival data factor value, the more active the performance in the temporal dimension. The spatial dimension of archival data niche refers to the space involved in archival data value realization activities. In the big data environment, archival data becomes increasingly active in digital space: On one hand, the archival work environment extends from physical space to digital space, with digital archives (rooms) continuously emerging, placing the formation, management, and utilization of archival data in virtual environments. On the other hand, as archival information resources transform from digital state to data state, the digital space occupied by archival data storage continues to grow rapidly.

2.2 Core Indicators of Archival Data Niche

The big data era has seen increasingly prominent demands for data aggregation effects. Therefore, the optimization of archival data niche cannot rely solely on enhancing the competitiveness of a single type of subject (or "species"). More critically, it is essential to promote the formation of co-opetitive relationships characterized by mutual constraint, synergy, and integration among different archival data populations, thereby enhancing overall co-opetitive capacity.

This also necessitates that indicators for assessing archival data niche breadth and overlap more comprehensively account for the complexity of such co-opetitive interactions.

2.2.1 Breadth of Archival Data Niche

In the biological world, niche breadth manifests as the adaptation range of biological units to ecological factors or the degree of diversification in environmental resource utilization. Accordingly, the breadth of archival data niche refers to the scope and quantity of adaptation, occupation, and utilization of ecological factors by archival data across the dimensions of resources, functions, and spatio-temporal characteristics [15]. The breadth of archival data niche must first be appropriate, as excessive niche expansion may cause problems such as spatio-temporal compression, resource grabbing, and functional overlap, thereby reducing the distinctive advantages of archival data and increasing population competition. The measurement of archival data niche breadth also implies a requirement for balance, meaning that different archival data niches should have balanced breadth, with internal archival data tending toward coordination. Taking the resource niche of archival data as an example, its breadth balance manifests as: First, archival data ontology should have diverse sources, rich types, and sufficient quantity. Second, archival data subjects should encompass multi-domain professionals in data, computer science, law, and other fields to meet the survival and development needs of archival data populations. Third, various technologies such as data collection, data processing, and data mining should be complete with supporting equipment, providing strong support for archival data value realization. Finally, the legal and regulatory framework for the entire lifecycle of archival data should be comprehensive, with broad social support.

2.2.2 Overlap of Archival Data Niche

Niche overlap measures the similarity between two different biological units in their connections with ecological factors [16]. Niches of different biological units may overlap in one or multiple dimensions. The more dimensions that overlap and the closer the breadth on overlapping dimensions, the higher the niche overlap between the two, and vice versa. Niche overlap can be broadly categorized into overlap and separation, with overlap further comprising three distinct situations: coincidence, inclusion, and intersection. As shown in Figure 3 [FIGURE:3], as the niches of different archival data populations A and B shift from overlap to separation, their dimensional performance trends diverge, reducing similarity in survival and development dependence and consequently weakening inter-population competition.

[FIGURE:3] Overlap States of Archival Data Niche

Niche overlap may lead to functional duplication, resource grabbing, and weakened co-opetitive capacity among populations. Therefore, biological units generally seek niche separation. However, in the big data era, where data aggregation and sharing are required, blindly pursuing low niche overlap for archival data is not entirely appropriate. Archival data possesses long-term preservation value and exhibits symbiosis and cumulativeness [17]. Aggregating archival data to create scale and aggregation effects represents an inevitable requirement for excavating and enhancing archival data value. Overlap caused by shared data resources and open space construction among archival departments, data departments, and the public is unavoidable. Under these circumstances, the focus on archival data niche overlap should center on how archival departments coordinate division of labor and cooperation among archival data subjects and allocate and supplement overlapping resources.

3 Current Issues in the Competitive Niche of Archival Data

On one hand, competition among archival data populations may achieve healthy development, promoting adaptation and evolution of archival data. On the other hand, it may also lead to adverse consequences, such as reduction in archival data population numbers. To steer competition in archival data niches toward healthy development, this study, based on the underlying framework of archival data niche, reveals the current status of co-opetitive capacity among archival data populations and summarizes existing problems in internal and external symbiotic development, providing references for timely adjustment of archival data ecological structures and future archival work. The challenges differ across dimensions: the resource and spatio-temporal dimensions face both breadth and overlap challenges, while the functional dimension's subdivision and collaboration mechanism helps enhance the diverse value and social benefits of archival data in the big data era, avoiding overlapping competition, but primarily suffers from bottlenecks of unbalanced functional breadth.

3.1 Resource Dimension: Ontology Breadth Requires Expansion, High Overlap in Talent Resources

Currently, the actual breadth of archival data ontology resource niche is far smaller than its fundamental breadth. Regarding the form of archival data ontology, traditional archives based on existing stock remain constrained by their original forms after digitization, exhibiting a structural resource problem of being more within the system and less outside the system. Incrementally collected archival data in real-time has more extensive sources and forms, capable of more comprehensively reflecting national landscapes and preserving social memory, with long-term value and competitive potential. However, new archival data also brings difficulties in screening and collection for archival departments: On one hand, real-time archival data changes rapidly, and failure to preserve and accumulate it in time leads to its disappearance. On the other hand, the diversity of sources and forms means that there is currently no unified standard for the scope of archiving and preservation of new types of archival data, with variations among units.

From the perspective of archival data quality, high-quality archival data is the prerequisite for actually increasing the breadth of ontology resource niche. At present, problems such as unclear rights and responsibilities and inconsistent business standards among archival departments and between archival departments and other departments lead to uneven archival data quality, hindering the integration of resources held by different archival data subjects and constraining the increase of ontology resources and the formation of co-opetitive capacity. For example, during the deepening of the "Run at Most Once" reform, the Rui'an City Archives faced incomplete collection due to unclear authority between archival and business departments over business records management, making it difficult to increase the actual breadth of archival resources [18].

Different archival data exhibit similarity in resource types, sources, and usage, with resulting competition currently concentrated in the contest for talent resources. Archival data possesses both archival properties and data forms, and fundamentally, different archival data populations all require composite talents with both archival professional knowledge and data literacy. However, archival talent is currently in overall shortage. According to 2023 national statistics on basic conditions of archival administrative departments and archives, among existing full-time personnel in archival administrative departments at all levels and various archives, the proportion of personnel with corresponding academic degrees in archival specialties is only 17.57% and 18.38%, respectively [19]. During the "13th Five-Year Plan" period, among 7,182 archival administrative departments and archives nationwide, only 1.35 archival specialty graduates were supplied over five years [20]. Given such professional levels and supply in archival institutions, other archival data subjects such as enterprises inevitably face development bottlenecks of lagging talent teams. Meanwhile, archival education struggles to meet data capability cultivation needs. In 2021, China approved 57 research projects on archival specialty education teaching reform, with one focus being the innovation of curriculum systems and talent training models in the digital intelligence era. However, only 3.13% of archival specialty teaching staff have computer science backgrounds, and teaching methods lack practical training integration, resulting in difficulty deepening existing technology teaching modules and challenges in course offering and teaching [21]. Limited professional supply, inadequate data education, and highly overlapping resource demands create fierce competition among archival data subjects at the talent level.

3.2 Functional Dimension: Unbalanced Functional Breadth, Weak Production Factor Value

Archival data not only possesses functions supporting policy decision-making, public services, and cultural inheritance, but also carries the important value of data as a production factor driving digital economic development. Its positioning in the functional dimension determines its competitiveness relative to other data species within the national resource system. Currently, the competitive niche of archival data faces constraining factors of unbalanced breadth in the functional dimension, primarily manifested in the relative weakness of archival data's production factor value.

As an important component of China's public data resources, archival data plays prominent functions in policy decision-making and public services. For example, in highway government services, the rapid accessibility of archival data optimizes administrative approval processes and enhances highway project construction and management efficiency [22]. In cultural inheritance, archival data itself contains rich creative materials. The classic character "Nezha" in Nezha: Birth of the Demon Boy, for instance, not only originates from archives recording early story forms but also benefits from continuous archiving and organization throughout its dissemination and evolution [23]. In contrast, in terms of production factor value, archival data remains an emerging entity: First, archival departments have yet to fully recognize its enormous potential as a production factor, with archival data rarely participating in data property rights registration practices promoted across various regions. Second, large amounts of archival data containing personal privacy and sensitive information still circulate in gray markets, lacking unified, standardized, and controllable transaction and protection mechanisms. As the big data era places increasing emphasis on data factor value, the weakness of archival data in this functional dimension will reduce its competitive positioning, weaken the data governance role of archival departments, and ultimately marginalize archival work within the national data governance system.

3.3 Spatio-Temporal Dimension: Uneven Regional Development, Barriers in Digital Space

In physical space, the breadth of competitive niche for archival data varies across regions, with this competitive strength distribution largely mirroring economic level distribution. For example, regarding regional differences in enterprise archival data, the 2024 China Archival Data Innovation Index (ADIA) Development Report shows that the Yangtze River Delta and East China regions lead in enterprise archival data innovation index scores, demonstrating high-quality development trends and strong competitiveness [24]. In contrast, Northwest China shows weaker archival data innovation, with clearly insufficient competitiveness in technological and management innovation. Regional differences in competitive niche stem from uneven regional development, fundamentally rooted in uneven regional resources. Regions with weaker competitiveness may have shortcomings in archival data work, such as talent cultivation, R&D investment, and technology transformation. From the population perspective, differences in competitive strength of archival data niches are inevitable. However, at the species level of archival data, unbalanced breadth across regions may weaken its overall development potential and affect value realization of archival data in broader fields.

In digital space, China's competitive niche of archival data demonstrates constraining factors of low overlap in management service space: At the species level, archival management and data management currently constitute two relatively independent and isolated systems [25], with archival departments not yet proactively engaging in in-depth cooperation with data departments on resource sharing and business synergy. At the population level, large amounts of archival data are scattered across various business systems, fragmented and not yet comprehensively aggregated. Taking archives as an example, China's existing archive network system is criss-crossed, including comprehensive archives at all levels built according to administrative sequences, archives (rooms) established within enterprises and public institutions, and archives (rooms) constructed within various professional systems. Sectoral segmentation has become the basic form of different archival management [26], with unified top-level design lacking for archival data sharing among different archival departments. Although this distribution pattern reduces direct spatial competition to some extent, it also leads to the formation of "data barriers," which not only restricts unified management and overall planning of archival data but also fails to meet the demands of the big data era for data aggregation, sharing, and deep utilization, hindering the full release of archival data factor value. The primary reasons lie in archival data subjects' concerns about losing characteristic resource advantages, security and accountability pressures, and the insufficient technical capabilities of archival departments in system interfaces, identity authentication, and other technical aspects.

4 Optimization Strategies for the Competitive Niche of Archival Data

To address challenges facing the competitive niche of archival data, optimization strategies led by archival departments must be implemented. Archival departments should coordinate multiple archival data subjects including data departments, enterprises, and the public, optimize the allocation of limited resources such as archival data, talent, and technology, and systematically adjust their niche positioning across the three dimensions of resources, functions, and spatio-temporal factors. The aim is to achieve: enrichment and orderly governance in the resource dimension, potential stimulation in the functional dimension, and regional coordination and integrated empowerment in the spatio-temporal dimension, ultimately systematically enhancing the niche co-opetitive effectiveness of archival data.

4.1 Resource Dimension Optimization Strategies for Competitive Niche of Archival Data

Resources are essential for population survival, competition, and development. Adjustments to the functional and spatio-temporal dimensions of the competitive niche of archival data must be based on resource dimension optimization. The optimization strategies based on the resource dimension specifically manifest as: First, gradually expanding resource breadth by increasing the sources and forms of archival data ontology and ensuring its high quality and reliability through orderly governance. Second, strengthening talent supply-side reform through structural overlap reduction to alleviate the talent competition dilemma among multiple subjects such as government departments and enterprises, ensuring optimized supply of archival data talent.

4.1.1 Deeply Practice the "Grand Archives" Concept to Expand Archival Data Resource Collection

In the big data era, the concept of "data is king" has been widely recognized. In a governance context where data sovereignty competition is intensifying, archival departments must not only respond to the institutional development of new data governance agencies such as big data bureaus but also strengthen the strategic layout of indigenous data sovereignty. This requires reconstructing the resource niche of archival data, enhancing the dominant position of archival departments by expanding high-quality endowment data resources, thereby gaining more institutional discourse power.

In January 2025, the National Archives Directors and Curators Conference emphasized the need to improve archival resource collection and research the inclusion of documents and materials formed by new businesses, new fields, and new platforms [27]. The essence of "Grand Archives" is to expand archives and archival departments [28]. Under the "Grand Archives" concept, the enthusiasm of archival data subjects increases, and the scope of archival resource collection expands. Therefore, deeply practicing the "Grand Archives" concept aligns with the goal of expanding archival data resources. Archival departments must recognize the broad origins of archival data, not limiting archival data sources to the digitization of existing archives. They should emphasize collecting data records with archival properties and actively optimize archiving scopes. Archival departments need to pay attention to bottom-level, fragmented, and complex archival data, such as public network data and public livelihood data. Such archival data truly covers the general populace, providing more possibilities for broadening archival data ontology resources and exploring social spaces for archival undertakings, and helping cultivate the core competitiveness of archival data species.

Regarding collection methods, archival departments can utilize platforms such as WeChat official accounts, social media, and thematic websites to solicit archival data. For example, State Grid developed a WeChat mini-program based on its "State Grid Archives" official account for collecting COVID-19 thematic archives, allowing frontline workers to upload photos and videos from work sites, thereby promoting the collection of public health emergency thematic archival resources [29]. Regarding collection technology, archival departments can employ artificial intelligence to capture and process data from different channels and integrate resources through big data technologies such as integration, exchange, and warehousing. For instance, Baidu uses intelligent agents to crawl information from web pages, analyze it, and form structured data [30].

4.1.2 Establish and Improve Regulations and Standards to Ensure Orderly Archival Data Governance

Regulations and standards serve as resources in the archival data ecology that combine guidance and guarantee functions. To promote archival data quality improvement and ensure genuine broadening of archival data ontology resources, the national level must first timely introduce targeted policies that clarify archival data governance objectives and development plans, guiding legislative departments and archival departments at all levels to prioritize the formulation of archival data regulations and standards. Government departments need to continuously adjust policy texts based on practical situations in the actual work of establishing and improving archival data regulations and standards to promote ecological balance of archival data [31]. Experiences formed by various regions and departments in archival data governance practice should be both publicized as exemplary cases and, according to relevant regulations, elevated to institutional norms.

As subjects obligated to govern data, archival departments should actively participate in archival data legislation and standard formulation. Specifically, at the regulatory level, they can propose that the National Archives Administration formulate departmental rules such as the Measures for Archival Data Governance, clarifying the responsibilities, rights, and obligations of archival departments, data departments, enterprises, and the public in archival data governance. At the standard and norm level, archival departments should seize opportunities in archival industry standard formulation and revision plans, such as supplementing the Basic Terminology of Archival Work (DA/T 1-2000) by adding general concepts like "archival data" and "archival metadata." Archival departments can also take the lead in developing standards and norms based on full lifecycle management of archival data, regulating different stages including archival data collection, organization and storage, and development and utilization. Additionally, they can design evaluation standards examining the form, content, and utility of archival data by referencing the Information Technology Data Quality Evaluation Indicators to respond to policies and regulations, ensuring the integrity, accuracy, availability, and security of archival data.

4.1.3 Rationally Complete the Education Chain to Optimize Archival Data Talent Supply

"Capacity" represents the most active component of archival data niche, with its impact being the most rapid and evident. Sufficient and rational talent supply can quickly provide momentum for optimizing the competitive niche of archival data. The Education Powerhouse Construction Plan Outline (2024-2035) requires comprehensively building a vocational education system integrating industry and education and a self-reliant and excellent higher education system [32]. Addressing the overlapping competition for archival data talent resources can be achieved through supply-side reforms in both vocational education and higher education.

In vocational education, archival departments can collaborate with data departments and enterprises to hold professional literacy lectures, promoting deeper understanding of industry development trends and professional responsibilities among personnel engaged in archival and data work, guiding them to deepen their comprehension of archival data and develop lifelong learning awareness and habits. Meanwhile, continuing education thematic training and technical upgrading should be conducted according to the composition of archival data-related personnel and actual work needs to adapt to constantly changing work requirements. On-the-job archival staff can fully utilize online education resources, particularly massive open online courses (MOOCs), such as national high-quality courses like Archival Management, Electronic Records Management, and Database Technology and Application.

In higher education, archival data education must gather multidisciplinary teaching faculty: First, "upholding fundamentals"—archival specialty teachers should continuously upgrade core curriculum systems, maintain the essence of archival science, and incorporate practical modules into courses such as electronic records management. Second, "innovation"—leveraging faculty from computer science, data science, and other fields to increase courses in data management and archival data resource development, such as database principles and applications, data mining, and data visualization. Wuhan University's archival science actively seeks transformation, emphasizing data literacy education. Taking undergraduate archival education as an example, the university has added innovative courses such as "Big Data and Culture," "Computational Archival Science," and "Data Analysis and Visualization" on top of foundational and core archival courses, and has built an "Intelligent Archives Full Lifecycle Management Virtual Simulation Training Platform" to ensure students can integrate professional knowledge with core activities of archival data formation, management, and utilization across various industries through practical scenarios [33]. Additionally, university archival education can further subdivide into directions such as intelligent archives, data governance, and digital humanities, corresponding to talent demands in different fields like enterprises, government departments, and cultural industries, achieving structural optimization of talent supply.

4.2 Functional Dimension Optimization Strategies for Competitive Niche of Archival Data

The functional stimulation of archival data as a production factor relates to the development of new-quality productive forces in the archival field and is closely connected to the role positioning of archival departments in shouldering data governance responsibilities. To enhance the breadth of production factor functions in the competitive niche of archival data, the key lies in the dual-wheel drive of institutional guarantee and scenario application.

First, archival departments should participate in the work of legislative bodies and data governance institutions to accelerate the exploration of incorporating archival data into institutional frameworks for data rights confirmation, transaction, and supervision, providing a legal foundation and market environment for production factor value realization. Initially, archival departments should strengthen their role positioning as professional data management departments, actively participate in relevant institutional design, and promote the inclusion of archival data into policies and regulations concerning data factor market construction and trusted data space development, clarifying archival data ownership, usage rights, and supervision responsibilities. Subsequently, they should accelerate the formulation of rights confirmation standards and operational procedures applicable to archival data, exploring actionable pathways for on-chain registration and compliant usage.

Second, archival institutions should actively explore stages such as archival data collection, circulation, production, and services, forming demonstration scenario construction through typical case promotion to attract more units to participate in archival data factor development. Taking local archival department exploration practices as examples: During the resource construction stage, the Changyuan City Archives in Henan Province focused on characteristic industries such as lifting equipment, medical devices, and culinary arts, collaborating with industrial chain-leading departments, responsible units, and industry associations to solicit and build characteristic archival databases, categorically integrating modules such as technological iteration and enterprise growth to lay foundations for subsequent utilization [34]. During the assetization and capitalization stage, the Xiaogan City Archives in Hubei Province, as a pilot unit for municipal public data resource assetization reform, used 60,000 livelihood archives as an entry point to conduct data inventory, rights and value confirmation, and accounting entry, promoting data resource development and utilization and creating a demonstration scenario with leading and promotional effects [35].

4.3 Spatio-Temporal Dimension Optimization Strategies for Competitive Niche of Archival Data

Spatio-temporal dimension optimization strategies for the competitive niche of archival data require overall breadth balance and coordinated overlap alignment [36]. On one hand, exchanges and cooperation should achieve the optimization goal of regional coordinated development. On the other hand, under data aggregation requirements and relying on coordination mechanisms led by archival departments, competitive capacity of archival data should be enhanced through data space integration.

4.3.1 Actively Conduct Exchanges and Cooperation to Promote Regional Coordinated Development

The strength of archival data competitiveness depends not only on resource endowments and technological levels but also closely relates to the data circulation environment and cooperation mechanisms in its physical region. The strong competitive niche of archival data in the Yangtze River Delta region is inseparable from high-quality and close cooperation. By sharing archival data resources and utilization experiences, the Yangtze River Delta region provides a favorable ecological environment for archival data. Evidently, exchanges and cooperation among archival data subjects constitute an important strategy for optimizing niche structure and narrowing development gaps between regions.

Regional archival departments, enterprises, and data departments should actively seek exchanges and cooperation, which can be stipulated through mutual assistance agreements and collaborative memoranda. For example, archival departments in Beijing, Tianjin, and Hebei jointly signed a cooperation agreement for cross-regional sharing and utilization of livelihood archives, advancing platform construction, data access, system testing, and formal operation in phases, gradually realizing a new service model of "cross-archive inquiry and remote certification" for livelihood archives, significantly enhancing the synergy and effectiveness of archival data in livelihood services across the three regions [37]. Another example is the Baoying County Archives in Yangzhou City, which leveraged its collection resources and collaborated with the county data bureau's practical experience in data services and utilization to jointly sign the Agreement on Convenient Archival Inquiry Services, building a convenient archival query service platform to promote the efficiency of local archival data public services [38]. Archival departments should summarize and promote these excellent experiences in regional coordinated development, providing reference models for other regions and further encouraging the formation of archival data co-opetitive capacity.

4.3.2 Build Shared Service Platforms to Integrate Archival Data Governance Space

Article 41 of the New Archives Law indicates that promoting the construction of archival information resource sharing service platforms and facilitating cross-regional and cross-departmental sharing and utilization of archival digital resources constitute basic requirements of the Party and state for archival undertaking development and inevitable requirements for advancing the national big data development strategy [39]. Addressing the current problem of dispersed archival data management service space requires unified technical infrastructure support. By building archival data sharing service platforms, management efficiency and resource utilization rates of archival data across regions, departments, and industries can be improved, thereby enhancing archival data co-opetitive capacity.

In a technology-driven context, archival departments should leverage technology empowerment to facilitate optimization of the competitive niche of archival data. For instance, by learning and utilizing cloud computing, blockchain, artificial intelligence, and other technologies, they can further construct and optimize archival data sharing platforms, achieving integrated construction of four functions: archival data sharing and interaction, archival data processing, archival data cloud storage, and archival data operation and maintenance [40]. Furthermore, to break "data barriers" with other enterprises and public institutions, archival departments should proactively access regional and national public data sharing service platforms. For example, to achieve connectivity with provincial public data resource registration platforms and promote interconnection of registration information, the National Public Data Resource Registration Platform (national platform for public data resource registration) was launched on March 1, 2025, aiming to implement "one certificate, one code" for electronic vouchers nationwide and support registration information query and sharing. The platform not only stipulates that Party and government organs and public institutions register public data resources included in authorized operation scopes but also encourages registration of public data resources not included in authorized operation scopes. Archival data constitutes an important component of national public data resources, and archival departments should strive to integrate into public data resource registration management and related work to enhance the niche advantage of archival data at the species level.

Archival data serves as a strategic resource driving China's economic and social progress, and archival data factors constitute an indispensable component for developing new-quality productive forces. As the data era advances and data competition intensifies, archival departments should leverage archival data factor value realization as the entry point to promote adaptation and evolution of archival data, further strengthening its core competitiveness. Examining current issues in the competitive niche of archival data and proposing corresponding optimization strategies through breadth and overlap indicators from the three fundamental dimensions of resources, functions, and spatio-temporal characteristics represents an inevitable requirement for high-quality development of archival undertakings in the big data era. In the future, archival departments should continue to deeply explore the "capacity" of archival institution collaboration, driving complementary "status" and "trend" of archival data, and continuously promote overall optimization of the competitive niche of archival data.

Funded Project: Jiangsu Provincial Archives Science and Technology Project "Research on the Construction of Oral Archives Digital Resources for Social Memory" (Project No.: 2024-5)

[1] Central People's Government of China. Complete! 50 Dynamic Scenarios Viewing the Full Text of the 2025 Government Work Report[EB/OL].(2025-03-05)[2025-03-10].https://www.gov.cn/yaowen/liebiao/202503/content_7010168.htm.
[2] Oliveira M I S,et al.Investigations into Data Ecosystems: a systematic mapping study[J].Knowledge and Information Systems,2019,61(2):589-630.
[3] Aho B.Data communism: Constructing a national data ecosystem[J].Big Data & Society,2024,1(3):1-14.
[4] Kang Li, Zhou Ming. A Preliminary Study on the Principles of Archival Niche[J].Archives and Construction,2011(12):4-7.
[5] Kang Li, Zhou Ming. Research on the Evaluation of Niche Status of National Comprehensive Archives in China's Provinces and Regions[J].Beijing Archives,2015(8):18-21.
[6] Kang Li, Zhou Ming, Li Zhongyu. Evaluation and Recommendations on the Niche Breadth of Provincial National Comprehensive Archives in China[J].Library and Information Service,2014(3):85-89.
[7] Nie Yunxia, Zhang Jiaxin, Gan Min. A Dialectical Analysis of Information Actor Niche and Digital Archival Resource Security[J].Shanxi Archives,2017(1):32-38.
[8] Yu Yingxiang, Feng Hui, Tian Zhipeng, et al. Analysis on the Ecological Operation of Archival Data in the Big Data Era[J].Information Science,2024(10):2-8.
[9][17] Jin Bo, Tian Zhipeng. Exploration on the Connotation and Characteristics of Archival Data[J].Archival Science Communication,2020(3):4-11.
[10] Li Bo. Ecology[M].Beijing:Higher Education Press,2000:103-107.
[11] Bao Qingde, Xia Chengbo. Niche: Perfecting Conceptual Connotation and Expanding Extensional Radiation—Commemorating the 100th Anniversary of the Concept of "Niche"[J].Studies in Dialectics of Nature,2010(11):43-48.
[12] Li Yining. Network Group Structure: The Evolution of Organizations in the 21st Century[M].Beijing:Xinhua Publishing House,2023:178-191.
[13] Nie Yunxia, Gan Min, Zhang Jiaxin. Analysis on the Construction Model of Community Archival Resource Ecological Pattern Based on Niche[J].Archival Science Communication,2016(4):96-100.
[14] National Archives Administration of China. Grasping the "Six Relationships" in Promoting the Newly Revised Archives Law[EB/OL].(2020-12-16)[2025-04-01].https://www.saac.gov.cn/daj/fyld/202012/1af592ec31444d7d8493cd71c767603b.shtml.
[15] Chen Wenjuan, Lou Cequn. Influence Mechanism and Adjustment Strategies of Information Niche Breadth[J].Information Studies: Theory & Application,2011(6):4-7.
[16] Wang Gang, Zhao Songling, Zhang Pengyun, et al. A Study on the Definition of Niche and the Improvement of Niche Overlap Measurement Formulas[J].Acta Ecologica Sinica,1984(2):119-127.
[18] Zhou Linxing, Cui Yunping. Exploring the Implementation Path of Archival Data Quality Control from the Perspective of Big Data[J].Archival Science Communication,2022(3):39-47.
[19] National Archives Administration of China. Summary of Basic Conditions of National Archival Administrative Departments and Archives in 2023 (Part 1)[EB/OL].(2024-09-20)[2025-02-08].https://www.saac.gov.cn/daj/zhdt/202409/fd579fbcb59f4f4eae534495f2170849.shtml.
[20] Xu Yongjun, Li Mengqiu. Strategies for the Development of Archival Higher Education During the 14th Five-Year Plan Period[J].Beijing Archives,2021(10):24.
[21] Xu Xiaotong, Zhang Jingyan, Zhang Weixia, et al. Demand, Dilemmas, and Pathways for Training Archival Science Professionals in the Digital Intelligence Era—A Survey from the Teachers' Perspective[J].Archival Science Communication,2024(3):104-112.
[22] China Highway Network. Application of Archival Datafication in Highway Government Services[EB/OL].(2025-05-26)[2025-08-01].https://jtyst.zj.gov.cn/art/2025/5/26/art_1229003857_59041649.html.
[23] Archival News Studio. Nezha in Archives: Cultural Inheritance and Innovation from Ancient Books to the Screen[EB/OL].(2025-02-11)[2025-08-01].https://mp.weixin.qq.com/s/MuI5iH3MihLBrpl2OSUQVg.
[24] Jiemian News. 2024 China Archival Data Innovation Index Report: Jiangsu Tops the List, Yangtze River Delta Shows Clustered Leadership Trend[EB/OL].(2024-10-20)[2025-02-08].https://m.jiemian.com/article/11852006.html.
[25] Zhou Yi. Archival Actions in Trusted Data Space Construction: A New Perspective for the 15th Five-Year Plan[J].Archives and Construction,2025(2):4-14.
[26] Yi Tao. Generation and Dissolution of Archival Data Barriers Under the Background of "Run at Most Once" Reform[J].Zhejiang Archives,2018(12):14-17.
[27] National Archives Administration of China. Upholding Integrity and Innovation, Working Diligently to Fully Achieve the Goals and Tasks of the 14th Five-Year Plan for Archival Undertakings—Report at the National Conference of Archives Directors and Curators[EB/OL].(2025-01-23)[2025-03-06].https://www.saac.gov.cn/daj/yaow/202501/9cb357d9822c44d69d9fb71c4c9926f25.shtml.
[28] Wei Qinzheng. Cool Reflections on the "Grand Archives" Trend[J].Archives Management,2006(3):30-31.
[29] National Archives Administration of China. Achievements in Digital Archives Construction Assist State Grid in Epidemic Prevention[EB/OL].(2020-02-19)[2025-04-07].https://www.saac.gov.cn/daj/qydagz/202002/1745a0460c94469ca4a1f7658fbd0573.shtml.
[30] Chen Xiaoting, Xu Yongjun, Hu Xiaolin. Transformation of Archival Work in the Era of Artificial Intelligence: Opportunities, Challenges, and Response Strategies[J].Archival Science Research,2024(5):4-13.
[31] Zhao Xinmiao, Yang Zhiyong. Research on Archival Data Quality Assurance from the Perspective of Policy Tools[J].Shanxi Archives,2024(9):5-15.
[32] Central People's Government of China. Central Committee of the Communist Party of China and State Council Issue the "Education Powerhouse Construction Plan Outline (2024-2035)"[EB/OL].(2025-01-19)[2025-03-09].https://www.gov.cn/zhengce/202501/content_6999913.htm.
[33] Cheng Yuan, Xie Pengxin, Wang Ping. Exploring the Training System for Digital Intelligence Talents in Archival Science Under the Background of Digital China Construction—Taking the Undergraduate Archival Science Program at Wuhan University as an Example[J].Archival Science Communication,2025(1):107-115.
[34] National Archives Administration of China. Henan Changyuan Accelerates Construction of Characteristic Archival Databases for Pillar Industries[EB/OL].(2025-07-16)[2025-08-05].https://www.saac.gov.cn/daj/c100226/202507/a1c38f786fa24b928952cc39a86a880f.shtml.
[35] National Archives Administration of China. Hubei Xiaogan Launches Pilot Reform for Archival Data Resource Assetization[EB/OL].(2025-07-23)[2025-08-05].https://www.saac.gov.cn/daj/c100227/202507/8739d250bff443b0b9da05b862a2dbd5.shtml.
[36] Zhou Yaolin, Luo Yingxu, Zhao Yue. Research on Optimization of Information Niche in Digital Archives[J].China Archives,2016(4).
[37] Li Li. Archival Departments in Beijing, Tianjin, and Hebei Jointly Sign Cooperation Agreement for Cross-Regional Sharing and Utilization of Livelihood Archives[J].Beijing Archives,2025(1):7.
[38] Yangzhou Archives and Local Chronicles Website. Baoying County Archives Collaborates with County Data Bureau to Break Through the "Last Mile" of Convenient Archival Services[EB/OL].(2025-04-15)[2025-06-13].https://daj.yangzhou.gov.cn/xwzx/gzdt/art/2025/art_749245661b1847a598b34fd929407aea.html.
[39] Yuan Jie. Interpretation of the Archives Law of the People's Republic of China[M].Beijing:China Democracy and Legal System Publishing House,2020:95-97.
[40] Bian Xianjie. Goal Orientation and Implementation of Archival Information Resource Sharing Platform Construction in the Big Data Era[J].Archives Management,2020(5):75-76.