Garbage in, garbage forever: why data needs a cold-chain mentality

Corporate data fragmentation poses significant challenges to economic coordination, regulatory oversight and public service delivery (Pemmasani and Abd Nasaruddin, 2022). While individual companies may optimize internal data practices, the absence of universally enforced standards creates structural inefficiencies at the ecosystem level, impeding cross-border trade, financial transparency, supply chain visibility and evidence-based policymaking (Fan et al., 2014). This lack of universal standards leads to systemic data deterioration that directly impacts societal outcomes. In the public sector, this deterioration manifests as failures in service delivery, exemplified by errors in social welfare eligibility assessments or delayed public health responses, often caused by incompatible data formats between government agencies and private providers (Van de Walle, 2016; Toscani, 2026a; Toscani, 2026b). While prior literature has predominantly addressed data quality through localized coding practices or internal governance (Miller and Mork, 2013; Gebru et al., 2021), this study argues that the current lack of a “cold chain” for data results in a profound loss of public value. From health-care information exchanges to smart city platforms and climate monitoring systems, the inability to seamlessly integrate heterogeneous corporate data undermines both private innovation and public welfare (Kazantsev et al., 2023). This paper contends that government intervention is essential to establish a “data cold chain”: a standardized framework designed to preserve data integrity as information moves across organizational and sectoral boundaries. Just as food safety regulations mandate strict temperature controls in perishable supply chains to ensure consumer safety (Singh et al., 2018), governments must enforce binding data standards to prevent value deterioration in digital ecosystems.

Unlike voluntary industry initiatives, which frequently suffer from fragmented adoption and competitive reluctance (Studer et al., 2005), government-led standardization offers unique institutional advantages. Regulatory authorities can establish mandatory compliance mechanisms that transcend individual corporate interests, create neutral arbitration platforms for multistakeholder governance and overcome the coordination failures inherent in market-driven approaches (Xi, 2024). Furthermore, government oversight ensures public accountability for data practices that fundamentally affect citizen outcomes. The data cold chain framework thus extends beyond technical interoperability to address the complex sociotechnical dimensions of governance (Markus and Topi, 2015). It recognizes that data deterioration stems not only from incompatible formats, but from organizational silos, misaligned incentives and the absence of shared quality metrics (Toscani, 2026a, 2026b). As the institutional architect, the government is uniquely positioned to align these elements through policy, regulation and coordinated investment in public data infrastructure. Consequently, this study seeks to answer the following research question:

Market forces alone have proven insufficient to achieve interoperability at scale. However, innovations emerged through prolonged negotiation among industry stakeholders, often taking decades to achieve widespread adoption. In rapidly evolving digital contexts, such timescales are untenable (Bidgood et al., 1997; Walravens, 2010). Government intervention accelerates standardization through several mechanisms. Mandating compliance through regulation proves more effective than relying on voluntary adoption, as demonstrated by cold chain regulation in China, where mandatory standards outperform recommendations (Zhao et al., 2018). Governments provide neutral governance structures that balance competing private interests, enabling coordination that market participants cannot achieve independently (Borgogno and Colangelo, 2019). Public institutions coordinate investments in shared infrastructure that individual firms systematically underinvest in, recognizing that these investments benefit competitors as much as themselves (Bhatt et al., 2017; Toscani and Prendergast, 2022). Finally, the government ensures equitable access to standardized data ecosystems, preventing monopolistic control by dominant platforms. The European Union (EU)’s General Data Protection Regulation (GDPR) exemplifies how government action can reshape entire data ecosystems, forcing thousands of organizations to harmonize practices around common principles (Calzati and van Loenen, 2023). Similarly, financial reporting standards enforced by securities regulators create transparency that markets alone would not produce (Armstrong et al., 2010). These examples demonstrate that regulatory intervention, when properly designed, catalyzes coordination that voluntary mechanisms cannot achieve (Zurstrassen, 2025). Corporate data governance, when left unregulated, optimizes for firm-level efficiency rather than ecosystem-level interoperability (Shao et al., 2024). Three structural barriers impede voluntary harmonization. Competitive disincentives lead companies to view proprietary data architectures as competitive advantages, actively resisting standardization that could benefit rivals (Jussen et al., 2024). Coordination failures emerge even when standardization would benefit all parties, as collective action problems prevent the emergence of shared frameworks without external coordination (Skopik et al., 2016). Short-term optimization pressures cause corporate decision-making to prioritize immediate operational needs over long-term ecosystem health, leading to fragmented practices that accumulate over time (Nadkarni and Prügl, 2021). These barriers are not theoretical abstractions, but practical realities observed across industries. Health-care providers develop incompatible electronic health record systems despite clear benefits from interoperability. Financial institutions maintain idiosyncratic transaction formats that complicate regulatory oversight (Xi, 2024). Supply chain participants deploy proprietary tracking systems that prevent end-to-end visibility (Hernández et al., 2024). In each case, rational firm-level decisions produce collectively suboptimal outcomes that only government coordination can address (Thakur-Weigold and Miroudot, 2024). The cold chain in perishable goods management offers a compelling model for government regulation of data quality. Food safety authorities mandate continuous temperature monitoring from production to consumption, ensuring that products remain within safe thresholds throughout distribution quality (Wang et al., 1995). They require mandatory documentation of handling at each transfer point, creating accountability for quality maintenance, because liability frameworks assign clear responsibility for quality failures, enabling enforcement when deterioration occurs (Reimann, 2003). Regular inspections and enforcement mechanisms ensure compliance rather than relying on voluntary adherence to best practices. Temperature serves as a singular, universally accepted metric (Singh et al., 2018), a quality indicator that all stakeholders monitor using standardized instrumentation. This contrasts sharply with fragmented data quality metrics across organizations, where each entity defines quality differently, making cross-organizational assessment impossible (Wang et al., 1995). The power of the cold chain model lies not in technological sophistication, but in regulatory simplicity: one metric, universally measured, consistently enforced. Applying the cold chain model to corporate data requires the government to undertake four core functions. First, define core data quality metrics analogous to temperature thresholds that all participants must monitor (Heinrich et al., 2018). Second, mandate continuous monitoring as data moves across organizational boundaries, ensuring visibility into degradation points (Adepoju et al., 2022). Third, establish clear liability for data degradation at each handoff point, creating accountability that drives careful stewardship (Chan et al., 2019). Fourth, create enforcement mechanisms with meaningful consequences for noncompliance, ensuring that standards shape actual behavior rather than remaining aspirational guidelines (Tanzi and Pitea, 2009). The government possesses institutional capabilities that private actors lack, making public leadership essential for ecosystem-wide standardization. Regulatory authority provides the power to mandate compliance across all market participants, preventing free-riding on standardization efforts that plague voluntary initiatives. A long-term perspective enables the government to prioritize ecosystem health over quarterly performance metrics that constrain corporate decision-making, allowing investments in infrastructure that mature over decades. Convening power grants the government the legitimacy to assemble diverse stakeholders, including competitors, to negotiate shared standards that serve collective interests (Gray et al., 2022). Public accountability through democratic oversight mechanisms aligns governance with societal interests rather than shareholder returns, ensuring that standardization serves broad public welfare (Akinsola, 2025). Recent initiatives demonstrate the government’s potential when it exercises these capabilities effectively. Singapore’s Smart Nation platform showcases how state-led coordination can integrate data across transportation, energy and urban planning sectors, creating synergies impossible through market coordination alone (Woods et al., 2023). Existing research on data governance tends to focus on either technical interoperability, examining standards development and application programming interface (API) design as engineering challenges (Wahyuni et al., 2025); corporate data management, analyzing internal governance and analytics capabilities; or privacy regulation, studying GDPR compliance and data protection frameworks. This is a comprehensive framework for government-coordinated, cross-sector data standardization that addresses the sociotechnical dimensions of ecosystem-wide data integrity (Hietala, 2024). While scholars recognize the need for interoperability (Noura et al., 2019), few explore the government’s role as active orchestrator rather than passive regulator responding to market failures. This gap has practical consequences. Policymakers lack clear frameworks for designing interventions that balance regulatory authority with innovation incentives (Renda and Pelkmans, 2023). Corporate leaders cannot anticipate what government-led standardization might require, leading to defensive postures rather than proactive engagement (Liveris, 2023). Civil society organizations struggle to advocate for specific policy mechanisms that would enhance data quality without stifling commercial activity (Mohyeddin, 2024). This paper fills this gap by proposing the data cold chain as a policy framework for government-led standardization, grounded in empirical evidence from practitioners navigating data deterioration across organizational boundaries. By examining both the problems that necessitate intervention and the mechanisms through which government can effectively coordinate solutions, this research provides actionable guidance for policymakers while advancing theoretical understanding of data governance in complex ecosystems.

We used semistructured interviews to understand how data professionals experience fragmentation across organizational boundaries and what role they see for government intervention (Eppich et al., 2019). The study deliberately included both public-sector officials and private-sector data leaders to capture tensions between regulatory mandates and corporate operational realities. This dual perspective proved essential for understanding not just where standardization is needed, but where it is feasible and how resistance might emerge. The interview approach allowed participants to share detailed narratives about specific instances of data deterioration, moving beyond abstract discussions of interoperability to concrete examples of value loss (Hicks et al., 2021). By grounding the research in lived experience rather than hypothetical scenarios, we captured the practical challenges that any government-led framework must address. The flexibility of semistructured interviews also enabled us to explore unexpected themes that emerged during conversations, leading to insights that structured surveys would have missed (Charmaz, 2014). To capture diverse insights and maintain proximity to unorthodox paradigms, this study used a broad geographical and industrial sample of 49 experts from 27 countries (see Table 1) across sectors including high-tech, pharmaceuticals, public sector, manufacturing and banking (Bamberger, 2018).

Recruited via LinkedIn with a 4% response rate, participants were selected based on their explicit involvement, active engagement and primary professional responsibility in machine learning (Hicks et al., 2021; Wang et al., 2019B). All experts possessed at least three years of professional experience with multiorganizational data projects, ensuring depth of perspective on practical challenges that transcends theoretical understanding (Ashok et al., 2017). This study was not originally designed with an exclusive focus on government, and as a result, public-sector officials represent a minority of the participants. The participants included government data officials, but mainly involved corporate data executives, including data architects, analytics directors and information governance leads, who encounter the operational consequences of standardization and interoperability requirements in practice. This sampling approach ensured diverse perspectives on current pain points in cross-organizational data exchange, revealing both universal challenges and sector-specific issues. Questions were deliberately framed to elicit concrete examples rather than abstract opinions, grounding responses in lived experience (Ren, 2022). Interviews were conducted in English, recorded with permission and transcribed using automated tools with researcher verification for accuracy (Chen, 2012). The conversational format allowed us to probe unexpected responses, following interesting threads that emerged during the discussion. Several interviews led to follow-up conversations when initial discussions revealed particularly illuminating cases, enabling deeper exploration of complex dynamics. Following grounded theory methodology, we conducted iterative coding that allowed theory to emerge from data rather than imposing predetermined frameworks (Corbin and Strauss, 2015). Analytically, these findings were derived through a multistage qualitative coding process (Locke et al., 2022) from first-order to higher-order codes, which identifies the axial coding stage at which structural parallels with the cold chain model became analytically legible (Table 2).

First-order codes were generated directly from interview language, such as “incompatible assumptions,” “unclear reuse rules” and “deadline-driven shortcuts.” Line-by-line open coding identified recurring themes of data degradation and coordination failure, producing hundreds of observations later organized into higher-order categories (Charmaz, 2014). Related codes were then clustered into second-order themes reflecting shared mechanisms, while axial coding linked them to distinct deterioration processes and possible government interventions (Jean et al., 2016). Although grounded theory guided coding, the data cold chain framework emerged through abductive reasoning, iterating between empirical patterns and regulatory analogies (Timmermans and Tavory, 2012). The four deterioration categories arose directly from interview data describing breakdowns, context loss, incompatible formats and progressive degradation across institutions. During selective coding, these patterns showed strong parallels with cold chain regulation in perishable goods: deterioration during transfer, prevention through continuous monitoring and limits of voluntary compliance. Integrating these insights produced four higher-order analytical categories centered on a government-orchestrated data cold chain, later validated through theoretical sampling and participant feedback. We revisited earlier interviews to confirm analytical fit and used later interviews to explicitly test the cold chain metaphor and its policy relevance. A recurring pattern showed that governance and public intervention are critical to resolving coordination failures, motivating a government-centered interpretation. Comparative analysis across sectors (e.g. health care and logistics) and contexts showed consistent underlying dynamics despite contextual variation. Findings indicate that data deterioration is not merely technical. But a policy barrier, as boundary crossings without a cold chain cause context loss and administrative friction. Overall, we identified three types of cases: cases where respondents expressed strong confidence in voluntary coordination and questioned the need for government intervention. These were primarily found among participants from market-liberal regulatory cultures (notably North American and some Southeast Asian contexts). We explain analytically why these accounts do not falsify the framework, but rather clarify the conditions under which voluntary mechanisms can partially succeed, and the limits of those conditions at the ecosystem scale. Cases where respondents consistently described government-led standardization efforts that had failed or created new coordination problems, for example, where mandated schemas were too rigid to accommodate legitimate domain variation. These cases led us to qualify the framework by emphasizing that the cold chain variables must be minimal and interoperability-enabling, not comprehensive or prescriptive. Finally, cases where time pressure and delivery demands were described positively, as drivers of pragmatic simplification rather than as sources of deterioration. These boundary cases prompted us to refine the claim in the discussion, distinguishing between productive time pressure that drives useful iteration and deadline-driven shortcuts that undermine data lifecycle coherence. To provide technical grounding, this study identifies four cold chain variables that must remain constant across transfers: provenance completeness, schema stability, temporal precision and integrity verification through automated checksums or cryptographic hashes. This abductive framework is both empirically grounded and theoretically generative. The cold chain is not merely a metaphor, but an analytical device revealing the regulatory gap between data protection, which governments regulate, and data integrity preservation, which they largely do not. It emerged through sustained engagement with empirical patterns, informed by adjacent regulatory domains, and later validated through participant feedback (Timmermans and Tavory, 2012).

The findings show that data deterioration is driven less by technical limits or individual incompetence than by the absence of uniform, enforceable regulation across the full public-sector data lifecycle. Interviews revealed that data loses consistency, meaning and policy relevance as it moves across agencies, projects and institutional boundaries. These recurring losses fall into four interrelated categories, each linked to a specific regulatory gap.

Interviewees consistently described upstream fragmentation in data definitions, formats and validation rules as the primary source of downstream data failure. Private-sector data architects and public-sector officials described the problem in structurally identical terms, but diverged sharply in their attribution of responsibility, corporate participants tending to externalize responsibility to regulators, public officials tending to attribute fragmentation to corporate competitive resistance. While individual data sets may be internally “clean,” they frequently become unusable once integrated with data produced under different institutional assumptions. One respondent explained that, from a research perspective, analysts are often forced to inherit implicit assumptions embedded in data sets created elsewhere (#34). In practice, this means accepting predefined ranges, categories or units without knowing how or why they were chosen. Crucially, respondents emphasized that poor-quality data is rarely the result of negligence. Instead, it is often produced by incompatible standards that are either imposed or tolerated by different public bodies. As one interviewee working on national AI standardization explained, their role focused almost entirely on defining core concepts for government use (#43). The challenge was not advanced analytics, but the absence of shared foundational definitions across organizations. Several participants consistently described the disproportionate effort required to reconcile data sets that are nominally interoperable. One expert described industrial vision systems that fail as soon as environmental conditions change, such as lighting or camera position (#48). While this example came from industry, respondents repeatedly drew parallels with government data systems, where locally optimized standards prove fragile when exposed to real-world variation. The consequences of this fragmentation are often severe. One participant recounted multiple projects that failed outright because teams developed models without first agreeing on deadlines, standards or integration requirements (#34). When these teams eventually attempted to align their outputs, the results were unusable. The interview data demonstrate a consistent pattern: voluntary coordination is insufficient. Without binding regulation enforcing shared schemas, metadata standards and validation protocols, organizations optimize locally while eroding collective data integrity. A Chief Data Officer from a highly data-driven firm described how even basic product categories, such as geographic labels like “South,” were interpreted differently across units, requiring an ongoing, resource-intensive standardization effort (#49). Viewed through a data cold-chain framework, this pattern is consistent with a system in which deterioration is not an incidental outcome, but a structurally determined one, analogous to an architecture where refrigeration is rendered functionally optional by design.

A second, closely related pattern concerns the systematic underuse of available public data. Interviewees consistently described situations in which valuable data sets remain partially exploited, or not used at all, due to regulatory uncertainty, privacy concerns or institutional risk aversion. Importantly, this underutilization is not driven by a lack of analytical capability, but by unclear and inconsistently interpreted governance frameworks. It is important here to distinguish between participants from heavily regulated sectors (health care and finance) who described legal ambiguity as a persistent operational problem, and those from less regulated sectors (logistics and retail analytics) who more often described it as manageable through internal legal counsel. One respondent working in content moderation explained that privacy agreements strictly limit what data can be exposed or reused, even when analytical value is clear (#31). Navigating these constraints was described as a central part of the job, yet the boundaries themselves were often ambiguous. The interview data demonstrate how respondents consistently described how different agencies interpret the same legal frameworks in divergent ways. This leads to inconsistent anonymization practices and fragmented data sets that cannot be recombined. One participant illustrated the frustration from a service perspective: customers frequently ask whether existing data, such as e-mail content or signatures, can be used to verify or update contact information (#41). While technically feasible and potentially valuable, organizational boundaries prevent systematic reuse, leaving large volumes of data outdated and unmanaged. As a result, analysts are often forced to work with incomplete representations of reality, even when more comprehensive data exists elsewhere in the public sector.

Human error is frequently cited as a primary cause of data failure. However, interview evidence suggests it is more accurately understood as a downstream symptom of weak governance and unclear standards. Respondents consistently described environments in which missing documentation, unclear provenance and inconsistent definitions force professionals to rely on assumptions and informal workarounds. One interviewee emphasized that data work requires not only technical skills, but also a deep understanding of how data is prepared, structured and constrained (#1). At the same time, they noted that rigid predefined options often fail to reflect real-world complexity, creating a tension between formal categories and practical insight. Another participant provided a particularly revealing account of how errors emerge. They described being handed results that appeared incorrect, only to discover that the issue was not a technical bug but a misunderstanding about how certain quantities were meant to be counted (#8). In their experience, such misalignments stemmed from communication failures and unspoken assumptions rather than faulty algorithms. When governance structures are weak, this reliance on human judgment becomes a liability rather than a safeguard. Several respondents stressed that assumptions silently replacing standardized rules introduce significant risks, including bias. One participant warned that poorly specified data practices can lead to gender or skin-tone bias, outcomes that organizations are highly motivated to avoid but poorly equipped to prevent without clear standards (#33). These findings invite a normative inference that human judgment should be eliminated from data processes. On the contrary, expert interpretation is often essential. However, governments must regulate the conditions under which judgment operates.

The final category concerns time pressure and its interaction with regulatory misalignment. Interviewees consistently reported operating under intense temporal constraints driven by political cycles, funding requirements and crisis-oriented decision-making. While time pressure is intrinsic to public administration, its negative effects are amplified when data standards are inconsistent or absent. These negative effects of time pressure on data quality were described most acutely by participants in public-sector and quasi-governmental roles, consistent with the political cycle dynamics described conceptually, but not previously illustrated comparatively. Several respondents described strategies for coping with deadline-driven work, emphasizing the need for flexibility and personal time management (#40, #47). These strategies help individuals manage stress, but do little to address systemic fragmentation. In practice, they often reinforce short-term optimization at the expense of long-term coherence. One participant observed that development teams frequently focus on narrow, short-term objectives, especially in environments driven by performance metrics or sales pressure (#35). Under such conditions, collaboration declines and data practices become increasingly siloed. Others explicitly linked time pressure to reduced learning and innovation, while process improvements often aim to shorten timelines and reduce manual errors (#15), respondents warned that rapid delivery frequently locks in overly limited problem definitions (#1). A participant captured this tension by describing the pressure to deliver proof-of-concept results at the end of research projects, often under rigid standards and expectations (#40). The need to demonstrate immediate outputs leaves little room for building a durable data infrastructure. Overall, the findings suggest that organizations frequently demand evidence-based decision-making without investing in the regulatory foundations required to sustain reliable data over time. Treating data as a short-term project resource rather than long-term public infrastructure accelerates value deterioration. Lifecycle-oriented regulation would lower the cost of reuse and enable faster responses without sacrificing reliability. In this sense, regulation does not inhibit agility, it institutionalizes it. Across all four categories, interviewee accounts converge on a single conclusion: data deterioration is fundamentally a regulatory failure. Fragmented standards, underutilized data, error-prone interpretation and time-driven shortcuts all originate from the absence of universally enforced data rules. The data cold chain framework makes visible why local competence and technical excellence cannot compensate for systemic incoherence. These findings reinforce the central argument of the paper: governments must take responsibility not only for regulating how data is used, but for regulating how data is structured, transmitted and preserved across its entire lifecycle.

This study reframes data deterioration as a systemic governance failure rather than a technical or behavioral shortcoming. While prior literature has predominantly addressed data quality through coding practices, internal governance models or post hoc documentation mechanisms (Miller and Mork, 2013; Gebru et al., 2021), our findings demonstrate that such approaches are insufficient when data traverses organizational and sectoral boundaries. The four categories of deterioration were present across all sectors and regulatory contexts, but the relative weight and primary mechanism differed systematically by actor group and institutional setting. The empirical evidence shows that even highly competent data professionals are constrained by fragmented regulatory environments that allow incompatible standards, inconsistent interpretations of legal requirements and uncoordinated lifecycle management. The data cold chain framework advances the literature by shifting the locus of responsibility from individual organizations to public institutions (Ghahramani, 2015). Analogous to food safety regulation, where governments mandate continuous temperature monitoring regardless of handler expertise, this study argues that governments must define and enforce a limited set of universal data integrity variables that remain stable across the entire data lifecycle. Central public authorities, such as a National Data Office, a central IT authority or sector-specific regulators (e.g. health or financial monitors) would define and enforce a limited set of universal data integrity variables that remain stable across the entire data lifecycle (Toscani, 2025a). These variables are not intended to replace domain-specific standards, but to provide a minimal, binding foundation upon which interoperability becomes structurally feasible. Importantly, the findings challenge the assumption that improved communication, collaboration or professional training alone can resolve data fragmentation. While sociotechnical scholarship correctly emphasizes the role of human judgment and epistemological diversity (Markus and Topi, 2015; Pachidi et al., 2014), our results indicate that human discretion becomes a source of deterioration precisely when regulatory anchors are absent. Without enforceable upstream standards, downstream collaboration merely redistributes ambiguity rather than eliminating it. The research also suggests a critical regulatory imbalance: governments have developed sophisticated regimes for data protection and privacy, yet have largely neglected data usability, interoperability and lifecycle coherence (Xi, 2024). This asymmetry produces a paradox in which data is legally protected, but analytically impoverished. The study thus extends debates on digital governance by arguing that protecting citizens’ rights and preserving data value are not competing objectives, but complementary ones that require coordinated regulatory design (Toscani, 2025b). To ensure the rapid adoption of these standards, governments should look beyond traditional legislation. A powerful nonregulatory lever is the modification of public procurement rules: by mandating that the government only procures digital services from vendors who adhere to “data cold chain” standards, the public sector can use its market power to drive industry-wide compliance (Pemmasani and Abd Nasaruddin, 2022). Ultimately, the data cold chain should be understood as enabling infrastructure rather than a regulatory burden (Fan et al., 2014). A lifecycle-oriented regulatory framework lowers coordination costs, accelerates decision-making under uncertainty and enables scalable evidence-based policymaking that serves both business innovation and the public good (Bidgood et al., 1997; Walravens, 2010). Finally, the findings contribute to policy discourse by demonstrating that regulation does not inherently reduce agility or innovation (Kazantsev et al., 2023). On the contrary, the absence of uniform standards increases time pressure, encourages short-term workarounds and undermines reuse. The data cold chain concept would also help address the data integration challenge that was identified years ago (Dong and Srivastava, 2013), as illustrated by the example of Palantir, which viewed AI largely as a layer built upon the prior necessity of data integration. Although generative AI has now reduced part of this constraint, a solution implemented at the source, like the data cold chain, would still be more logical and effective (Klein, 2026). A lifecycle-oriented regulatory framework lowers coordination costs, accelerates decision-making under uncertainty and enables scalable evidence-based policymaking. In this sense, the data cold chain should be understood as enabling infrastructure rather than a regulatory burden.

This study has several limitations that should be acknowledged. First, while the research proposes the data cold chain as a regulatory framework, it does not specify a definitive set of universal data variables that governments should mandate. The identification of such variables is intentionally left open, as their precise configuration is likely to vary across sectors, risk profiles and institutional contexts. As a result, the framework is conceptual rather than prescriptive at the technical level. Second, the study relies on qualitative interviews with data professionals, which capture perceptions, experiences and institutional constraints but do not directly measure data quality outcomes before and after regulatory intervention. While this approach is appropriate for theory building and policy framing, it limits the ability to make causal claims about the quantitative impact of standardization on performance or innovation. Third, although the sample is geographically diverse, regulatory cultures differ significantly across jurisdictions. The findings may therefore reflect a stronger alignment with regulatory systems where government intervention is institutionally accepted, such as the EU or parts of Asia, compared to more market-driven governance models. This limits the immediate generalizability of implementation pathways, even if the underlying mechanisms of data deterioration are broadly applicable. Finally, the cold chain analogy, while analytically powerful, necessarily simplifies the multidimensional nature of data quality. Unlike temperature, data integrity cannot be reduced to a single scalar variable. The value of the analogy lies in regulatory logic rather than literal equivalence, and misuse of the metaphor could lead to overly rigid or technocratic interpretations if applied uncritically.

Future research can extend this work in several directions. First, empirical studies are needed to identify and test candidate “cold chain variables” for data, such as provenance completeness, schema stability, temporal resolution or transformation traceability. Comparative sectoral studies, across health care, finance, logistics and climate governance, would help determine which variables are truly universal and which must remain context-specific. Second, quantitative and quasi-experimental research could evaluate the effects of government-mandated data standards on interoperability, policy effectiveness and innovation outcomes. Natural experiments arising from regulatory changes, such as the introduction of mandatory reporting schemas or cross-agency data platforms, offer promising opportunities for causal analysis. Third, future work should examine institutional design questions related to enforcement, liability and compliance. How responsibility for data deterioration should be allocated across data producers, intermediaries and users remains underexplored. Insights from financial reporting regulation, food safety and environmental monitoring could inform viable enforcement models for data governance. Finally, interdisciplinary research is needed to explore the political economy of data standardization. Resistance from incumbents, concerns about surveillance and geopolitical competition over digital infrastructure all shape governments’ capacity to impose uniform standards. Understanding how regulatory legitimacy is constructed, and contested, in data ecosystems will be essential for translating the data cold chain framework into practice.