Data-related Issues Research Articles

Data governance challenges on smart home involving multiple research sites

Abstract Background The development of Ambient Assisted Living (AAL) technology has great potential for ubiquitous health monitoring through collaboration between multiple research sites, but its use poses significant challenges to data governance. This study aims to investigate data governance challenges arising when smart home data flows across different research sites. Methods A four-analytic-dimension method was used to identify potential data-related issues that may occur when heterogeneous smart home data moves around multiple research sites. These dimensions include data domain, stakeholders, value, and governance goal. Results Data domain has difficulty in categorizing new data types generated by analytical models without a specified agreement, and determining the regulations for varying levels of anonymization and privacy protection is unclear in the AAL research field. Stakeholders in the AAL project may hold have different norms and practices for data use. Although some common values may be shared, such as improving the quality of homecare, conflicts may still arise and need to be regulated by data governance. Shifting AAL data between stakeholders may reduce its value, as different stakeholders may have varying interests. For instance, removing identifiable information from data to preserve privacy may reduce its value for researchers exploring individuals’ activity patterns. Governance goals are objectives for maintaining and facilitating data. While some governance goals can be widely acknowledged and shared, such as ensuring data security and privacy, conflicts may still exist over others, such as data access. Conclusions The big data generated by smart home technology highlights the need to develop proper data governance models to regulate data. This study’s implications include a better understanding of potential data-related challenges when data flows around multiple research sites, helping policymakers set up suitable data governance models. Key messages • AAL tech for health monitoring poses data governance challenges due to varying stakeholder norms and values. • Proper data governance models need to be developed for AAL data as it remains challenges in data domain, stakeholders, value, and governance goal.

Digitization Planning for Large Collections and Agencies with Specify 7

The Specify Collections Consortium (SCC) supports research collections around the world with an intuitive, robust, and highly-customizable software platform to digitize their holdings and manage their collections. For over 25 years, Specify has provided and sustained biological research museums and biorepositories with software for managing, integrating, and publishing collections information (Fig. 1). The SCC has increasingly engaged with very large collections managed by institutions ranging from universities to individual government departments to entire federal agencies, to envision a framework that supports standard computerization workflows and best practices. From our involvement in working with these institutions, which have successfully navigated the transition to Specify, we have gathered insights and learned lessons that may be helpful to others to better anticipate and address key data-related issues in their own institution when embarking on a Specify (or any collections management system) implementation. Understanding how to approach data preparation for import into a collections management system is crucial, as collections often face significant challenges in this area. We will discuss some of the key hurdles that collections encounter and methods to overcome them such as unstandardized data within a single collection and between multiple collections, unparsed names for taxonomy and people, and reconciling pre-existing custom data structures with the fixed structure of a collections management system. We will also cover the decision-making process for determining what collection data should be shared between collections versus what should be managed separately (scoping, data sharing, and data exporting). Large institutions often have multiple collections with diverse needs, and finding the right balance between centralized coordination and individual collection autonomy can be complex. The presentation will discuss the factors considered and the strategies employed to navigate this balance, as it can have significant implications for the overall success of a Specify deployment (Fig. 2). In addition to the technical considerations, the presentation will also delve into the human side of transitioning to a new collections management system. Successful collections management platform implementations require not only technical expertise, but also effective communication and collaboration among the collections staff, volunteers, and management. This presentation offers a framework to help other large collections successfully transition to Specify. Our team at the Specify Collections Consortium has extensive experience in supporting the transition of large research collections to their robust and highly customizable software platform. We have gained valuable insights into the key challenges and best practices for data preparation, system implementation, and change management. By sharing the lessons learned from these institutions and the SCC's experience, the presentation provides valuable insights and guidance to support other organizations in their collections management system implementation projects. A better understanding of this process will ultimately contribute to preserving and improving access to natural history collections (and soon geoscience collections) worldwide.

The Needs of Researchers Implementing Supply Chain Digitalisation

Abstract The digitalisation of the supply chain has presented substantial opportunities for companies to enhance resource allocation, pricing strategies, and overall operational efficiency. In this context, researchers play a crucial role in developing novel methodologies and algorithms that can confer a competitive edge to stakeholders. This study employed operational research methods to investigate the specific requirements of researchers in this domain. The results highlight the challenges, motivations, and practical needs experienced by researchers. Additionally, data-related issues and databases were examined to identify areas of improvement. The findings indicate that enhancing researchers' engagement in data-driven solutions for supply chain problems primarily hinges on addressing issues related to data quality, data accessibility, and regular dataset updates. By addressing all the aspects defined in this study, organisations can enhance the practical implementation of findings and drive advancements in supply chain management.

Addressing Data Challenges to Drive the Transformation of Smart Cities

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data is the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data.

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

Over half a decade into the adoption of IFRS 9: a systematic literature review

PurposeIn 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.Design/methodology/approachThe study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.FindingsWe document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.Originality/valueAs the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.

Conditional Proxy Re-Encryption-Based Key Sharing Mechanism for Clustered Federated Learning

The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme’s security against selective ciphertext attacks without relying on random prediction.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

The Importance of China's Competition Law in the Management of Data in the Country's Rapidly Developing Digital Economy: Policy Recommendations for Pakistan

With the expansion of the digital economy and the rising importance of data, organizations are under more regulatory scrutiny over the collection, use, and sharing of data. Antitrust and competition authorities and governments worldwide are now evaluating and deliberating on the applicability and suitability of antitrust and competition laws in addressing data-related issues, notably within the digital economy. Parallel ideas and concerns are also gaining prominence in China. China has not only been enhancing its legislative framework to govern and facilitate state control over data, but it has also significantly strengthened regulatory oversight and control over Internet and technology firms. Competition legislation has prominently featured in China's governmental efforts to suppress the Internet and technology industries. Regulatory competition in the post-industrial digital economy is examined. Regulatory competition is achieved through neo-protectionism, which aims to boost social and economic development and growth by creating new comparative advantages in the digital economy. Innovative, digital, and information Neo-protectionism has emerged as a key aspect of international economic strategy for nations that lead its implementation. This article examines how China's competition laws may regulate Internet and technology companies' data and behavior. By analyzing China's data regulatory structure and competition rules, including political and economic aspects, this is achieved. China's political economy and competition rules consider many interests, aims, and priorities. Concerns that other nations may not associate with competition law may be included. China's macroeconomic monitoring guides competition law enforcement.

A systematic review of retractions in biomedical research publications: reasons for retractions and their citations in Indian affiliations

Retractions of peer-reviewed biomedical journal articles with Indian authorship have been on the rise for many years. Our study aimed to investigate the reason behind these retractions, namely plagiarism, falsification, fabrication, duplicate publication, author conflicts, ethical issues, fake peer-reviews, and data-related issues, besides providing year-wise trends regarding retraction, authorship, impact factor, and citations. We retrieved retracted publications with Indian affiliations indexed in MEDLINE between 1 January 1990 to 31 December 2021. During this period a total of 619 papers from 372 different journals with median values (interquartile range) pertaining to impact factor [3.2 (1.5, 5.2)], retraction time [24 (10, 51)] months, pre-retraction citations [4 (1, 12)], and post-retraction citations [4 (2, 12)] were retracted. While retractions still account for a small fraction of all publications (0.1%), the overall rate of retractions, that is, the number of retractions relative to the number of newly published journal articles in a given year, has been increasing. The reasons for retractions included plagiarism (27%), falsification and fabrication (26%), duplicate publication (21%), erroneous data (12%), authorship issues (4%), fake-peer reviews (3%), and ethical and funding issues (2%). We have analysed these reasons separately and compared them with each other. Besides a spurt in retraction due to plagiarism, instances of falsification have been escalating over the past decade. Half of the papers retracted on grounds of falsification were published by repeat offender authors in high-impact journals. Furthermore, 82% of retracted papers continued to accumulate citations even after the release of the journal retraction notices. The increase in retractions raises concerns over research quality as well as the wastage of scientific resources, which is especially pressing considering the present environment of scarce funding. The problem of retractions due to reasons such as plagiarism, duplicate publication, authorship issues, and, ethical issues as well as post-retraction citations can be mitigated by educating and raising awareness on publication ethics and responsible research conduct of researchers and journal publishers. Retractions due to fabrication, falsification, and fake peer reviews are more challenging to difficult to address and require further research for the identification of effective solutions.

Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers

Data pipelines are an integral part of various modern data-driven systems. However, despite their importance, they are often unreliable and deliver poor-quality data. A critical step toward improving this situation is a solid understanding of the aspects contributing to the quality of data pipelines. Therefore, this article first introduces a taxonomy of 41 factors that influence the ability of data pipelines to provide quality data. The taxonomy is based on a multivocal literature review and validated by eight interviews with experts from the data engineering domain. Data, infrastructure, life cycle management, development & deployment, and processing were found to be the main influencing themes. Second, we investigate the root causes of data-related issues, their location in data pipelines, and the main topics of data pipeline processing issues for developers by mining GitHub projects and Stack Overflow posts. We found data-related issues to be primarily caused by incorrect data types (33%), mainly occurring in the data cleaning stage of pipelines (35%). Data integration and ingestion tasks were found to be the most asked topics of developers, accounting for nearly half (47%) of all questions. Compatibility issues were found to be a separate problem area in addition to issues corresponding to the usual data pipeline processing areas (i.e., data loading, ingestion, integration, cleaning, and transformation). These findings suggest that future research efforts should focus on analyzing compatibility and data type issues in more depth and assisting developers in data integration and ingestion tasks. The proposed taxonomy is valuable to practitioners in the context of quality assurance activities and fosters future research into data pipeline quality.

Predicting the BRAF mutation with pretreatment MRI radiomics features for melanoma brain metastases receiving Gamma Knife radiosurgery

To develop a model using radiomics features extracted from magnetic resonance imaging (MRI) images of Gamma Knife radiosurgery (GKRS) to predict the BRAF mutation in patients with melanoma brain metastases (MBM). Data of 220 tumours were classified into two groups. One was a group whose BRAF mutation was identified, and the other group whose BRAF mutation was not identified. We extracted 1,962 radiomics features from gadolinium contrast-enhanced T1-weighted MRI treatment-planning images. Synthetic Minority Over-sampling TEchnique (SMOTE) was performed to address the unbalanced data-related issues. A single-layer neural network (NN) was used to build predictive models with radiomics features. The sensitivity, specificity, accuracy, and the area under the curve (AUC) were evaluated to assess the model performance. The prediction performance for the final evaluation without the SMOTE had an accuracy of 77.14%, a specificity of 82.44%, a sensitivity of 81.85%, and an AUC of 0.79. The application of SMOTE improved the prediction model to an accuracy of 83.1%, a specificity of 87.07%, a sensitivity of 78.82%, and an AUC of 0.82. The current study showed the feasibility of generating a highly accurate NN model for the BRAF mutation prediction. The prediction performance improved with SMOTE. The model assists physicians to obtain more accurate expectations of the treatment outcome without a genetic test.

Calibrating lane-changing models: Two data-related issues and a general method to extract appropriate data

Lane-changing is a routine but difficult driving task with important implications on traffic flow characteristics. Despite significant progresses in lane-changing decision modeling, lane-changing models are often improperly calibrated due to two issues related to trajectory data processing. First, the time of insertion (i.e., the time instant where a vehicle crossed the lane marking) is incorrectly considered as the lane-changing decision point, since the lane-changing decision is typically made earlier. Secondly, there is an imbalance between the number of non-lane-changing and lane-changing events, where non-lane-changing events typically dominate trajectory data. These issues can overestimate model performance and biased parameters. In this paper, we propose a method that combines (i) the wavelet transform method to pinpoint the correct lane-changing decision point, and (ii) a case–control design to systematically neutralize the dominance of non-lane-changing events in the data. The proposed method is applied to two NGSIM datasets to assess the performance of four representative lane-changing models. Results uncover that (i) lane-changing models are sensitive to various degrees of data imbalance, (ii) regardless of a driver’s decision time window (e.g., 1 s, 2 s, or 3 s), an analysis time window of 6 s will work reasonably well for evaluating the performance of a lane-changing model, while the optimal control-to-case ratio is 1:1; and (iii) when possible, a temporal discretization interval (i.e., an approximation of a driver’s typical decision time window) of 2 s should be preferred, while 3 s should be avoided. The proposed method also enabled us to outline a performance range for the selected lane-changing models.

Stakeholder accountability in the era of big data: an exploratory study of online platform companies

PurposeThis paper aims to examine the nature and extent of disclosure on the use of big data by online platform companies and how these disclosures address and discharge stakeholder accountability.Design/methodology/approachContent analysis of annual reports and data policy documents of 100 online platform companies were used for this study. More specifically, the study develops a comprehensive big data disclosure framework to assess the nature and extent of disclosures provided in corporate reports. This framework also assists in evaluating the effect of the size of the company, industry and country in which they operate on disclosures.FindingsThe analysis reveals that most companies made limited disclosure on how they manage big data. Only two of the 100 online platform companies have provided moderate disclosures on big data related issues. The focus of disclosure by the online platform companies is more on data regulation compliance and privacy protection, but significantly less on the accountability and ethical issues of big data use. More specifically, critical issues, such as stakeholder engagement, breaches of customer information and data reporting and controlling mechanisms are largely overlooked in current disclosures. The analysis confirms that current attention has been predominantly given to powerful stakeholders such as regulators as a result of compliance pressure while the accountability pressure has yet to keep up the pace.Research limitations/implicationsThe study findings may be limited by the use of a new accountability disclosure index and the specific focus on online platform companies.Practical implicationsAlthough big data permeates, the number of users and uses grow and big data use has become more ingrained into society, this study provides evidence that ethical and accountability issues persist, even among the largest online companies. The findings of this study improve the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process, which will help policymakers and standard setters develop future data disclosure policies.Social implicationsFrom these findings, the study improves the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process – which are helpful for policymakers and standard setters to develop data disclosure policies.Originality/valueThis study provides an analysis of ethical and social issues surrounding big data accountability, an emerging but increasingly important area that needs urgent attention and more research. It also adds a new disclosure dimension to the existing accountability literature and provides practical suggestions to balance the interaction between online platform companies and their stakeholders to promote the responsible use of big data.

Strategies to Address Current Challenges in Real-World Evidence Generation in Japan.

The generation of real-world evidence (RWE), which describes patient characteristics or treatment patterns using real-world data (RWD), is rapidly growing more popular as a tool for decision-making in Japan. The aim of this review was to summarize challenges to RWE generation in Japan related to pharmacoepidemiology, and to propose strategies to address some of these challenges. We first focused on data-related issues, including the lack of transparency of RWD sources, linkage across different care settings, definitions of clinical outcomes, and the overall assessment framework of RWD when used for research purposes. Next the study reviewed methodology-related challenges. As lack of design transparency impairs study reproducibility, transparent reporting of study design is critical for stakeholders. For this review, we considered different sources of biases and time-varying confounding, along with potential study design and methodological solutions. Additionally, the implementation of robust assessment of definition uncertainty, misclassification, and unmeasured confounders would enhance RWE credibility in light of RWD source-related limitations, and is being strongly considered by task forces in Japan. Overall, the development of guidance for best practices on data source selection, design transparency, and analytical methods to address different sources of biases and robustness in the process of RWE generation will enhance credibility for stakeholders and local decision-makers.

The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model

PurposeThere is a growing importance of business intelligence systems (BIS) adoption in today’s digital economy age which is characterized by uncertainty and ambiguity considering the magnitude and influence of data-related issues to be solved in contemporary businesses. This study aims to investigate critical success factors that affect business intelligence efficiency based on the DeLone and McLean model in Jordanian banking industry.Design/methodology/approachA quantitative research method through a questionnaire was used to collect data from actual users who depend on business intelligence tools to make operational and strategic decisions in Jordanian banks. The data obtained were tested using the partial least squares–structural equation modeling approach.FindingsThe survey findings attest that system quality, information quality, user quality, user satisfaction and user performance are important factors and contribute to business intelligence efficiency in the Jordanian banking industry.Practical implicationsThe findings gained from this work can help policymakers in Jordanian banks to improve the business intelligence success and organizational performance.Originality/valueTo the best of the authors’ knowledge, this study is the first of its kind to propose a theoretical model to assess drivers of BIS efficiency from the Jordanian banks’ perspective.

The importance of theory at the Information Systems Journal

The importance of theory at the <i>Information Systems Journal</i>

BrightBox — A rough set based technology for diagnosing mistakes of machine learning models

The paper presents a novel approach to investigating mistakes in machine learning model operations. The considered approach is the basis for BrightBox – a diagnostic technology that can be used for analyzing prediction models and identifying model- and data-related issues. The idea is to generate surrogate rough set-based models from data that approximate decisions made by monitored black-box models. Such approximators are used to compute neighborhoods of instances that undergo the diagnostic process — the neighborhoods consist of historical instances that were processed in a similar way by rough set-based models. The diagnostic process is then based on the analysis of mistakes registered in such neighborhoods. The experiments performed on real-world data sets confirm that such analysis can provide us with efficient and valid insights about the reasons for the poor performance of machine learning models.

Machine Learning-Enabled Intelligent Fiber-Optic Communications: Major Obstacles and the Way Forward

Machine learning (ML) has achieved phenomenal success in revolutionizing a number of science and engineering disciplines over the last decade. Naturally, it is also being reckoned as a powerful technology to transform future fiber-optic communication systems. Over the past few years, we have seen extensive research efforts by both academia and industry to assimilate and gain from ML paradigm in several aspects of optical communications and networking. However, despite years of rigorous research, ML has not yet gained broad acceptance in commercial fiber-optic networks. In this article, we identify major common factors which are currently hindering widespread adoption of ML in practical optical networks. As ML-based methods are inherently data driven, we particularly highlight critical data-related issues as well as intrinsic limitations of the ML algorithms. Taking two important use-cases, i.e., quality-of-transmission estimation, and proactive fault detection and management as examples, we elucidate how these limiting factors shrink the deployment prospects of ML-based solutions. We also briefly discuss main challenges faced by ML-assisted methods in seven other key areas of fiber-optic communications. Finally, we suggest some useful strategies that can help alleviate existing obstacles, thus paving the way for vast deployment of ML -powered tools in real optical network infrastructures.

Roadmap Incorporating Data Management Perspective for Platform Business Model Innovation

In recent business environments data has become crucial, especially in the platform business where advances in digital technology continuously increase the platform’s value by facilitating user engagement and transactions that strengthen network effects and enhance the ability to access and analyze vast amounts of data. An important challenge for platform firms is, therefore, to establish a business strategy to address data-related issues for a sustainable competitive edge. However, research is surprisingly sparse to incorporate a data management perspective into a business model roadmap which has been widely used as a strategic management tool. In this paper, we argue that the platform business model roadmap integrating a data management perspective supports platform firms in identifying a change of direction and potential gaps that are aligned with the current and future context in terms of sustainability. The purpose of this paper is to suggest a concept of the platform business model roadmap and identify what structure should be incorporated considering the virtuous cycle of the platform and data. An in-depth discussion of the suggested platform business model roadmap is expected to be of high value in terms of practicality for the platform’s sustainable growth.