Data Assets Research Articles

Improved Watermarking Performance in Color Images through a hybrid of DWT-DCT Integration for Copyright Protection

The era of big data has changed the view of data, data can now be considered as data assets. The definition of data assets is data that has rights (exploration rights, use rights, and ownership rights). Some experts believe that data assets have value at their core, the value contained can be in the form of information owned by data assets. Sharing digital data is growing every day as more people access the Internet quickly. Unauthorized individuals can easily access multimedia, such as text, images, video and audio. This research focuses on digital image watermarking to ensure security and copyright protection. This scheme uses a watermarking technique based on the hybrid of the two transformation domains (frequency domain), Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). The digital image is processed into several parts by the DWT, then the watermark is embedded into one of the parts in the frequency domain using the DCT transformation, then the parts are combined again in the DWT. The experimental results show that the watermarked images achieve the highest PSNR result was 45.2719 dB, and the lowest was 43.3194 dB. an average PSNR value of 44.1254 dB, by testing ten color image datasets sourced from the SIPI-USC image database.

Data assets and corporate sustainable development: evidence from ESG in China

This paper studies the impact of data assets generated inside firms on their Environmental, Social, and Governance (ESG) performance. Using a sample of listed firms in China from 2010 to 2020, we find that firms that use data assets have better ESG performance. Further analyses show that data assets improve ESG performance by enhancing operational capacity and improving monitoring. Furthermore, the impact of data assets on ESG performance is more pronounced for firms with lower asset turnover rates, weaker market power, and higher agency costs, and in scenarios where mandatory ESG disclosure is absent and CEOs have IT-related backgrounds. This paper enriches the literature by shedding light on the role of data assets in sustainable development and corporate social value creation and has practical implications for data governance in the digital era.

Innovating supply chain management with blockchain applications

Innovating supply chain management with blockchain applications

Assessing the Adoption of Blockchain Technology in Smart Sustainable Cities: Insights from Construction Practitioners in Malaysia

ABSTRACT In response to rapid urbanization and associated challenges, city planners worldwide are increasingly turning to the development of smart sustainable cities (SSC). Blockchain technology has emerged as a disruptive force with the potential to facilitate this transformation. While existing research has explored the concept, features, and applications of blockchain within SSC, limited attention has been given to the perspectives of construction practitioners regarding its adoption. This study seeks to address this gap by assessing the acceptance level of construction practitioners toward blockchain technology in SSC. Through the distribution of questionnaires to construction practitioners, comprising 153 responses, this research reveals a generally high level of acceptability toward blockchain technology in SSC, with notable exceptions in property or asset transactions and data management. Furthermore, significant disparities in acceptance were observed across different age groups and educational backgrounds, with older generations and those with lower levels of education demonstrating lower levels of acceptance. These findings provide valuable insights for city planners and relevant authorities, enabling them to design informed strategies and roadmaps to ensure the successful integration of blockchain technology in SSC by considering the acceptance levels of construction practitioners.

Analysis of Hotspots and Trends in Domestic and International Data Asset Research: A Knowledge Graph Analysis Based on CiteSpace

The article summarized 363 literature on data assets in the CNKI and WOS databases and conducted a knowledge map analysis using CiteSpace. The research found that the study of data assets at home and abroad is continuously growing, but there are differences in origins, content, and hotspots. Foreign research focuses on areas such as big data, data governance, machine learning, and the Internet, while domestic research focuses on data assets, the digital economy, data transactions, and accounting. Domestic research is gradually expanding to include data middle platforms and asset confirmation, while foreign research focuses on the Internet and artificial intelligence. This analysis helps grasp the research trends and hotspots in the field of data assets, promoting cooperation and knowledge sharing.

ESTIMATES OF CONSTRUCTION INFRASTRUCTURE STOCK FOR CAPE VERDE: 1980–2019

Building and other construction assets constitute a significant part of a country’s physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries’ specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980–2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country’s construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development.

Visualisasi Data Aset Tidak Bergerak Menggunakan Looker Studio Pada PT XYZ

PT XYZ is a company engaged in the electronics industry. This company has several divisions and units, one of which is the Project Deployment Center (PDC) division has a unit called Project Operation-Commercial Sector (POCS). The task of this unit is to meet the need for information and materials needed for project operations in accordance with the approved plan and to monitor and evaluate all commercial sector project activities. In carrying out project operations, especially related to monitoring, this unit only displays data in the form of google spreadsheets, so project managers and teams have difficulty in monitoring the data of their immovable assets. To solve this problem, it is necessary to visualize data in the form of a monitoring dashboard. This monitoring dashboard includes registration, borrowing, returns and calibration. Registration is required to register assets, borrowing and return transactions are intended if the assets are borrowed and returned, calibration is intended if the assets will be calibrated. This monitoring dashboard uses Looker Studio which functions as a data visualization medium in the form of a monitoring dashboard. The result of this work is to display a monitoring dashboard that has an attractive and informative appearance so that with the dashboard, it is hoped that it can help project managers and teams in monitoring asset data easily and quickly.

Does ESG performance affect the systemic risk sensitivity? Empirical evidence from Chinese listed companies

PurposeThis paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.Design/methodology/approachSince Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.FindingsThe study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.Practical implicationsThe study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.Originality/valueThe paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

The Transformative Impact of AI in Finance and Banking

The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

Emerging Cyber Security and Data Privacy Threats: Challenges and Opportunities: An Analytical Overview

ABSTRACT: In the digitally emerging world where everyone has the access to internet it is very crucial to secure our data because of right to privacy. Cyber security deals with all the laws designed for the cybercrimes. Digitalization and networking provide number of benefits to us in different fields like e-commerce, banking, education, communication but due to cybercrime, a new criminal methodology also arises. Systems, important files, data, and other important virtual things are at risk if there is no security to protect it. The need of the hour is to make people aware of the laws that already have been made and to amend the laws according to the development of technology. The objective of this paper is to spread awareness about the laws and the precautions which we can take to secure our precious data and belongings. With the advent of various types of technology and availability of such technology at lesser cost, every human being around the world has fallen trap to use technology in day-to-day life. From ordering food online to shopping clothes, books and other necessary items to booking tickets, appointments with doctors etc., to digital payments, every aspect of one’s life has become dependent on technology. To put it in simple words, life has become dependent on two essential things – (i) smart phone; and (ii) internet access. However, it is important to note that the success of such technology is inter-alia dependent on the availability of data/information that it collects and/or collected for it. Thereby, data has definitely become the ‘new oil’ since availability of data, processing it and utilizing it in formulating a perfect algorithm for technology has become very expensive for companies providing digital services. Cyber security is a set of strategies, techniques, and controls to reduce risk and ensure that your data assets are protected. If data privacy is about control, then cyber security has the means to add, some, but not all, of the aspects of that control. Cyber security is at the heart of the discipline of data protection.

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

TagTaste: From transformation to disruption

Evolving consumer preferences challenge dominant business logic across industries in this digital era. The dominant logic in the Food and Beverage (F&B) industry, which exploits legacy products with minor continuous improvements, is being challenged. Incumbents must pursue frequent F&B product innovation to satisfy evolving consumer preferences. However, their business models constrain the capability and agility for frequent innovation. This case study documents TagTaste’s journey in transforming the F&B industry by addressing the constraints. It developed a platform ecosystem, data-centric assets, and frameworks to undertake frequent F&B product innovation. Its novel sensory data assets and frameworks translate an F&B product into data points for assessing consumers’ product perception. Through the integration of data-centric assets and frameworks into its platform ecosystem, it has developed a novel Food and Beverage Knowledge as a Service (FBKaaS) model to foster frequent F&B product innovation. After successfully transforming F&B product innovation through FBKaaS, it aims to disrupt the F&B industry by scaling up through two key initiatives. Firstly, it aims to democratize F&B product recognition, and secondly, it aims to enhance operational efficiency through algorithmic management. This case provides insight into digital transformation, data analytics, platform ecosystem, disruption, and F&B innovation.

Blockchain Opportunities for Water Resources Management: A Comprehensive Review

Blockchain technology has been used for the digitalization of physical asset management and data management processes in many areas in the industry and academia, including the water domain. Its potential as an immutable data storage system and smart contract integration has provided a plethora of use cases and utility in the domain of hydrology and water resources. This systematic review critically examines the application of blockchain technology in the field of water resources and hydrology. By analyzing 104 academic publications and 37 non-academic studies from 2017 to 15 October 2022, this paper identifies the current state of blockchain applications in water management, delineates their potential use cases, and assesses their practical utility and scalability. Despite the theoretical promise of blockchain for enhancing water governance, data security, and stakeholder trust, the review reveals a noticeable gap between theoretical potential and the existence of workable, real-time applications specifically in water resources management. The findings indicate that while blockchain technology has been effectively implemented in various sectors, its adoption in hydrological domain is still emerging, with limited empirical evidence to support full-scale deployment. The paper concludes with a call for more empirical research to validate theoretical benefits, address scalability and interoperability challenges, and integrate blockchain technology with real-time data networks for sustainable water management practices.

Hardware Asset Recording Information System Based on Web at PT. Smart Connect

PT. Smart Connect is an IT company that focuses on the design, development, and maintenance of LAN and WAN networks, network routers, and websites. The company has many assets in the form of computer network devices that need to be recorded meticulously. Currently, asset recording is still done manually using Google Spreadsheet, which is considered less effective and time-consuming. In the rapidly evolving digital era, the utilization of technology and information systems has become crucial. The proper use of information systems can enhance efficiency in managing asset data and facilitate information access. Therefore, PT. Smart Connect requires further improvement in its information system for asset recording and reporting. The goal is to expedite the asset recording process and provide more detailed reports, thereby increasing efficiency and productivity in company asset management.

A road data assets revenue allocation model based on a modified Shapley value approach considering contribution evaluation

This paper constructs a two-layer road data asset revenue allocation model based on a modified Shapley value approach. The first layer allocates revenue to three roles in the data value realization process: the original data collectors, the data processors, and the data product producers. It fully considers and appropriately adjusts the revenue allocation to each role based on data risk factors. The second layer determines the correction factors for different roles to distribute revenue among the participants within those roles. Finally, the revenue values of the participants within each role are synthesized to obtain a consolidated revenue distribution for each participant. Compared to the traditional Shapley value method, this model establishes a revenue allocation evaluation index system, uses entropy weighting and rough set theory to determine the weights, and adopts a fuzzy comprehensive evaluation and numerical analysis to assess the degree of contribution of participants. It fully accounts for differences in both the qualitative and quantitative contributions of participants, enabling a fairer and more reasonable distribution of revenues. This study provides new perspectives and methodologies for the benefit distribution mechanism in road data assets, which aid in promoting the market-based use of road data assets, and it serves as an important reference for the application of data assetization in the road transportation industry.

Analyzing Depreciation: A Financial Accounting Data Analytics and Visualization Case

ABSTRACT This case uses a financial accounting context to help instructors incorporate data analytics in the accounting curriculum. The case’s flexible design allows it to be used in a data analytics or financial accounting course for instructors with little to no data analytics experience. For an analytics course, students extract, transform, and load fixed asset and depreciation data from a company’s five sites. Students will then validate the accuracy and completeness of the data by creating visualizations. The case can be accomplished using any data analytics tool; Teaching Notes are presented using Power BI. The case and Teaching Notes include modifications for a financial accounting course where students are given data visualizations to identify depreciation anomalies (where no analytics software is required). An extension for audit courses is also discussed. The case serves to develop students’ analytics skills while simultaneously reinforcing their understanding of depreciation concepts.

PROSES PENGAKUAN DAN PENGUKURAN ASET TAK BERWUJUD DATA DAN INFORMASI GEOSPASIAL: KELEMAHAN DAN KENDALA

The purpose of this study is to analyze the implementation and constraints and provide recommendations in the process of recognition and measurement of intangible assets of basic geospatial data and information. This research uses a qualitative method through a case study at the Geospatial Information Agency. Data collection methods were conducted by document analysis, observation, and in-depth interviews with policy implementers and related management. The results showed that there are implementation weaknesses and obstacles in the process of recognizing and measuring intangible assets in the form of basic geospatial data and information. Weaknesses occurred in implementation including the budgeting process, budget execution, and reporting. Implementation weaknesses that occurred included the absence of involvement of the Government Internal Supervisory Apparatus in budget revisions, the absence of standardized submission forms, corrections in the reporting section that were not communicated to the budgeting and implementation sections, and capital expenditure returns that had not been integrated with the asset module. In addition, obstacles also occur, including communication factors, resources, dispositions, and bureaucratic structures that occur simultaneously with each other. This research provides recommendations for guideline revisions and practical mechanisms to properly implement the guidelines in the process of recognizing and measuring intangible assets in the form of basic geospatial data and information.

Data Sovereignty in Information Systems

Data has become a strategic asset for societal prosperity and economic competitiveness. There has long been an academic consensus that the value of data unfolds during its use. Consequently, many stakeholders have called for expanding the use and reuse of data, including the public and open variety, as well as that from private data providers. However, citizens and organizations want self-determination over their data use, that is, data sovereignty. This fundamentals paper applies a literature review to conceptualize the term in Information Systems (IS) research by summarizing current findings and definitions to add further structure to the field. It contributes to the current research streams by introducing a core conceptual model consisting of seven interacting core aspects, involving trust between data providers and consumers for data assets, supported by data infrastructure and contractual agreements on all data lifecycle stages. We evaluate and discuss this conceptual model through recent field examples and provide an overview of future research opportunities.

Supporting Asset Management with GIS and Business Intelligence Technologies: The Case Study of the University of Turin

Despite the promising outcomes achieved over time in Asset Management, data accessibility, correlation, analysis, and visualization still represent challenges. The integration, readability, and interpretation of heterogeneous information by different stakeholders is a further concern, especially at the urban scale, where spatial data integration is required to correlate virtual information with the real world. The Geographic Information System (GIS) allows these connections, representing and digitizing extensive areas with significant benefits for asset analysis, management, and decision-making processes. Such benefits are central for managing large and widespread university campuses as they are comparable to small cities, covering a wide urban region and including resources highly integrated into the urban context. The paper presents how GIS integrated into Business Intelligence (BI) tools can support university Asset Management System (AMS) creation for the optimal use of resources, illustrating the University of Turin case study. The results discussion considers the relationship between the different elements of the assets and their synergy with the city. It focuses on four themes, dealing with the asset identification of buildings and resources, especially the educational ones, asset spatiotemporal evolution, and buildings’ distances for proximity analysis. The benefits achievable through the AMS, related challenges, and possible future developments are highlighted.

High-resolution digital outcrop model of the faults, fractures, and stratigraphy of the Agardhfjellet Formation cap rock shales at Konusdalen West, central Spitsbergen

Abstract. Structure-from-motion (SfM) photogrammetry has become an important tool for the digitalisation of outcrops as digital outcrop models (DOMs). DOMs facilitate the mapping of stratigraphy and discontinuous structures like folds, faults, and fractures from the centimetre to kilometre scale. With pristine, treeless exposures, the outcropping strata in Svalbard, Arctic Norway, hold exceptional potential for analogue studies and are ideally suited for the acquisition of high-resolution DOMs. Here, we present the acquisition, processing, and integration of the Konusdalen West digital model data set, comprising both DOM and derived digital terrain model (DTM) data. Drone-based image acquisition took place over 2 weeks in July and August 2020. The Konusdalen West DOM and DTM cover a 0.12 km2 area and span a 170 m elevation difference. The DOM covers the upper two-thirds of the mudstone-dominated Late Jurassic–Early Cretaceous Agardhfjellet Formation. The Agardhfjellet Formation and its time equivalents are regional cap rocks for CO2 sequestration and petroleum accumulations on the Norwegian Continental Shelf. A total of 15 differential GNSS control points were used to georeference and quality assure the digital data assets, 5 of which function as reference checkpoints. SfM processing of 5512 acquired images resulted in high-confidence, centimetre-scale resolution point clouds, textured mesh (DOM), tiled model, orthomosaics, and a DTM. The confidence-filtered dense cloud features an average inter-point distance of 1.57 cm and has an average point density of 3824.9 points per metre. The five checkpoints feature root mean square errors of 2.0 cm in X, 1.3 cm in Y, 5.2 cm in Z, and 5.7 cm in XYZ. Increased confidences and densities are present along the western flank of the Konusdalen West outcrop, where a fault fracture network in mudstone-dominated strata is best exposed and photographed most extensively. Top and side view orthomosaics feature maximum resolutions of 8 mm per pixel, enabling the mapping of faults, formation members, marker beds, fractures, and other sub-centimetre features. Additional structural measurements and observations were taken in June 2021 to place the data in the geological context. Data described in this paper can be accessed at Norstore under https://doi.org/10.11582/2022.00027 (Betlem, 2022b).