Data-driven Economy Research Articles

US intellectual property law and its impact on business: Recent developments and trends

Recent developments in United States intellectual property (IP) law have significant implications for businesses, shaping innovation, competition, and market dynamics. Intellectual property rights (IPRs), including patents, trademarks, copyrights, and trade secrets, are pivotal to safeguarding innovation and fostering economic growth. Recent legislative and judicial changes reflect an ongoing evolution in IP protections, addressing emerging challenges such as technological advancements, digital transformation, and globalized trade. The America Invents Act (AIA) continues to influence patent filing and litigation strategies, while decisions by the Supreme Court and Federal Circuit courts reshape patent eligibility and fair use doctrines. Businesses face new complexities in balancing protection with innovation, particularly in areas such as artificial intelligence (AI), biotechnology, and e-commerce. Furthermore, the shift toward globalization necessitates alignment with international IP frameworks, compelling businesses to navigate cross-border enforcement challenges and harmonize strategies. The Digital Millennium Copyright Act (DMCA) and evolving interpretations of the Lanham Act highlight the role of US law in adapting to digital disruptions and brand protection in a data-driven economy. Trade secrets are increasingly emphasized, as reflected in the Defend Trade Secrets Act (DTSA), offering enhanced federal protections to combat cyber theft and misappropriation. This review critically examines these legal trends, their business implications, and strategies for compliance and competitive advantage. By exploring case studies and regulatory shifts, it provides insights for business leaders, legal professionals, and policymakers. The study concludes with recommendations for leveraging IP law to foster innovation, mitigate legal risks, and sustain long-term business growth in a competitive landscape.

An Intelligent Approach to Data Quality Management AI-Powered Quality Monitoring in Analytics

This study explores an intelligent approach to data quality management using AI-powered quality monitoring systems. Traditional data quality processes often rely on manual interventions, which are time-consuming, error-prone, and incapable of handling the increasing volume and complexity of modern datasets. AI-driven solutions address these limitations by leveraging machine learning, automation, and anomaly detection to continuously monitor, towards detect inconsistencies, inaccuracies, and missing values across data pipelines. AI-powered systems not only ensure data integrity but also optimize data processing workflows, reducing the need for human oversight. Additionally, automated monitoring provides actionable insights for proactive issue resolution, thereby improving overall data governance and analytical outcomes. This paper highlights the benefits of implementing AI-based quality monitoring, including improved accuracy, scalability, and efficiency in data management processes. The study concludes that AI-powered data quality management is a transformative solution for businesses seeking to maintain high-quality data in a dynamic and complex analytics landscape. Accurate analytics and successful decision-making in today's data-driven economy depend on ensuring data quality. An insightful method for managing data quality using quality monitoring systems driven by AI is presented in this paper. Automated statistics cleansing, anomaly detection, and real-time consistency assurance are three ways in which AI-driven solutions tackle these problems. A.I. improves data correctness, finds mistakes, gets rid of duplicates, and fills in gaps in datasets by using machine learning algorithms, predictive analytics, and anomaly detection methods. In addition, AI systems keep a close eye on data pipelines, offering insights and solutions to prevent data breaches in real time. Data management scalability, efficiency, and dependability are all enhanced by this method, which also decreases the need for human involvement. The results show that analytics systems are much improved with AI-powered data quality management, which helps businesses get better insights and make better choices. We can get high-quality data, optimised processes, and enhanced analytical findings by incorporating AI into data quality frameworks, according to this research

Big data for SMEs: A review of utilization strategies for market analysis and customer insight

This paper provides a comprehensive review of how small and medium-sized enterprises (SMEs) can leverage big data for market analysis and customer insights, offering strategies to enhance competitiveness in today’s data-driven economy. The study explores the role of big data in helping SMEs understand customer behavior, preferences, and trends, enabling them to make informed decisions and improve customer satisfaction. It highlights the practical challenges SMEs face in adopting big data solutions, including limited resources and technical expertise, and suggests scalable, cost-effective strategies for data collection, storage, and analysis. Key utilization strategies discussed include the integration of cloud-based platforms for data management, the use of data analytics tools to process customer feedback, and the application of machine learning algorithms to predict customer behavior. The paper also examines case studies where SMEs successfully implemented big data solutions to achieve significant market insights and improve business outcomes, such as enhanced marketing strategies, personalized customer experiences, and streamlined operations. The review concludes that, despite the challenges, SMEs can effectively harness the power of big data by adopting tailored strategies that align with their specific business needs and resources. By utilizing big data, SMEs can gain a competitive edge, improve customer engagement, and ultimately drive business growth. The paper emphasizes the importance of training, data literacy, and the need for partnerships with technology providers to ensure the successful integration of big data into business processes.

Modernizing Enterprise Development: Harnessing SAP CAPM and OData for Cloud-Native and Microservices Architectures

This article presents a comprehensive architectural approach for building a highly resilient system capable of processing 5-10 billion events daily. In response to the exponential growth of data generation across industries, we explore the design principles and technologies required to handle massive-scale event ingestion, real-time processing, and complex analytics. The article details the implementation of a solution utilizing managed services, microservices architecture, and cloud-native data warehousing, addressing key challenges such as scalability, low latency, fault tolerance, and cost-efficiency. We discuss the critical role of Infrastructure as Code (IaC) in maintaining consistency and enabling rapid scaling. The article also covers robust testing methodologies and performance validation strategies essential for ensuring system reliability at scale. By sharing real-world performance metrics and lessons learned, this article provides valuable insights for organizations aiming to build next-generation data processing systems that can drive real-time decision-making and maintain competitiveness in the data-driven economy.

Data-Driven decision making in agriculture and business: The role of advanced analytics

Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy. Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.

Brain-Computer Interface Integration With Extended Reality (XR): Future, Privacy And Security Outlook

The Brain-Computer Interface (BCI) is a rapidly evolving technology set to revolutionize our perception of the Internet of Things (IoT). BCI facilitates direct communication between the brain and external devices, enabling the control or interaction of devices without physical intervention. BCI technology is becoming more sophisticated, allowing third-party software embedded in emerging technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) to access sensors that read brain activity. These can be grouped under the umbrella term Extended Reality (XR). While BCI technology is disrupting the way data is collected, interpreted, and utilized within IoT networks, it is important to consider the potential privacy and security threats that it poses. Previous and not-so-recent cybersecurity research only scratched the surface in terms of security and privacy aspects of the then-emerging neural and brain-connecting technologies. However, recent advances in reconstructing language, music tracks, and imagery solely based on decoding neural signals pose a significant risk of mental privacy invasion and cybersecurity abuse. In this paper, we present an analysis of the potential threats posed by the integration of BCI with VR, AR, and MR. We analyze the involvement of major technological players in shaping BCI and XR advancements, examining the potential for these technologies to create detailed user profiles and reshape the monetization of user data in the ever-more-aggressive data-driven economy. We also outline a position view on the cybersecurity aspects that are not related to privacy and profiling per se, for example, cybersecurity attacks on the brain (e.g., ``brain rewriting'' attacks) facilitated by potentially vulnerable XR-BCI devices and software. The paper concludes by emphasizing the need for further research on the privacy and security implications of XR-BCI integration and inviting deeper exploration of the topic beyond theoretical papers and toward a more applied experimental setup.

Data-driven strategies for business expansion: Utilizing predictive analytics for enhanced profitability and opportunity identification

In today's hyper-competitive business landscape, leveraging data-driven strategies is paramount for sustainable growth and profitability. This review presents an overview of the imperative role of predictive analytics in facilitating business expansion by enhancing profitability and identifying opportunities. Predictive analytics harnesses historical data and advanced modeling techniques to forecast future trends, enabling businesses to make informed decisions with precision. By understanding predictive analytics, businesses can effectively identify expansion opportunities by analyzing market trends, segmenting customer bases, and uncovering new markets and niches. Moreover, predictive analytics empowers organizations to enhance profitability through optimized pricing strategies, demand forecasting, and personalized marketing initiatives tailored to customer preferences and behaviors. Implementing data-driven strategies for business expansion requires building a robust data infrastructure, selecting appropriate tools and technologies, and fostering a data-driven culture within the organization. Real-world case studies illustrate the transformative impact of predictive analytics on business expansion, offering insights into best practices and lessons learned. Despite the undeniable benefits of predictive analytics, challenges such as data privacy concerns, ensuring data quality, and organizational resistance must be addressed. However, the rewards of embracing data-driven decision-making far outweigh the challenges, positioning businesses for sustainable growth and competitive advantage in the dynamic marketplace. In conclusion, this review emphasizes the critical role of predictive analytics in driving business expansion by enhancing profitability and opportunity identification. By harnessing the power of data, businesses can unlock new avenues for growth, mitigate risks, and stay ahead of the curve in today's data-driven economy.

Research on the Measurement and Mechanism of Data Elements Enabling Breakthrough Innovation of Private Enterprises

This research endeavor holds a pivotal role in elucidating the measurement and underlying mechanisms of data elements, which have emerged as pivotal catalysts for fostering breakthrough innovations in private enterprises. Through a meticulous and rigorous analysis of the intricate utilization and integration patterns of these data elements, we aspire to offer a comprehensive and nuanced understanding of how private firms can harness the immense power of data to foster disruptive and transformative innovations. Our study focuses on identifying the crucial factors that underpin the successful execution of data-driven strategies. These strategies augment enterprises' innovative prowess and propel them to the forefront of their respective industries. The insights gained from our research transcend theoretical frameworks, offering practical strategies and profound understandings that serve as a compass for private enterprises navigating today's rapidly evolving, data-driven economy. By harnessing these insights, private enterprises can maintain their competitive edge, seizing growth opportunities in a fiercely competitive environment. Ultimately, this research holds tremendous potential in shaping the future of private enterprises, empowering them to leverage data as a strategic asset and transform their businesses for enduring growth and prosperity.

AI 거버넌스의 정의와 쟁점 및 발전 과제: 비판적 관점

In the data-driven economy, AI algorithms will eventually drive power interests and value allocation of scarce resources in terms of who gets what, when, and how. However, to date, the decision-making process and internal mechanisms of algorithm-based AI remain “a black box”. Therefore, a critical systematic analysis of the benefits and limitations of AI’s practical utility and applicability, as well as legal and institutional promotion and control measures to enhance transparency in algorithm design, is needed now more than ever. The current structural situation of human oppression, control, and domination by AI power in the name of techno-engineering-based social innovation and development in the age of digital capitalism requires a multidisciplinary approach to governance that utilizes political science, educational politics, and critical theory perspectives. This paper aims to comprehensively review the emerging AI governance definitions, issues, and development plans from critical perspectives.

Big Data Analytics: Techniques, Tools, and Applications in Various Industries


 
 
 Big data analytics has emerged as a powerful tool for extracting valuable insights from large and complex datasets across diverse industries. This article provides a comprehensive review of the techniques, tools, and applications of big data analytics in various industries. Through qualitative analysis and extensive literature review, this study explores the key methods and technologies used in processing and analyzing massive volumes of data to uncover hidden patterns, trends, and correlations. The article discusses the significance of big data analytics in enhancing decision-making processes, optimizing operations, and gaining competitive advantages in today's data-driven economy. Furthermore, it examines the challenges and opportunities associated with implementing big data analytics solutions across different sectors. By examining real-world case studies and industry examples, this research highlights the practical applications of big data analytics in areas such as healthcare, finance, manufacturing, retail, and marketing. The findings underscore the transformative impact of big data analytics on improving business performance, customer engagement, and innovation across diverse domains. Overall, this article aims to contribute to the understanding of big data analytics techniques and their wide-ranging applications, providing insights for researchers, practitioners, and decision-makers in leveraging data-driven approaches to drive organizational success.
 
 

Official statistics and big data in Latin America: Data enclosures and counter-movements

With the rise of the data-driven economy, the state-owned sector of official statistics has been pressurised to ‘modernise’ and engage with big data. However, most of these data sources are controlled by tech corporations. We address the conflicts this brings about from a Latin American perspective through three case studies involving national statistical offices, international organisations, and the private sector. We investigate strategies for enabling data markets for official statistics and analyse how the statistical field has acted in this context. In doing so, we contribute to understanding the political economy of big data in Latin America and debates on how digitalisation encompasses the reshaping of state-business relations. Supported by secondary data and semi-structured interviews, we appraise Bourdieu's theory of fields, Marxian readings on the enclosure of commons, and Polanyi's double movement to analyse how data commodification challenges data as public goods – a fundamental principle for official statistics. The findings demonstrate that data enclosures have prevented the state's access to compiling official statistics and show that the initiatives for introducing big data in Latin American national statistical offices have involved testing data markets through public–private partnerships supported by international organisations and big tech. As a result, the statistical field has reacted to the data market with a ‘double movement’: mobilising symbolic capital such as ‘trust’ for partnering with businesses to access data and technologies and, conversely, defending the public value of data in counter-movements protective of the relative autonomy of the national statistical offices and the state's control over informational capital.

The Investment Canada Act: Updates for the Knowledge-Based and Data-Driven Economy at the Dawn of the Age of Machine Knowledge Capital

The Investment Canada Act: Updates for the Knowledge-Based and Data-Driven Economy at the Dawn of the Age of Machine Knowledge Capital

ENHANCING EMPLOYEE EFFICIENCY AND PERFORMANCE IN INDUSTRY 5.0 ORGANIZATIONS THROUGH ARTIFICIAL INTELLIGENCE INTEGRATION

The paper explores the transformative effects of Industry 5.0, a data-driven economy, and the imperative adoption of Artificial Intelligenc (AI) driven systems across all organizational levels. It emphasizes the urgent need for comprehensive transformation, extending to Human Resource Management (HRM). Industry 5.0 presents significant challenges, necessitating strategic HRM strategies involving skill enhancements and AI-assisted knowledge management. A central focus is the profound impact of AI-driven innovations on organizational efficiency, employee efficacy, and productivity. AI rapidly acquires and processes data, providing organizations with prescriptive and predictive insights, enabling them to navigate potential future scenarios effectively. These insights can be leveraged to enhance productivity, engage employees, and facilitate organizational growth. Beyond HRM, the paper recognizes AI's influence on marketing and sales strategies in Industry 5.0. AI-driven advancements revolutionize customer engagement and personalization. AI-powered chatbots, for example, offer tailored interactions that elevate customer satisfaction and engagement. AI's data analytics capabilities empower businesses to craft highly targeted marketing campaigns, enhancing service quality and response times, yielding favorable marketing and sales outcomes. Furthermore, AI-driven Chatbot tools such as Chorus and GrowthBot play a pivotal role in recording and analyzing sales conversations, offering valuable insights into customer behavior. This informs informed decision-making and accelerates lead conversion. AI's rapid data processing capabilities enable organizations to refine marketing and sales strategies, boosting their effectiveness and financial results In summary, the research highlights AI's transformative impact across organizational facets, including HRM, marketing, and sales. Embracing AI-driven processes and systems offers a competitive edge by enhancing employee productivity and revolutionizing customer engagement and sales strategies. This comprehensive approach is crucial for thriving in the data-driven landscape of Industry 5.0, where harnessing the power of AI is paramount for success.

Intellectual property and fourth industrial revolution technologies: how the patent system is shaping the future in the data-driven economy

Intellectual property and fourth industrial revolution technologies: how the patent system is shaping the future in the data-driven economy

Integrating Block chain Technology to Harness Big Data: Methods, Obstacles, and Future Outlook

The distributed and secure nature of blockchain technology makes it an effective tool for combating corruption, increasing transparency, and streamlining processes across fields like healthcare, government, and finance. This research demonstrates that blockchain technology can revolutionize government transparency, financial management, and corporate structures. In nations with high corruption indices, combining blockchain with forensic accounting significantly enhances the openness and accountability of anti-corruption efforts. System integration challenges, legal constraints, a shortage of skilled professionals, and scalability issues hinder blockchain adoption from reaching its potential. Overcoming these obstacles requires further research, legal frameworks, and cross-sector collaboration. To enhance decision-making in a data-driven economy and maximize blockchain's potential, a governance model that integrates big data analytics with blockchain could enable secure, transparent, and scalable adoption

데이터베이스 이용제한 행위에 대한 경쟁법 적용

In a data-driven economy, companies (esp. platform companies) with large amounts of data can use it to gain competitive advantages, such as product and service innovation and improvements. However, when big data is concentrated in the hands of a few companies, new entrants may find it difficult to enter the market or, if they do, to compete. It is legal for companies to collect significant amounts of data and to manage and control it. However, the issue of misuse of data to increase entry costs and extend market power is being debated in various countries around the world on the need for regulation at the competition law. This study will introduce the Canadian Competition Bureau's Intellectual Property Enforcement Guidelines (the “IP Guidelines”) and the case of the Toronto Real Estate Board's (the “TREB”) restrictions on the use of a real estate database in the Toronto area, which addressed whether the restrictions constituted an abuse of market power under Canadian competition law(the “Case”).
 The case has been a long-running legal battle that began in 2011 with an investigation by the Canadian Competition Bureau and concluded in 2018 with a decision by the Supreme Court of Canada. As such, it is considered to be a precedent-setting case in the context of determining abuses of market power in relation to database restrictions. Recently, the Korea Fair Trade Commission also imposed a corrective order and a fine on NAVER for the act of preventing a third party (a competitor such as Kakao) from providing real estate listing information provided by NAVER through a contract with a real estate information provider. The case contains many similar legal issues to the NAVER case and is worthy of comparison and analysis. This study draws implications for Korean competition law enforcement through foreign regulations and case studies on platform operators' data access restriction.

Regulatory Issues of Data and Algorithms for the Data-Driven Economy

Abstract Data and algorithms are the lifeblood of the data-driven economy. Big data and big algo are bringing new challenges and legal issues to the fore. This article deals with some aspects of these issues and tries to put forward an analytical framework for typifying, securing access to and use of data, and ultimately maximizing the generation and flow of data. This article then deals with algorithms and endeavours to propose six principles for auditing algorithms and a brand new international governance framework for algorithms and their auditing via a treaty and relevant mechanisms.

As startups na era da proteção de dados

This paper develops a brief study about the General Law for the Protection of Personal Data - LGPD, Law nº 13.709/2018, and the impact of this legislation on startups. The objective is to verify if this business model will be able to benefit from the data protection regulations or if it will face a great challenge. The study considers the current moment: the Era of the Data-Driven Economy. Information has become a major source of productivity and power, in view of the increase in new technologies, as well as their consequences in legal transactions carried out between individuals. It is essential to be careful with the use of public personal data, which are easily found and obtained from the most diverse sources and are seen by startups as important assets, playing a relevant role in the economy, since they can be used as a way to profit from of information from individuals or profiles traced through a set of data. The paper points out that the great challenge for startups is the creation of a new business culture, concerned with ethics in the processing of personal data. In order to obtain results and conclusions, an integrated, analytical, deductive research methodology and a bibliographical research technique are used.

Driving Performance Improvement of an Organization through Data Object Fusion

To succeed in today's data-driven economy, organizations must find ways to put their massive data stores to work competitively. This research delves into the possibility of using data object fusion techniques and, more significantly, consensus clustering to boost the efficiency of businesses in an area of expertise. A case investigation of the automotive service sector demonstrates potential results and puts theoretical knowledge into practice within an organization. Therefore, this study addresses the prospective benefits of data object fusion in the automotive service sector. Furthermore, by combining the findings of different clustering methods, consensus clustering can provide a more precise and reliable outcome. Moreover, a consistent representation of the data objects is obtained by applying this technique to disparate datasets acquired from different sources inside the organization, which improves decision-making and productivity in operations. The research highlights the significance of data quality and the selection of proper clustering techniques to achieve dependable and accurate data object fusion. The findings add to the expanding knowledge of using data-driven ways to enhance organizational performance in any emerging sector.

The Interplay Between the Essential Facility Doctrine and the Digital Markets Act: Implications to Big Data

Today many companies are collecting and extracting data from different sources to help them with their strategic decision-making. Big data is the basis of data-driven economy, bringing significant competitive advantage and market power to companies who are able to harness and exploit its potential. Digital transformation of markets and economy challenges the existing structures of consumer protection, data protection and competition law. Data is a commodity as well as a strategic asset. The term Big data refers to the amount of data that cannot be processed in a short time by traditional informatics devices. Undertakings possessing a large scale of different data have a competitive advantage. Possible application of the essential facility doctrine to Big data issues has not attracted much attention in competition assessment. This paper will try to fill the gap by providing some insights into competition and data issues. Also, the question whether data can be considered under the essential facility doctrine will be analysed. Furthermore, it will be shown that essential facility criteria are applicable, although there is room for some adjustments to data markets. The last part of the paper will scrutinize the Digital Markets Act that tries to shed some light and clear some possible problematic behaviour of the so-called gatekeepers. The regulation leaves the conventional approach and shortens the process of tackling possible anti-competition concerns. It regulates only those undertakings that have significant impact on market and the possibility to become an important gateway in the future. When the status of a gatekeeper is established in accordance with all prescribed criteria, there will be no need to show that the elements of the essential facility doctrine are fulfilled. The essential facility doctrine will still be relevant to undertakings that are not designated as gatekeepers.