Big Data Market Research Articles

A Study of Hypertension and Fitness of Individuals with Spinal Cord Injury: A Cross-Sectional Study.

Background/Objectives: Individuals with spinal cord injuries have a higher incidence of chronic conditions such as hypertension and cardiovascular diseases due to a sedentary lifestyle and low levels of physical activity caused by their disability. Additionally, their physical fitness levels are lower compared to those without disabilities. This cross-sectional study aimed to investigate the relationship between hypertension and the fitness of individuals with spinal cord injuries in South Korea while considering differences across sexes and spinal cord injury levels. Methods: This study used data for 835 individuals with spinal cord injuries aged 20-64 years who visited the Korea Paralympic Committee fitness standard test centers from 2018 to 2022, obtained from the Korea Culture Information Sports Association's big data market. The data were analyzed using a series of t-tests, a one-way analysis of variance, a logistic regression analysis, and the four-quartile method. Results: The prevalence of hypertension was 24.4%, and it was different according to the spinal cord injury impairment level. A lower grip strength, a lower arm curl, and a higher body mass index were associated with increased blood pressure. Conclusions: Therefore, a high level of physical strength in people with spinal cord injuries is thought to contribute to lowering blood pressure.

Big Data Education Landscape for Graduates in Morocco: Insights from 2022 Offerings

The main objective of this research was to analyze graduate programs in Big Data in Morocco in 2022, focusing on the technical skills, tools and technologies, and functional skills taught in the programs. In the digital age, the Big Data revolution has generated massive amounts of data, transforming the way businesses and organizations around the world make decisions and innovate. In this ever-changing context, Training of Big Data professionals in Morocco in 2022 is crucial. The expansion of Big Data has created a growing need for skilled professionals to manage these immense data sets. Higher education programs play a key role in preparing these future experts. This study adopted a content analysis methodology, examining the curricula of 16 public engineering schools, and 21 accredited public schools and science faculties in Morocco. The technical skills, tools and technologies, and functional skills taught were analyzed and compared with existing literature on Big Data skills. The programs focus on technical skills such as programming, machine learning, statistics, and mathematics, with Python as the main tool. However, certain skills, such as predictive analysis, are under-represented. Functional skills in communication and management are essential; however, skills in agile methods and change management vary in coverage. The results offer an insight into the alignment between higher education programs and the needs of the Big Data market. Educational institutions can adjust their programs to better meet emerging needs, focusing on under-represented skills, and ensuring the continued relevance of training.

Big data as a source of market power of digital platforms

Big data is widely used by digital platforms in multi-sided markets, which are often considered monopolistic. Big data is analyzed as a potential source of market power of digital platforms, and its characteristics as an economic resource are considered. Arguments for classifying certain categories of big data (for example, historical data) as club goods are specified. The use of big data by digital platforms has already become the object of antitrust proceedings. Based on the analysis of those cases, as well as the review of scientific literature, the relationship between the market power of digital platforms and their use of big data is examined. Firstly, big data can be used by platforms while implementing the strategy of price discrimination. Secondly, the usage of big data creates barriers to entry for new platforms, which are caused by the lack of access to data and increased spending on hardware, software and hiring personnel. Finally, big data can be used by digital platforms to exert market power on adjacent markets and create discriminatory conditions. Based on this research, recommendations for antimonopoly regulation of digital platforms in Russia are provided. Several structural alternatives to antimonopoly regulation of big data are considered: the requirements for anonymized big data disclosure; the creation of a big data market; the partial restrictions on generating and using big data in certain ways (similar to the new regulation in the EU).

Специфіка розвитку ринку BIG DATA для потреб відновлення економіки України в повоєнний період

This article analyses the characteristics and prospects of the global big data market, and iden-tifies the sources of data leakage in the global big data market. A study of the peculiarities of the de-velopment of the big data market in Ukraine in the economic context before and during the war was conducted, which revealed the influence of international institutions in the fight against cyber-attacks in the context of disturbances. The directions of adaptation of the Ukrainian cyberspace to functioning in the conditions of war are identified, and recommendations are made on the directions of implementation of Big Data technologies in the post-war period for the needs of economic growth. The results of the study of the big data market are summarized. Recommendations on the directions of implementation of big data technologies in the post-war period for the needs of economic growth are given. The purpose of the study is to research and analyse trends, development prospects and threats to the big data market at the global level and at the level of the Ukrainian economy during military operations; to develop recommendations on economic directions to implement the protection of per-sonal data in the context of disruptive factors. The results of the study show that the big data market in Ukraine is operating in a disruptive environment, but this does not prevent its development for the benefit of Ukraine's infrastructure and economy. The biggest threat to this infrastructure is cyber-attacks from the Russian Federation. The aim of these actions is to steal data in order to manipulate it and to destabilise the Ukrainian population and its government institutions. The perspectives for further research are the development of strategies for the implementa-tion of big data technologies for the development of Ukraine and the economy in the post-war era.

The Ethical Crossroads of Personal Data Collection

In the present day, we find ourselves immersed in an unprecedented technological revolution, driven by the convergence of artificial intelligence, information and communication technologies (ICT), big data analytics, cloud infrastructure, machine learning, and the Internet of Things (IoT). This transformation fundamentally redefines the interaction with information, services, and decision-making. Transparency emerges as a fundamental principle, demanding authenticity, genuine value, and integrity from brands and companies. We are witnessing a paradigm shift in the relationship between people and technology, from artificial intelligence to the Internet of Things IoT, reshaping the way we live, work, and communicate. Society is undergoing a complete digital transformation, reshaping entire sectors and shaping a new digital economy. Artificial intelligence and ICT are building an interconnected society, altering social dynamics, citizen participation, and the way information is consumed. This rapid introduction of technologies challenges traditional structures, demanding adaptability at both individual and organizational levels. In 2024, ethics in data management becomes a crucial pillar to cultivate trust. As privacy, equity in access to technology, and data security become hot topics, attention focuses on implementing ethical principles such as fairness, confidentiality, clarity, and responsibility. The focus on personalized adaptation, along with data privacy and security, stands out as a priority in 2024. While personalization is sought, transparency in data acquisition and usage becomes imperative for companies. The year is proclaimed as “the year of data,” where how brands use their data will make the difference between thriving and perishing. In terms of general figures and data, the amount of information produced and consumed globally is expected to double by 2025, reaching 97 zettabytes. The strategic relevance of data is highlighted, with 87 % of advertisers considering data their most underutilized asset. The percentage of valuable data with potential for analysis has grown from 22 % to 37 % between 2012 and 2020, consolidating the perception that data is the new gold mine in the digital business realm. The big data market is currently valued at $138,9 billion and is still growing. Industry 4,0 increasingly depends on the adoption of big data and artificial intelligence (AI) technologies, with 48,5 % of organizations currently using data as an engine to drive innovation. In 2024, the need for data and AI to go hand in hand to harness their power is emphasized. However, most organizational structures still face challenges in effectively addressing this paradigm shift. It is anticipated that artificial intelligence and algorithms will play a significant role in determining purchasing choices, political preferences, partner selections, family planning, health management, and medical recommendations soon. Trends for 2024 include the intensification of clean data structure usage, effective implementation of these structures, and an increase in “black box” AI solutions. This refers to AI systems and machine learning models that operate hidden from human understanding, driven by complex mathematical models and high-dimensional datasets. Major technology companies are accelerating their support for AI-driven marketing.

Challenges and prospects in big data analytics: a comprehensive review of developments, hurdles, and future research directions

Big data and business analytics are trends that are positively affecting the business world. This comprehensive review article explores the shifting paradigms and dynamic trends within Big Data Technology (BDT), predominantly for last 5 years, based on an extensive literature review and comparative analysis methodology. It elucidates the transformative influence of big data analytics (BDA) in various sectors, emphasizing the rapid ascendance of cloud computing, Artificial Intelligence (AI) integration, and development of sophisticated analytics tools. The review leverages a wealth of academic literature and market research to underscore the predicted expansion of the big data market. This projected growth indicates the widespread adoption of BDT across industries, with healthcare becoming a significant consumer, motivated by the demand for personalized medicine and improved patient care. The review then navigates emerging trends such as open data usage and ethical concerns surrounding big data, indicating the increasing necessity for stringent guidelines for data use and robust individual data control mechanisms. This is derived from a methodical analysis of recent scholarly articles and industry reports. The article also scrutinizes the evolving definition of "big data" through comparative study of the 3V model and the expanded 7V model in various literature sources, reflecting the evolving nature of data and the unique challenges introduced by modern big data analytics. The review also outlines the challenges for successful implementation of big data projects and highlights the current open research directions of big data analytics. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

Optimal Trading Mechanism Based on Differential Privacy Protection and Stackelberg Game in Big Data Market

Big data has become a fundamental resource and a commodity in economic activities, thus, it is necessary to build a market model capable of supporting efficient data trading. However, two major challenges remain. Firstly, researches have considered constructing data trading mechanisms, while few of them are based on the method of measuring data value in multiple dimensions. Secondly, a data market involved an intermediary trading platform (i.e., a third party) which is honest but curious, results may be obtained due the the leakage of private information. In this paper, we design TM-OUE, a data trading mechanism based on Optimized Unary Encoding that enables reasonable trading mechanism and protects the privacy of data trading. First of all, we combine qualitative and quantitative methods to measure the value of data in multiple dimensions and formulate data trading model between the data provider and data users. Then, we utilize an Optimized Unary Encoding (OUE) protocol to protect the privacy of the data trading mechanism. Based on the above steps, we develop a two-stage single leader multi-follower Stackelberg game to jointly maximize profits of the data provider and data users. Experimental results demonstrate that TM-OUE can offer appropriate price for data and maximize benefits both for data providers and data users, which guarantees fair data trades while protecting privacy.

New Thinking on Artificial Intelligence and Economic and Social Changes Combined with Singularity View

After the concept of "singularity" was put forward, the debate on the possibility of artificial intelligence gaining subject "consciousness" and the threat to the sustainability and subjectivity of human species has intensified. From the historical development of modern artificial intelligence, this paper analyzes the two "contradictory" attitudes of human towards artificial intelligence, and then summarizes and puts forward some new thoughts on this basis. This paper analyzes the positive effects of artificial intelligence on the economy and society, and draws a conclusion that the economy will continue to benefit from the development of artificial intelligence and may reach the economic singularity from the perspective of the continuous expansion of artificial intelligence application scale and AI big data market. This paper argues that there is no sufficient and reliable evidence for the arrival of the singularity that machines possess "subject consciousness", and the value of new thinking brought by this argument is worth paying attention to. For the accompanying challenges to social order, the government needs to adopt reasonable policies, control the scale of artificial intelligence through financial guidance, fiscal expenditure and other means to maximize social welfare, and at the same time, increase redistribution efforts and improve the social security system.

RELEVANCE OF BIG DATA IN BUSINESS

The article presents the survey consisting the and author's study of analysis of the scale and structure of Big Data market worldwide within the period of last decade. The authors do perform the self made and forecast of the Big Data market volume and salary in this field for the coming years based on the provided statistics and Prais–Winsten statistics function. Further the authors do perform an attempt to interpret the results obtained taking into account the Figures of the Big Data market development and draw conclusions based on the results of the study. The authors' conclusions boil down to confirming the hypothesis about the growth of the Big Data market, the growth investments as well as growth of salaries level on this sphere within the future, especially for such countries as the People’s Republic of India and it’s neighbors, as well as the Russian Federation, other countries of the BRICS block. Another main idea is that the salary rate together with the speed of market development are the crucial factors influencing the supplement of the labor force in this segment.

A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.

Depersonalized data: the Russian approach in light of global practices

Data is an important business asset that can be valued and shared for different business purposes. Data about users of services, social platforms, marketplaces, and smart devices owners is used for a variety of purposes: analytics and service improvement, hypothesis testing, and new product development, partnerships, profiling and personalized advertising, machine learning, and the development of new technologies such as IoT. Data is not only used in the usual context of IT giants, but also in such economic sectors as agriculture and industry.1 The amount of data being produced is growing exponentially.2 The commercial turnover of information is expected to grow to an estimated $549 billion by 2028 for the Big Data market alone.3 At the same time, the use of data concerning a natural person falls under the human rights regime aiming to protect the fundamental right to privacy. That is why the data protection regulations historically attempted to reconcile the competing values of the free flow of data and individual’s privacy.4 Nowadays, the pervasive use of large volumes of information which include personal data makes this an even harder endeavour and creates new challenges for data protection legislation, since it is necessary to foster innovation based on the data economy and at the same time face the increasing threats to the privacy of individuals. In theory, de-identification5 which dilutes the link between data and individual can successfully reconcile these goals. For this reason, the processing of depersonalized data justly deserves more freedom than the processing of personal data. Depersonalization has become a widespread practice and in many cases is subject to regulation. However, there is an inverse relationship between the degree of data depersonalization and its value to business, as the more accurate and voluminous the data on people is, the more patterns can be drawn from it.

Big Data Technology Fusion Back Propagation Neural Network in Product Innovation Design Method

With the rapid development of science and technology, the big data market has emerged. In addition to the influence of the modern market environment, more and more enterprises pursue great profits, which requires enterprises to have higher standards for their products. Therefore, product innovation is an inevitable trend. However, product innovation is a comprehensive problem, involving a wide range. Both the characteristics of the product and the consumer should be taken into account. In the design method, it is necessary to pay attention to the interrelation with big data. Firstly, the theory of big data technology is expounded, including the framework and the principle of big data technology, and the classification of data analysis. Secondly, this research explains the process and method of product design, as well as the market-based research process, and proposes the route of using big data technology for product innovation. Finally, the Microsoft COCO: Common Objects in Context (MS-COCO) data set is taken as the research object, and ten groups of data are selected. Through the Back Propagation Neural Network (BPNN) model and neural style transfer (NTS) model, the results of the product innovation design method of big data technology are predicted. Two experiments were carried out, namely, the comparison analysis of the prediction accuracy of BPNN and NTS model; the error analysis of BPNN prediction and NTS model, and the final quality assessment of the two products. The final results reveal that: (1) the prediction accuracy of the BPNN is around 95.71%, and the prediction accuracy of the NTS model is about 85.7%. Therefore, the prediction accuracy of the BPNN is obviously higher than that of the NTS model in the product innovation design method. (2) The mean absolute error (MAE) of the prediction of the BPNN model is about 0.08, and the MAE of the prediction of the NTS model is about 0.11. Thus, the error of the BPNN model in the product innovation design method for product prediction is small. Furthermore, based on the BPNN model, the quality assessment of the two products is finally carried out, and the error rate is 0.11% and 0.12% respectively, which is very small. Thereby, the BPNN model has higher accuracy in predicting the product innovation design methods. The big data technology studied here is of great significance to the product innovation design method and has certain research significance for the intelligent and systematic design of products. Combined with the BPNN model, the accuracy of the prediction ability of product innovation can be guaranteed to a certain extent. This article is protected by copyright. All rights reserved.

Blockchain of Resource-Efficient Anonymity Protection with Watermarking for IoT Big Data Market

According to the ever-growing supply and demand of IoT content, IoT big data in diversified applications are deemed a valuable asset by private and public sectors. Their privacy protection has been a hot research topic. Inspired by previous work on bounded-error-pruned IoT content market, we observe that the anonymity protection with robust watermarking can be developed by further pruning data for better resource-efficient IoT big data without violating the required quality of sensor service or quality of decision-making. In this paper, resource-efficient anonymity protection with watermarking is thus proposed for data consumers and owners of IoT big data market via blockchain. Our proposed scheme can provide the IoT data with privacy protections of both anonymity and ownership in IoT big data market with resource efficiency. The experiments of four different-type IoT datasets with different settings included bounded-errors, sub-stream sizes, watermark lengths, and ratios of data tampering. The performance results demonstrated that our proposed scheme can provide data owners and consumers with ownership and anonymity via watermarking the IoT big data streams for lossless compressibility. Meanwhile, the developed DApp with our proposed scheme on the Ethereum blockchain can help data owners freely share and trade with consumers in convenience with availability, reliability, and security without mutual trust.

Research on Medical Big Data Analysis and Disease Prediction Method Based on Artificial Intelligence.

In recent years, the continuous development of big data, cloud services, Internet+, artificial intelligence, and other technologies has accelerated the improvement of data communication services in the traditional pharmaceutical industry. It plays a leading role in the development of my country's pharmaceutical industry, deepening the reform of the health system, improving the efficiency and quality of medical services, and developing new technologies. In this context, we make the following research and draw the following conclusions: (1) the scale of my country's medical big data market is constantly increasing, and the global medical big data market is also increasing. Compared with the global medical big data market, China's medical big data has grown at a faster rate. From the initial 10.33% in 2015, the proportion has reached 38.7% after 7 years, and the proportion has increased by 28.37%. (2) Generally speaking, urine is mainly slightly acidic, that is, the pH is around 6.0, the normal range is 5.0 to 7.0, and there are also neutral or slightly alkaline. 8 and 7.5 are generally people with some physical problems. In recent years, the pharmaceutical industry has continuously developed technologies such as big data, cloud computing, Internet+, and artificial intelligence by improving data transmission services. As an important strategic resource of the country, the generation of great medical skills and great information is of great significance to the development of my country's pharmaceutical industry and the deepening of the reform of the national medical system. Improve the efficiency and level of medical services, and establish forms and services. Accelerate economic growth. In this sense, we set out to explore.

Dynamic Data Transaction in Crowdsensing Based on Multi-Armed Bandits and Shapley Value

Crowdsensing gradually forms a big data market where workers are willing to trade reusable data with different data collectors. It is challenging for the data collector to choose the transaction party due to the changeable value of the data, while determining the transaction price is also a tough issue. In this paper, we research the dynamic data transaction in crowdsensing. The contribution of the new data to the collector is modeled as the Shapley value, with each worker as a player in the cooperative game. The data collector then judges the contribution of the worker and determines the transaction object. To maximum the profit in the transaction, the collector will dynamically adjust the offering price to workers. The contextual bandit model is utilized in the price decision, with each candidate price as an arm and the time-variant data value as the context. Based on the classic LinUCB learning policy, we learn the mapping of the observed data value and the reward, and estimate the optimal reward in current transaction. The simulation on the data demonstrates that the actual reward got by the collector is close to the maximum reward he can get, which verifies the effectiveness of our scheme.

Development a Data Validation Module to Satisfy the Retention Policy Metric

Every year the size of the global big data market is growing. Analysing these data is essential for good decision-making. Big data technologies lead to a significant cost reduction with use of cloud services, distributed file systems, when there is a need to store large amounts of information. The quality of data analytics is dependent on the quality of the data themselves. This is especially important if the data has a retention policy and migrates from one source to another, increasing the risk of a data loss. Prevention of negative consequences from data migration is achieved through the process of data reconciliation – a comprehensive verification of large amounts of information in order to confirm their consistency.
 This article discusses probabilistic data structures that can be used to solve the problem, and suggests an implementation – data integrity verification module using a Counting Bloom filter. This module is integrated into Apache Airflow to automate its invocation.

The Justification Basis of Personal Data as a New Object of Civil Right

Data is a new object produced with the development of the scientific and technological society, with unique characteristics such as intangibility and non-exclusivity, but data still has the independence and property required by the object of civil right, and is a new type of object of civil right in modern civil law. Analyzing the justification basis of personal data as a new type of civil right is the premise to solve the division of data ownership in the fields of big data market, network security, etc.

Conception about the Data Visualization Techniques Including Data Stream Mining and Bioinformatics

Data visualisation techniques are effectively beneficial to for data streaming in a significant way. Graphical representation is also measured with help of this data visualisation process. Several types of graphs, images, charts and maps are used to maintain data visualisation process. Decisions making process and problem-solving ability is also enhanced with help of these data visualisation procedures. Pie charts, bar charts, waterfall charts, Gantt chart and heat map are used to maintain the data visualisation process. Bullet graphs are also useful for this conception about data visualisation process. Researcher uses “interpretivism” research philosophy, “inductive” research approach and “qualitative” research method to gather information related to this study. Creative and innovative ideas and thoughts related to data are also used with help of this particular data stream mining in a significant way. Data visualisation market value and big data market size is also mentioned in this study. Constant cycles of news and entertainment are also gathered with help of this data visualisation techniques including data stream mining.

BIG DATA В УМОВАХ ІННОВАЦІЙНОГО РОЗВИТКУ ЦИФРОВОЇ ЕКОНОМІКИ

The purpose of the article is to clarify the essence of the digital economy, to determine the degree of its influence on the conditions of activity and management of business entities, to determine the prerequisites and opportunities for adjusting the system of values, priorities and guidelines in order to build new models of management of business entities with the help of BIG DATA. The article examines the content of the categories "digital economy", "digitalization" and BIG DATA, highlights the key components of the digital economy. It has been established that the introduction of digitalization elements by business entities significantly increases the possibilities of new management formats of consumer market entities, affects the degree of their economic development, competitiveness, etc. The difficulties faced by the process of digitization and the development of the digital economy in Ukraine are considered, the country's place in the Global Digital Competitiveness Rating, as well as the indicators of Ukraine's innovative development are examined The positive aspects of the "digital transformation of the economy" are singled out. It is claimed that the rapid development of the digital economy led to the development of new technologies - Big Data, the use of which significantly simplifies the processing of large data sets through the use of digital methods and tools. The main indicators of identifying the dynamics of the development of the global Big Data market (aggregate income of the Big Data market and losses of Big Data market subjects from data leakage) were studied, based on which conclusions can be drawn about its permanent expansion. It is noted that the main goal of the Big Data system is to transform complex data into accessible information to simplify the process of making management decisions in real time. A list of Big Data Analytics software is offered, which is used for effective data processing and achieving a competitive advantage in the market, and its software analytical tools allow you to monitor customer needs, current market changes and other valuable information. Apache Hadoop is highlighted among the most popular analytics tools for working with Big Data; Spark; Cassandra and Talend, their brief technical characteristics are given. Based on the results of the research, the conclusions are formulated that Big Data allows businesses to identify new opportunities and create new types of companies, and to use the information obtained to optimize business processes.

Особливості ідентифікації ризиків ринку BIG DATA

The paper hypothesizes that the dynamic digitalization of the economy, based on the benefits of using Big data, accelerates the use in management and production processes of technologies offered by this market. However, it is noted that the acquisitions of the Big data market also exacerbate the socio- economic contradictions between countries with developed market economies and institutionally underdeveloped countries, which include Ukraine. The authors of the study proposed to identify the Big data market with such key indicators as total revenue, number of Internet users, losses of Big Data market participants from data leakage. Thus, the high rates of development of the Big data market in terms of growth of total market income in 8,03 times during 2011 — 2020 were witnessed. The main segments of the Big Data market (service segment, software segment and service segment) were identified. It is established that the largest share of the Big Data market is occupied by the services segment (37,5% in 2020). From 2021, the growth rate of the software segment is expected to exceed other segments of this market — hardware and services. The results of the analysis of the Big Data market by M. Porter’s five forces model show that the most important of the competitive forces in the market is a high level of competition, in which market participants are encouraged to focus on potential needs and expectations of their customers to strengthen bases of differentiation and clear positioning of the services. According to the results of the SWOT-analysis of the Big Data market, the following strengths were identified: business expansion due to the increase in the amount of information it owns; increasing the number of customer reviews through social networks; establishing strategic partnerships with suppliers, dealers and other stakeholders through the use of Big Data; permanent training of employees to maintain the competitiveness of organizations; established IT system of the enterprise, which promotes faster adoption of effective management decisions; high incomes due to effective management decisions, possession of market research results through Big Data technologies. The identified market opportunities are: population growth, which means an increase in the number of potential consumers and the amount of data collected; growth in the number of enterprises that implement e-commerce in their activities; growth of active consumers due to the integration of Big Data into social networks; increasing the share of automated processes, which helps reduce costs; growing popularity of IT specialization in universities; globalization of the economy, which allows companies to expand their activities to other countries. On the basis of identified strengths of the market and its capabilities, the following strategic directions of development are proposed: entry of enterprises into new markets due to market globalization and effective implementation of Big Data technologies; use of social networks to collect consumer data and involve them in Big Data processes; improvement of the e-commerce system of enterprises due to the capabilities of well-established IT systems of the enterprise with the capabilities of Big Data; reduction of product prices due to cost optimization and effective interaction with contractors. The results of the research allowed to identify the following main risks of the industry: destruction of data confidentiality; collection of false data, infringement of intellectual property of a third party, etc. The formed risk matrix indicates that the most significant risks of this market are the reduction of information security of the entity due to hacker attacks (probability of 50%, significant damage) and the destruction of data confidentiality (probability of 25%, significant damage). Qualitative interpretation of risks in the Big Data market allowed to characterize the impact of adverse factors of internal and external environments, namely: insufficient unreliability of cloud storage for data storage; high level of distrust of data carriers to companies using Big Data; high staff turnover in the market, etc. Assessing the risk of data loss due to hacker attacks allowed to identify it as a risk of a high level of importance (6 p.). Based on the obtained result, it is concluded that the activities of Big Data market participants are vulnerable to possible hacker intrusions and require more effective measures to ensure reliable data protection of enterprises and their customers.