ETHICAL CONSIDERATIONS IN THE USE OF BIG DATA FOR BUSINESS ANALYTICS: А CROSS-INDUSTRY PERSPECTIVE

Ethical considerations

Introduction.Previously, companies analyzed relatively small sets of organized data stored in corporate databases.However, technologies are now emerging for collecting and processing streams of unstructured information in real-time.This information can be anything from text to sensor data, and such data is now referred to as big data.
The volumes of data that companies collect and use are enormous, and the speed of incoming data is so high that conventional storage and analytics methods cannot cope with them.For example, 500 hours of video are uploaded to YouTube every minute.New technologies and approaches such as Hadoop, Spark, and NoSQL databases are needed for effective work with such data.In general, big data is a term that denotes massive unorganized data volumes generated rapidly from various sources.The main goal of big data is to derive value and knowledge from data, not just to store it.Ethical questions also arise in the use of such large data sets.
Data security and confidentiality are relevant topics, and this is entirely justified.Hundreds of data leaks occur annually, some of which involve billions of user accounts.When data leaks occur, personal information (such as email address, name, date of birth, phone number, or even physical address) can end up on the internet.Often, this information is sold on black markets or may become publicly accessible.Users typically have no voting rights on this matter.Data breaches have occurred in many internet companies, from Facebook to LinkedIn, and the only thing a user can do to prevent unauthorized use and potential abuse of their data is to limit its exposure to public access.Understandably, this means having to stop using various internet platforms, as refusing to accept their terms and conditions will deprive you of the opportunity to have an account.
Therefore, users today face the dilemma of data storage ethics: should large companies store data on centralized servers that are a prime target for attacks?And what can realistically be done?And, more importantly, how does a company use and protect the data of its users?
Considering the relevance of the work, the purpose of the research is to identify the key ethical aspects of using big data in business analytics, including human rights ethics in the use of big data and the formation of directions for minimizing ethical inconsistency between the collection and utilization of big data.
To achieve the stated goal, it is necessary to determine the main features of applying big data in the field of business analytics and outline the basic ethical issues of using big data, as well as identify ways to address the identified ethical problems.
Review of literature.In recent years, the issue of big data research has seen significant development.The use of modern information technologies, according to O. Vdovychenko and others, opens up new opportunities for business analysts and changes traditional methods of data processing and ways of thinking in human society [1].The use of big data in business analytics, according to the research of H. Mykhaltchenko and M. Tytarenko, is a relatively new, imperfect data protection technology, and the surveillance mechanism is either absent or in an embryonic state [2].
A number of researchers have concluded that the use of big data provides increasingly more opportunities, bringing many conveniences to the process of organizing business analytics in enterprises [3; 4; 5].There is also an opinion that ethical aspects of data development should reflect certain issues related to the use of big data [6; 7; 8; 9].For example, the disclosure of personal information affecting confidentiality, the increase in the use of personal data volumes, and issues of network and device information pollution.These problems require companies to study and assess the development of big data utilization technology from an ethical perspective, based on the common interests of consumers and business owners.The specifications of big data technology and the ethical aspects of their use in business analytics need to be clearly defined to ensure that the development of big data technology brings real benefits.I. Alekseenko, O. Poltinina, and S. Lelyuk note that business analytics is entering a society where data is already collected on a large scale, digital products, services, and infrastructures are predominantly provided by private entities, and leading analysts hold a dominant position in the global internet economy.This context inevitably influences how big data will ultimately be used in society, by whom, and for what purpose [10].
Given the relevance of the topic, it is advisable to conduct a qualitative study based on the analysis of scientific literature by leading researchers, taking into account the most recent perspectives on the issues of using big data in business analytics.
Materials and methods.The primary objective of the article is to explore the ethical challenges and limitations associated with the use of big data in business analytics.It aims to identify and analyze the key ethical issues, providing insights into the implications of using large datasets for business analysis.The article considers the multifaceted aspects of ethics in big data applications and emphasizes the need for comprehensive solutions.
The material for the research was scientific literature, including scientific periodicals by researchers from various countries, ensuring objectivity and diversity of the obtained results.
To obtain results, general scientific methods were used, including the method of analysis to determine the essence and specificity of big data, features of their application in modern business practices, and business analytics.The method of grouping and systematization was employed to identify the areas of application of big data, the comparison method to determine differences between big and small data, and the method of generalization to identify the main sources of collecting big data for use in business analytics.
Results and discussion.Today, the topic of big data is one of the most discussed and relevant.Big data allows not only storing but also managing data that is computed in hundreds of terabytes, organizing and structuring, analyzing, and obtaining qualitatively new information.Indeed, the collection of big data and the ethical issues related to confidentiality and privacy are increasingly coming to the forefront of public awareness.
Since there are no significant innovations in conducting analytics with big data, the scales of data collection today are immensely large, leading to the disheartening conclusion of expanding ethical norms in this matter.Today's developers can connect to extremely diverse and large data resources.Just a few years ago, it was challenging to imagine the possibility of such an asset appearing.The problem is that our ability to identify patterns from obtained data is changing faster than our legal and ethical principles can catch up.Humanity has learned to do what was impossible a few years ago.And if in the era of digitization, the modern individual does not preserve the values that were once dear to them, they risk giving up their significance for the sake of innovation and convenience, losing much of what previous generations created and cherished.
When making decisions about working with large datasets, it is essential to realize that big data comprises extensive sets of diverse data.Extensive because their volumes are such that a regular computer cannot handle their processing, and diverse because these data of various formats are unstructured and contain errors.Big data accumulate and are used rapidly for various purposes.Big data is not an ordinary database, even if it is very large.
The main distinction between big data and small data is presented in Table 1.

Small data
Big data The records database of thousands of corporation employees.The information in such a database has predetermined characteristics and properties, and it can be presented in the form of an Excel spreadsheet Employee action log.For example, all data generated during the operation of a call center with 500 employees Information about the names, age, and marital status of all 2.5 billion Facebook usersthis is just a very large database Click-throughs, sent and received messages, likes and reposts, mouse movements or screen touches of all Facebook users Archive of records from urban surveillance cameras Data from a traffic rules violation video recording system with information about the traffic situation and the license plates of violators; information about subway passengers obtained through a facial recognition system, including those who are wanted Source: developed by the author based on [6; 11; 2; 12].
To store data, data repositories (Data Warehouse) or lakes (Data Lake) are typically organized.Data Warehouse uses the ETL (Extract, Transform, Load) principlefirst comes extraction, then transformation, and finally loading.Data Lake differs with the ELT (Extract, Load, Transform) methodfirst loading, then data transformation.There are three main principles for storing big data: 1. Horizontal scaling: the system should have the ability to expand.If the data volume increases, it is necessary to increase the cluster's capacity by adding servers.
2. Fault tolerance: processing requires significant computational power, increasing the possibility of failures.Big data should be processed continuously in realtime mode.
3. Locality: in clusters, the principle of data locality is appliedprocessing and storage occur on one machine.This approach minimizes the power consumption for information transfer between servers.
Big data, in other words, are structured, partially structured, or unstructured large datasets.Despite its relevance to many fields, the term's boundaries are blurred and may vary depending on the specific task.Nevertheless, there are three main features that received the abbreviation VVV: 1. Volume: the data volume is most often measured in terabytes, petabytes, and even exabytes.There is no exact understanding of when data becomes "big".There are tasks where information occupies less than a terabyte, but due to its non-uniform structure, processing requires the power of a five-server cluster.
2. Velocity: the speed of data growth and processing.A vivid example is new data for analysis appearing with each user session on social networks.Such information streams requires high-speed processing.If one machine is sufficient for data processing, it is not big data.The number of servers in the cluster always exceeds one.
3. Variety: data diversity.Even if there is a lot of information, but it has a clear and straightforward structure, it is not big data.For example, user biographies on social networks are structured and easily analyzable.However, data on reactions to posts or time spent in an application does not have a precise structure.
In recent years, two more characteristics have been added to the VVV perspective: 1. Viability: the viability of data.With a large diversity of data and variables, it is necessary to verify their significance when building a forecasting model.For example, factors predicting a consumer's inclination to make a purchase: mentions of the product on social media, geolocation, product availability, time of day, and the buyer's profile.
2. Value: the value of data.After confirming the viability, big data experts study the interrelation of data.For instance, a service provider may attempt to reduce customer churn by analyzing the duration of calls in the call center.After evaluating additional variables, the predictive model becomes more complex and effective.
Big data is essential in marketing, transportation, automotive manufacturing, healthcare, science, agriculture, and other fields where necessary information can be collected and processed.
Large businesses can leverage such data for the following purposes: 1. Process optimization: for example, major banks use big data to train chatbotsprograms that replace live employees for simple queries and, if needed, transfer to a specialist.
2. Forecasting: by analyzing big data on sales, companies can predict customer behavior and demand for products based on the season or global situations.
3. Model building: through the analysis of income and expenditure data, a company can build a model to forecast revenue.
There are three main professions in big data: data engineer, data scientist, and data analyst.
Data scientists specialize in big data analysis.They seek patterns, build models, and use them to predict future events.For example, a big data researcher might use statistics on cash withdrawals from ATMs to develop a mathematical model for predicting cash demand.This system would advise cash carriers on how much money to bring to a specific ATM and when.To excel in this profession, one needs an understanding of basic mathematical analysis, knowledge of programming languages such as Python or R, and the ability to work with SQL databases.
Data analysts use the same set of tools as data scientists but for different purposes.Their task is to perform descriptive analysis, interpret and present data in an easily understandable form.They process data and produce results by compiling analytical reports, statistics, and forecasts.Other professionals who do not specialize in this field also work with big data: -Interface designers who analyze behavioral research data to create user interfaces; -NLP engineers who develop programs for chatbots and call center automation by analyzing natural language; -Marketing analysts who study data arrays to develop marketing policies and personalize advertising; -Engineers and programmers in enterprises dealing with data processing volume.
A data engineer deals with the technical side of the issue and primarily works with information: organizes its collection, storage, and initial processing.Data engineers assist researchers by creating software and algorithms for task automation.Without such tools, big data would be futile since their volumes are impossible to process.Knowledge of Python and SQL, as well as the ability to work with frameworks, is important for this profession.The analysis of big data allows businesses to systematize information and discover non-obvious cause-and-effect relationships.
The use of big data in modern business analytics has several essential features.Particularly noteworthy is that big data fundamentally differ from traditional structured data.Large data volumes are measured in terabytes, petabytes, or even exabytes.This comes with a constant growth of data generated by users, sensors, and other sources.High-speed real-time data processing or processing with minimal delays is necessary: the processing and analysis process must be scalable and capable of handling data at high speeds.
The diversity of data for analysis arises because they are loaded from various sources such as social networks, Internet of Things sensors, and websites.They can have different naturestexts, images, audio, video, or other indicators.They also come in structured, semi-structured, and unstructured forms.
Ensuring data authenticity is necessary to maintain data integrity and accuracy, as well as to avoid forgery or alteration of data.Consequently, business analysts require sophisticated algorithms and tools, such as machine learning and artificial intelligence, to detect and analyze complex relationships, patterns, or trends, as traditional methods may not be up to the task.
Working with big data involves numerous stages: collection, storage, processing, analysis, and utilization.Different technologies and tools are applied at each stage.Collecting big data is the process of extracting information from various sources and transmitting it to the storage system.Subsequently, business analysts will determine how the collected data sets can be processed and used for the company's development.The main sources of data acquisition are shown in Figure 1.
To collect information from these sources, special programs called crawlers, parsers, scrapers, or collectors are used.These programs can automatically navigate web pages, retrieve the necessary information, convert it into the required format, and
However, during registration on social networks or when purchasing a mobile phone, users usually overlook the fact that their personal data may be collected and processed.When visiting various websites and pages on social networks, or when using various software without consent for data processing, obtaining the necessary information or engaging in communication becomes impossible.This is where the main ethical problem of using big data lies.Therefore, alongside the obvious benefits, big data poses serious risks to individuals and society.

Figure 1. Main sources of collecting big data for use in business analytics
Source: author's own development.
One of the main dangers is privacy violation due to algorithmic imperfections, as well as the collection and analysis of personal information without the explicit consent of the user.Many IT giants like Facebook and Google operate in this manner, which can lead to data leaks or misuse.Sometimes companies intentionally violate user privacy, reselling data to other organizations.However, more often, they simply neglect data protection.
Unlocking the full potential of big data analytics in education requires the existence of a big data ecosystem, within which systems support and facilitate the collection, analysis, exchange, and dissemination of data and information.
Typically, big data is presented as a positive, stimulating factor that helps optimize the activities of entities.However, when discussing the positive effects associated with big data, it is essential to remember potential threats that go beyond personal data leaks and possible fraudulent activities.The issues of freedom and quality of life for individuals constantly under external surveillance, even if they are not aware of the potential use of their data, are rarely addressed in data analysis discussions.
In other words, in the business and consumer sphere, there is a risk that all its subjects will constantly feel under surveillance due to the continuous collection and processing of their data, potentially leading to a reduction in their creative abilities and/or an increase in stress levels.
Human behavior depends on many factors that are impossible to calculate, predict, or neutralize.While experts in the field of big data may argue that such methodologies exist, the potential cyber security problems associated with hacker attacks on data repositories pose another risk for business analysts and company executives using big data for business development.This threatens data loss, distortion, or leakage and, as a result, the loss of the company's positive reputation.
Another ethical challenge facing representatives of the business analytics community using big data is the issue of ethics in the use of big data for indecent, unjust, or harmful purposes: discrimination, manipulation, total control.This violates human rights and freedoms, although sometimes it can provide quick and positive results for company sales.For example, internet data may contain errors, fakes, and spam, leading to distorted analytics and decisions.
Summarizing views on existing ethical issues in the use of big data for business analytics, they can be systematized as follows (Figure 2).
Ensuring the ethical use of big data requires the development and adherence to appropriate rules, regulations, and standards, as well as active participation from all parties: companies, government, research institutions, and consumers.
Currently, the policy system is imperfect, and the oversight mechanism is not unified.The development of big data is inevitably associated with the following problems.In the most general sense, these can be divided into five main types: 1. Human rights ethics.Due to uneven development in countries and regions, people have unequal opportunities for access and use of big data.Human rights ethics emphasizes equality, freedom, and the comprehensive development of individuals, while uneven technological development seriously violates human rights ethics.
2. Moral ethics.The stability of human society depends on the constraints of the moral system.Regarding the ethics of big data, A. Mian and H. Rosenthal [13] links several elements, such as ethics, regulation, innovation, and public participation, as the basis for guiding ethical management of big data for business analytics.At the same time, our moral system constantly evolves with the development of society.Y. Deng et al. notes that it is not easy to judge whether the process of big data analysis should be endowed with ethical characteristics.Therefore, the status of big data in society is a normative issue [14].
3. Information ethics.Information ethics pertains to ethical requirements related to information development, dissemination, information management, and information use.As mentioned by S. Oneshko and L. Paschuk, big data is now the main tool for the
4. Management ethics.Big data provides general insights into the audience but does not allow for the consideration of individual characteristics of each specific consumer or client.
5. Responsibility ethics: the ethical issue of responsibility is the goal, means, and consequences of the comprehensive consideration of the behavior of a responsible subject.This involves a general ethical analysis and investigation of responsibility relations, the distribution of responsibility, and the principle of responsibility in modern society [12].

Figure 2. Generalization of existing ethical problems in the use of big data for business analytics purposes
Source: author's own development.Existing problems and ethical limitations of using big data can and should be addressed, but specific measures need to be taken.This requires coordinated efforts between companies utilizing big data for effective business analytics and the government, which serves as a regulator in the realm of information and data protection.Therefore, resolving ethical issues in the use of big data in business analytics demands a comprehensive approach that encompasses technical, legal, social, and cultural aspects.The main opportunities for addressing ethical problems in the use of big data in business analytics are outlined in Table 2.

Social responsibility
Enhancing the social responsibility of companies in the use of big data.This may involve active participation in addressing social issues and adherence to principles of equality and justice.

Education and training
Increasing awareness among employees, analysts, and managers regarding the ethical aspects of using big data.This can include training and educational programs

Stakeholder involvement
Involving various stakeholders, including representatives of the public, activists, and human rights organizations, in the process of shaping rules for the use of big data.This will contribute to a more extensive discussion and consideration of different perspectives

Audit and control
Conducting regular audits to verify companies' compliance with ethical standards and privacy protection requirements

Legislative measures
Introducing and improving legislative acts that regulate the use of big data.This may include restrictions on the use of certain types of data or the establishment of higher security standards Source: compiled by the author based on [16; 11; 17].
The general approach should aim to strike a balance between utilizing big data for the benefit of both business and society while preserving the privacy and rights of users.
Conclusions.Summarizing, it is worth mentioning that the application of new technologies should be guided and regulated.For the development of secure opportunities in the use of big data in business analytics, it is necessary for relevant national departments to take the initiative, work well on the technology of data collection and application, analyze product ethics, and create moral prerequisites for the use of big data in big business.Against this background, it is important for society to be informed about various issues related to data usage regulation.Not only the state but also other entities should commit to structural investments in collecting and comparing signals about the opportunities and risks associated with the implementation of artificial intelligence in society.Otherwise, its ability to make relevant changes or establish new rulesor do so in a timely mannerwill be severely limited.In addition to regulatory issues, it will be necessary address problems related not only to the technology itself but also to the extent of its use and the scale of its consequences, which will in turn require active management of a broader digital existence, ultimately embedding the toolkit for the application of big data.Undoubtedly, ethical use of big data will enhance trust in companies and strengthen business reputation.However, the problem of finding directions to address ethical issues in the field of big data application for business analytics remains open, as most modern companies are concerned with obtaining economic benefits rather than forming a lasting positive reputation.Accordingly, further research directions may include analyzing reputational risks of using big data, allowing identification of potential costs for protecting customer data and the impact of information obtained through big data processing.
the global network contains a huge amount of information in the form of sites, blogs, mail, chats, videos and other content Internet smartphones and tablets generate data about calls, messages, photos, videos, geolocation, application usage Mobile devices data comes from numerous sensors and sensors that measure temperature, pressure, humidity, light, sound and other physical parameters with high accuracy Various devices provide information about user preferences, tastes, social circle, political views, travel, etc. Social networks provides data from analysis of images, electrocardiograms, tomography for diagnosis and treatment Medical equipment take pictures and maps of the Earth, as well as collect data about other space objects Satellites Journal of Innovations and Sustainability ISSN 2367-8151 2023, Vol. 7, No. 4 https://is-journal.com

Table 2 Addressing ethical issues in the use of big data in business analytics
Development and implementation of clear ethical standards and rules for the use of big data.These standards should define what data can be collected, how it can be used, and how to ensure user privacy in the inter-industrial environment