Methods and Means of Legal Regulation of Relations in

Entrepreneurship: Novels of Digitalization

Olga V. Sushkova

Kutafin Moscow State Law University, Moscow, Russia

Keywords: Business and Professional Activities, Blockchain Technologies, Big Data Analytics, Crowdsourcing,

Innovative Business Models, Banking.

Abstract:

The use of blockchain technology is often used in business and professional activities because of the

decentralization of user data and consensus building through a public network of participants to ensure the

accuracy of information. It is important to evaluate the fundamental characteristics of such a technology:

transparency, security, decentralization and immutability of transactions. Businesses around the world are

experimenting with the scalability of blockchain technology, so it is necessary to legally qualify the

technology for systemic application and establish common standards and rules, technical capabilities, asset

digitization, and the application of a self-regulatory mechanism. Foreign scientists Kumar V., Ramachandran

D., Kumar B. suggest that blockchain technology must pass three tests: a test of decentralization (i.e., political,

architectural, commercial and contractual), a test of cryptoassets, and a test of a business model. Cloud

computing has undergone significant changes, but the standards and interoperability issues of this platform

are now becoming apparent. Scientists Kathuria A., Mann A., Khuntia J., Saldanha T.J., Kauffman R.J.

believe that cloud computing can be based on technological capabilities, cloud service portfolio capabilities

(cloud service offerings, market offerings) or cloud integration capabilities (legacy synchronization and

consistency) to influence profits, competitiveness and commercial turnover of business entities. Data analytics

is transforming business operations, but business and professional actors need to solve managerial and

technological challenges to benefit from large datasets. Incompatible IT infrastructure and data architecture

can impede the ability to store, analyze and retrieve effective information from datasets that include structured,

semi-structured, and unstructured data.

1 INTRODUCTION

Assets, trust of counterparties, property, money,

personal data and the system of contracts can be put

on a programming language in the blockchain domain

and it is important to manage how to create and

receive the desired result from each of these

components. Other scholars argue «despite the many

potential benefits of blockchain, the concepts

associated with blockchain (eg support, adoption,

implementation, etc.) still need to be understood and

well understood by many managers. Questions

related to how the results got from the use of

blockchain technology can ensure that through its

implementation in business processes the value of the

organization of management and business activities

https://orcid.org/0000-0002-0098-9555

of the business is increased, remain unanswered»

(Wamba, Queiroz, 2020). Blockchain auditing is one

of its greatest strengths regarding transparency,

although cybersecurity issues inevitably arise. The

major challenge for blockchain is to ensure that the

system is not riddled with fake blocks or invalid data

infiltrating from fake and illegal transactions or their

sources used to making important decisions that can

damage the digital ecosystem. Audit becomes nearly

impossible in a digital environment without the

ability to recalibrate what is fact rather than fiction.

Cloud computing is at the core of digital

transformation. It is imperative to explore the

relationship between cloud computing and the

Internet of Things, AI, blockchain, data analytics and

crowdsourcing to develop an innovative business

model. The downsides of cloud computing

Sushkova, O.

Methods and Means of Legal Regulation of Relations in Entrepreneurship: Novels of Digitalization.

DOI: 10.5220/0010662300003224

In Proceedings of the 1st International Scientiﬁc Forum on Jurisprudence (WFLAW 2021), pages 83-88

ISBN: 978-989-758-598-2

highlighted in this study are due to poor integration

and lack of business value. Cloud computing hacks

have also caused some of the largest retail data

breaches in online history, invalidating service level

agreements between businesses and cloud providers.

When the data of hundreds of millions of customers

get on the open Internet, this is a serious problem that

needs to be solved in court. End users are left stranded

when their credentials are stolen and class action

claims take a long time to resolve (Trofimova, 2019).

The Mandatory Data Breach Notice Principles and

Guidelines are directed to privacy and data officers

around the world to ensure disclosure of consumer

data breaches (e.g. credit card numbers, name, date of

birth, login information and password). After a

commensurately small fine for e-commerce service

providers, such unscrupulous business entities come

up with new mechanisms to carry out fraudulent

activities (Gracheva, Korobeev, Malikov, Chuchaev,

2020). Legislative regulators of business law do not

allow such persons to be prosecuted because it is

extremely difficult to prove their guilt because of

technical tricks.

IOT (Internet of Things) devices are placed in key

public places on the websites of businesses and

customers to stimulate digital innovation, to produce

video analytics for visitors and customers (for

example, in museums), and in commercial

storerooms to clarify a particular type of product on

the shelf ( its demand) and equipment operability for

its timely technical inspection or repair. By what

means can businesses communicate to citizens that

their tools are collecting and analyzing data in real

time? What are the legal and ethical regulators and

how business and professional entities can effectively

use the tools offered by scientific and technological

progress? Business entities need to study the existing

factors in the information's development society and

form law enforcement practice. This will contribute

to the development of legislative regulators that

ensure the normal conduct of business and

professional activities.

There are serious issues related to technology

incompatibility between enterprise-wide platforms

for sharing big data and analytics with an

organization and its industry system, and

inconsistency between internal and external

databases. Receiving data from third parties can also

create a risk that the data will be outdated and of

lesser value. Missing, incomplete, or inaccurate data

can distort models and algorithms. Data got meets

two important criteria: understanding and quality. To

extract information from the collected data, it is

important that analysts can understand and

distinguish relevant data from unrelated and

misleading data.

2 THE DISCUSSION OF THE

RESULTS

Data analytics in Russia has an expanded character:

the Concept for the creation of a digital analytical

platform (Order of the Government of the Russian

Federation of December 17, 2019, No. 3074-r) has

been adopted, which defines the procedure for

analyzing statistical data. The analytics used in

business and professional activity is different, but it

has elements of statistics. So far, this is one of the few

normative sources that regulate the public relations

under consideration and legal relations between the

subjects involved in business processes.

Tarasenko O.A. notes extended analytics in the

banking sector is little used in comparison with its use

in other types of business and professional activities

(insurance). Banks using advanced analytics have a

definite advantage over their competitors. Using

advanced analytics is expected to increase soon due

to many benefits: in terms of customer retention, risk

assessment, better decision making and fraud

prevention (Tarasenko, 2020). Mikhaylov A.V.

believes big data is the collective name for

approaches, tools and methods for processing results.

A vast amount of information from an ever-growing

number of sources will be systematized and processed

in such a way that the user of the processed data will

receive qualitatively new information. These legal

relationships are regulated by the Civil Code of the

Russian Federation through a contractual model - an

agreement on the provision of information (Article

783.1 of the Civil Code of the Russian Federation),

which is planned to be used to work with big data

(Mikhaylov, 2020).

Kharitonova Yu.S., Savina V.S. note “there are

discussions in science about how the use of big data

can be regulated, various legal regimes are proposed

- copyright or related rights, trade secrets, antitrust

regulation, etc.” (Haritonova, Savina, 2020). Using

big data is necessary for the effective functioning of

AI systems, which raises a whole range of legal and

ethical issues (for example, about the boundaries of

the use of personal data). The European Union

already has a privacy impact assessment (PIA)

procedure - an assessment of the impact of the

functioning of robots and other devices connected to

the Internet on the confidentiality of personal data.

The procedure is regulated by a special act on the

WFLAW 2021 - INTERNATIONAL SCIENTIFIC FORUM ON JURISPRUDENCE

collection, processing and storage of big data -

General Data Protection Regulation (GDPR)

(Regulation (EU) 2016/679). Each citizen, according

to the act, is given the right to file objections

regarding the fact of the collection of data (profiling)

and processing of the results about him, which can

have a significant impact on his rights and

obligations.

Data analytics in foreign countries have been

gaining momentum in recent years because of the

emergence of big data. Scientists argue (Akter,

Wamba, 2016) that it is "a holistic process that

includes the collection, analysis, use and

interpretation of data for various functional units in

order to got practical ideas, create business value and

establish a competitive advantage." Big data goes

beyond the capabilities of conventional database

systems (Dumbill, 2013) because the data does not

match the structure of the database architecture (as an

intellectual property object - added by the author). To

extract maximum value from data, advanced

information technology is required because existing

information systems are not suitable because of the

size and incompatibility of big data processing.

According to Mikhaylov A.V., blockchain

technology soon should become one of the most

popular in vertical and horizontal business relations

(Federal Law of 02.08.2019, No. 259-FZ).

Blockchain is based on multiple data duplication and

storage in a distributed network. Distortion of data in

this case is almost impossible - each record contains

a history of changes. The technology is being applied

in copyright fixing, in insurance, in crowdfunding

(Mikhaylov, 2020). The digitalization process

(blockchain technology) will allow accepting goods

(commercial activities), fixing the execution of the

contract, and making it part of an electronic business

contract (or the construction of a smart contract as an

electronic way of fulfilling an obligation).

Sushkova O.V. notes that as more and more users

use the Internet to meet their needs through the

purchase of goods - Internet e-commerce services are

becoming more global and complex. The question

arises about the effective regulation of Internet

commerce.

First, the legal regulation of such a global

environment would provide protection against

existing adverse actions. Second, there is a lack of

regulation in the delivery of goods ordered remotely.

Third, confidentiality issues can be quickly resolved.

In the changing conditions of the use of digital

technologies, there are frequent cases of violation of

the rights of buyers and users of the global network.

To solve this problem, blockchain technology can

become an algorithm that stores all transactions and

does not use the details of these operations to provide

other actions (Sushkova, 2020). Yu.G. Leskova notes

that «the use of the institution of self-regulation ...

will allow the state to solve two major tasks:

expanding the boundaries of self-regulation and

improving the methods of realizing the social rights

of citizens» (Leskova, 2013)

3 RESULTS

Blockchain, based on advanced cryptography,

operates as an open source distributed database

(Kirkland, Tapscott, 2013). Bitcoin is one of the most

popular blockchain applications running on an open

ledger (Kumar V., Ramachandran, Kumar B., 2020).

This open source platform allows anyone to change

the underlying code, giving everyone an opportunity

to see what's really going on. It is a peer-to-peer (P2P)

system that does not require intermediaries to

authenticate or settle transactions. The system can

record any structured information, for example, who

paid whom, who owns the money, or which light

source from which power source (Iansiti, Lakhani,

2017). Blockchain is a secure platform and cannot be

hacked, although recent research (Orcutt, 2019) has

reported security issues on some platforms.

Blockchain can actually reduce costs, such as the

cost of verifying transaction details and eliminate the

cost of intermediary services (Michelman, 2017). A

blockchain transaction works by representing a

transaction as a block in the system, which is then

broadcast to all parties in the network. When those on

the network approve the transaction, the block is

added to the chain, providing an ineradicable and

transparent record of the transaction, for example,

transferring money from one side to the other (as a

new electronic form of payment, along with payment

orders and others) (Crosby, Pattanayak, Verma,

Kalyanaraman , 2016).

The blockchain architecture comprises

contiguous blocks in sequential form that store

transactions and records similar to those stored in a

traditional public ledger. The blockchain comprises

decentralized ledger technology (DLT), which is

supported by peer-to-peer networks and is not

controlled or owned by any single authority. It is

protected from unauthorized access, and the user

cannot lose control over digital identities, even if they

lose access (Dunphy, Petitcolas, 2018). Blockchain

technology has three other recognized characteristics:

persistence, anonymity, and auditability. Blockchain

persistence is where tampering can be easily detected

Methods and Means of Legal Regulation of Relations in Entrepreneurship: Novels of Digitalization

as transactions are verified, written to blocks, and

propagated throughout the network. Anonymity on

the blockchain supports users as they can generate as

many addresses as they want to avoid revealing their

identity. The blockchain auditing capability allows

users to track and trace any transaction by accessing

any nodes in the distributed network, providing

improved traceability and data transparency (Zheng,

Xie, Dai, Wang, 2016). Blockchain operates on five

principles: record irreversibility, computational logic,

transparency with pseudonymity, distributed

database, and peer-to-peer networks.

3.1 Implementing Digital

Transformation in the Blockchain-

based Banking Sector

Tarasenko O.A. says that blockchain technologies are

used in banking to find cost optimization and new

business models. Large-scale events 2017-2018 with

the use of blockchain technology were: placement of

bonds of the National Settlement Depository, the

creation of a factoring platform by Sberbank and

M.Video, implementing S7 Airlines in partnership

with Alfa-Bank of a solution for the sale of air tickets.

The scientist notes that the limiting factors for the

spread of technology are: the unavailability of the

market infrastructure, the lack of effective cases and

personnel, the emergence of significant negative

experience. Analysts confirm this trend: up to 90% of

blockchain projects have not brought benefits.

Despite this, according to experts, in the coming years

there will be improvements in technology with an

emphasis on increasing its productivity. The success

of the Visa payment system is 56 thousand

transactions per second, while performing most

blockchain networks is only dozens of transactions

per second. Tarasenko O.A. notes that we will

comprehend the understanding of the effectiveness of

the application of such technologies in the Russian

banking sector over the next 10 years (Tarasenko,

2020).

Thanks to the constant development of data and

computing power, big data is effectively used for

business or data analysis (Wamba, Akter, Edwards,

Chopin, Gnanzou, 2015). Big data and traditional

analytics researchers are looking at different ways to

extract additional information from different data

sources to gain a competitive advantage (Battisti,

Shams, Sakka, Miglietta, 2015). Traditional analytics

differs from big data analytics in four dimensions:

volume, variety, speed, and availability (Morabito,

2015). Volume represents a disproportionately

enormous amount of data and less data storage needs

of business and professional entities. These

organizations need to capture large amounts of data

from pervasive, heterogeneous and developing

sources and devices in order to generate effective and

meaningful information for making accurate

decisions. Diversity refers to the different data

collected from businesses, which can include

structured, semi-structured, and unstructured data.

Because of the dynamic nature of big data, speed

depends on the speed of data creation and analysis,

and sometimes includes real-time analytics. We

define accessibility as the ability to get data from a

variety of sources (Ohlhorst, 2013). Many researchers

substitute accessibility and include fidelity as the

fourth dimension of big data and describe dimensions

as 4V. Truthfulness is related to the validity and

access to the complete dataset because the

uncertainty, complexity, inconsistency, and

anonymity of big data can affect reliability. Recently,

other authors have proposed two more dimensions:

value and variability, characterizing big data as 6V

(Akter, Bandara, Hani, Wamba, Foropon,

Papadopoulos, 2019). Variability is associated with

the heterogeneity of big data because it can be

generated because of the difference in speed. The

economic value of data types determines the

dimension of big data. Data in its raw form is useless

until it is explored using appropriate analytics to

extract meaningful information.

Consumers, automation and monetization are

considered the three main driving forces of big data

(Sathi, 2012). Big data has seen a vigorous growth in

recent years thanks to the Internet of Things (IoT),

which includes machine intelligence. IoT, due to the

interconnected nature of network technologies and

smart devices, can facilitate fast and constant real-

time data exchange with the potential to improve

functionality and scale up processes, leading to new

and better products and services (Xia, Zhang, Chiu,

Jing, 2020). Big data opens up new opportunities and

adds operational and financial value. Companies can

leverage their resources to achieve better results by

harnessing the potential of big data. Cost

effectiveness and efficiency, improved decision

making and exploration of new opportunities are

considered the three major benefits of big data

analytics (Davenport, 2014). Large companies can

adopt big data technologies to leverage traditional

technologies. This can significantly improve

efficiency by increasing productivity and product

quality through added value (Manyika, Chui, Lund,

Ramaswamy, 2017). Production data can be analyzed

to map the optimal use of resources - time, human

resources and raw materials. Big data can improve the

WFLAW 2021 - INTERNATIONAL SCIENTIFIC FORUM ON JURISPRUDENCE

before and after production stages in the supply chain

and combine production data with other functions,

increasing overall efficiency and effectiveness

(Feinleib, 2014). Big data analytics can decide more

efficiently and quickly, and provide opportunities for

fact-based decision making. Data-intensive

companies such as Google, eBay, Amazon and

Facebook are generating additional revenue and

implementing new value streams through Big Data

analytics. Information from large datasets can

transform business models, enhance innovation and

productivity, and open up new markets using data-

driven approaches (Gobble, 2013).

4 CONCLUSION

Implementing digital business transformation should

focus on how to integrate new and other technologies

(for example, the Internet of Things) for various

business functions in hybrid modes, for integration,

recombination and convergence. Cloud accounting is

gaining traction when supported by AI, big data, and

blockchain-based financial reporting (Ionescu, 2019).

To develop a holistic platform using innovative

technologies, foreign scientific literature proposes a

framework showing how to integrate AI, IoT and

blockchain for the next generation cloud computing

environment (Gill, 2019). Recent research highlights

the link between AI, deep learning and blockchain as

complementary technologies for digital

transformation (Arora, Chopra, Dixit, 2020). This

integration can help business community actors

develop customer and supplier relationship

management through innovative business models that

are being implemented. Leading cloud AI platforms

such as Microsoft Genee, Oracle Crosswise or

Salesforce Einstein strive to achieve a competitive

advantage in their markets through predictive and

prescriptive analytics. Fundamental applications of

new technologies (for example, AI, advanced

technologies, the Internet of Things and robotics) and

understanding how to integrate these processes can be

effective if there is a systemic legal regulation at all

levels (international, European and national).

This study, using an interdisciplinary perspective,

puts forward technology as a fundamental building

block for the future of digital business transformation.

To answer the questions in technology-driven

research, we started with a discussion that clarified

the concept of business transformation and its

implications for various industries. The author then

introduced AI, blockchain, cloud computing, and data

analytics with use cases and applications. As the

operational effectiveness of applications will shape

the future of digital business transformation, the

findings shed light on various challenges and

opportunities. The most important question for

business and professional entities is to establish the

relationship between these technologies in order to

get the maximum benefit. Innovative processes

include hybridization, integration, recombination and

convergence. The study summarized the early

emergence of technology and its impact on digital

transformation through business use cases.

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