Добавил:
ivanov666
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:I.T. Innovations in Business. Teaching handbook
.pdf
3. Modern technological
innovations
This section dives into a few modern technological innovations
which have made a huge impact in today’s world and will continue
to propel society into the future.
3.1. Big Data and Aificial Intelligence
Data is a collection of facts, figures, and statistics that are used to
represent information. Data can be in various forms, including numbers, text, images, audio, video, and more. Data is essential for making informed decisions, identifying trends, and understanding patterns.
Data can be categorized into two types: structured and unstructured. Structured data is organized and formatted in a specific way,
making it easy to search, sort, and analyze. Examples of structured data
include spreadsheets, databases, and tables. Unstructured data, on the
other hand, is not organized in any way, making it difficult to search
and analyze. Examples of unstructured data include emails, social
media posts, and images. Data can also be categorized into two types
based on its source: primary and secondary data. Primary data is collected directly from its source, while secondary data is collected from
other sources, such as books, articles, and reports. Primary data can be
collected through surveys, experiments, observations, and interviews.
The nature of data is constantly evolving, with data becoming bigger, more complex, and more diverse. Big Data refers to data sets that
31
3. Modern technological innovations
Volume
Velocity
Value
• Hypothetical
Veracity
• Accountability
Variety
are too large and complex to be processed by traditional data processing methods. The rise of the Internet of Things (IoT) has led to the
creation of more data than ever before, with sensors and devices collecting data in real-time. This data can be analyzed to gain insights
into consumer behavior, product performance, and market trends.
The five main and innate features of big data are the 5 V’s (velocity, volume, value, variety, and veracity). Knowing the 5 V’s enables data scientists to extract more value from their data while also
enabling the scientists’ company to become more customer centric.
Big data was only discussed in the early part of this century in
terms of the three V’s: volume, velocity, and variety. Two more V’s
(value and veracity) have been developed over time to assist data scientists in better articulating and communicating the important qualities of big data. The number five represents the five fundamental questions that any news item should address.
• Terabytes
• Records/Archives
• Transactions
• Tables, Files
• Statistical
• Events
• Correlations
• Trustworthiness
• Authenticity
• Origin, Reputation
• Availability
• Batch
• Real/Near-Time
• Processes
• Streams
• Structured
• Unstructured
• Multi-factor
• Probabilistic
Fig. 12. The 5 V’s of Big Data
Volume: The first of the 5 V’s of big data is volume, which refers
to the amount of data that exists. Volume is the foundation of big data
because it is the initial quantity and amount of data collected. When
the volume of data is vast enough, it is referred to as big data. What is
32
3.1. Big Data and Aificial Intelligence
deemed big data, however, is relative and will fluctuate based on the
available computing power on the market.
Velocity: It relates to the speed with which data is generated and
moved. This is a key consideration for businesses that require their data
to flow fast so that it is available at the appropriate moments to make
the best business decisions possible. A big data organization will have
a vast and continuous flow of data being created and sent to its eventual destination. Data could come from a variety of sources, including
machines, networks, smartphones, and social media. This data must be
swiftly digested and analyzed, sometimes in near real time. In healthcare, for example, several medical devices are now available to monitor
patients and collect data. From in-hospital medical equipment to wearable gadgets, acquired data must be swiftly transmitted and processed.
However, in other circumstances, having a limited amount of collected data may be preferable than collecting more data than an organization can process, as this might result in slower data velocities.
Variety: The term “variety” refers to the variety of data types. An
organization may collect data from a variety of different data sources, the value of which may vary. Data can come from both inside and
outside of a company. The standardization and sharing of all data collected is a difficulty in variety.
Data collected can be unstructured, semi-structured, or structured. Unstructured data is disorganized data that comes in many files
or types. Unstructured data is typically not a good fit for a standard
relational database since it does not fit into traditional data models.
Semi-structured data is information that has not been arranged into
a specific repository but does have associated information, such as
metadata. As a result, it is easier to process than unstructured data.
Structured data, on the other hand, is information that has been organized into a prepared repository. This means that the data is more
easily accessible for efficient data processing and analysis.
Veracity: It relates to the data’s quality and accuracy. Data collected may be incomplete, erroneous, or incapable of providing true,
valuable information. Overall, veracity refers to the level of trust in
the obtained data. Data might become jumbled and difficult to use at
times. If the data is incomplete, it can produce more confusion than
33
3. Modern technological innovations
insights. In the medical field, for example, if data on what drugs a patient is taking is missing, the patient’s life may be jeopardized.
Both value and truthfulness contribute to the definition of data
quality and insights.
Value: This refers to the value that big data may give, and it is
closely related to what organizations can do with the data they collect. The ability to extract value from big data is required, as the value of big data increases considerably based on the insights that can
be gleaned from it.
Organizations can obtain and analyze data using the same big
data techniques, but how they derive value from that data should be
unique to them.
With respect to big data, here are a number of key trends:
1. Non-database sources as dominant data generators: Voice assistants and IoT devices are driving a rapid increase in big data
management needs across industries, forcing organizations to
reexamine their data processing requirements.
2. Increased focus on information quality and improved governance: Organizations are recognizing the importance of reliable and accurate data for making informed decisions and are
investing in strategies to enhance data quality and governance.
3. Leveraging AI and ML technologies: Artificial intelligence
(AI) and machine learning (ML) are being used to extract
valuable insights from large datasets, enabling organizations
to make more accurate predictions and optimize their operations.
4. Stronger reliance on cloud storage: The cloud provides a scalable and cost-effective solution for storing and processing
large volumes of data, allowing organizations to access their
data from anywhere and at any time.
5. Real-time analytics: With the evolution of streaming analytics, organizations can now analyze data as it is generated, enabling them to make immediate decisions and respond quickly to changing market conditions.
6. Expansion of edge computing: Edge computing allows organizations to process and analyze data closer to the source,
34
3.2. Blockchain Technology and Web 3.0
reducing latency and enabling real-time decision-making
in remote or resource-constrained environments.
7. Ethical customer data collection: As data privacy concerns
grow, organizations are focusing on collecting and using customer data in a responsible and ethical manner, ensuring compliance with regulations and building trust with their customers.
8. AI/ML-powered automation: Big data analytics is increasingly being used to automate various tasks and processes, improving efficiency and freeing up human resources for more
strategic activities.
9. Wider usage of RPA technologies: Robotic Process Automation (RPA) is being employed to automate repetitive and
rule-based tasks, enabling organizations to process and analyze data more efficiently.
10. Data Fabric and Data Mesh: These approaches to data management and integration are gaining popularity, allowing organizations to create a unified and flexible data architecture
that can support their evolving needs.
11. Cybersecurity: As the volume of data increases, organizations are placing a greater emphasis on data security, implementing robust cybersecurity measures to protect their valuable information.
3.2. Blockchain Technology and Web 3.0
In more recent times, the term Web 3.0 (aka Web3) has become
a buzzword. This phrase in general is used to describe the third generation of internet services, still under development and is aimed at
creating a decentralized and open web that possesses greater utility
for all users. In addition, experts have purported that it will be the
new face of the web powered by technologies such as artificial intelligence, IoT, and blockchain.
One of the key and central themes to Web3 is blockchain technology. Blockchain technology is a decentralized, distributed ledger
35
3. Modern technological innovations
that stores the record of ownership of digital assets. In summary, it
is a shared, immutable ledger that facilitates the process of recording
transactions and tracking assets in a business network10. Though it is
still under development, there are a number of challenges to consider, such as scalability, interoperability, and regulatory issues. In addition, the technology is complex and requires specialized knowledge
and expertise for effective development and implementation.
Blockchain plays a crucial role in transforming the conventional approaches for data storage and management. Blockchain offers
a unique collection of data or a universal state layer, which is subject
to collective management.
Block N–1: This represents the previous block in the blockchain
(Fig. 13). It contains a cryptographic hash of Block N, which serves
as a unique identifier for that specific block. It also contains a list of
transactions that occurred before those in BlockN. Essentially, Block
N–1 is the block that directly precedes Block N in the blockchain.
Block N-1 Block N Block N+1
Fig. 13. Blockchain Structure
10
What is blockchain? // IBM : site. URL: https://www.ibm.com/topics/block-
chain (date of access: 27.07.2023).
11
Iqbal M., MatulevičiusR. Exploring sybil and double-spending risks in block-
chain systems // IEEE Access. 2021. Vol. 9. P. 76153–76177.
36
11
3.2. Blockchain Technology and Web 3.0
Block N: Block N is the current block in the blockchain. It includes a reference to Block N–1’s hash, thereby linking it to the previous block. Block N contains a batch of new transactions that have
been validated and added to the blockchain. Miners or validators
in the blockchain network verify these transactions before they are
included in BlockN.
Block N+1: This is the block that will follow Block N in the blockchain. It is typically empty or contains only a few initial transactions
because it is in the process of being mined or validated by network participants. Once enough transactions are added and verified, Block N+1
will be appended to the blockchain, and the cycle continues.
A blockchain can be pictured as a digital notebook shared
by a group of people. Instead of it being owned and controlled by
one person, all parties have a copy of the notebook. Each of the group
members works together to update it. The notebook records transactions, such as every gift received or given to someone else. The peculiar aspect is as follows: Each page of this notebook is linked to the
one before it, thereby creating a chain of pages. Also, on every page,
there is a list of the undertaken transactions over time. Thus, if anyone tries to change information/data on an earlier page, it would be
obvious because it destroys everything that comes after it. As such,
the notebook is very secure. In order to make a change, each member of the group must be in agreement. Since everyone has their own
copy of the notebook, no one person can make secret changes.
This shared notebook can be likened to the blockchain which
is a way to keep a secure and unchangeable record of transactions,
like a digital ledger that lots of people keep an eye on. Blockchain has
found its usefulness for cases such as digital money (cryptocurrencies),
healthcare records, supply chain because it ensures that no one can rig
the system or falsify records without everyone else knowing about it.
The arrival of Bitcoin was one of the first points for drawing the
outline for Web 3.0. The Bitcoin blockchain and other protocols
helped in creating networks where hackers would have to break into
multiple houses all over the globe for accessing data in one house.
Blockchain placed the foundation for Web 3.0 definition as it facilitated the data storage in multiple copies of the P2P network.
37
3. Modern technological innovations
The protocol helps in the formal specification of management rules
in the protocol. In addition, the protocol also guides the security of
data through majority consensus from all participants in the network
(Fig. 14). The participants receive incentives in the native network token for their contribution to the network’s security and maintenance.
Fig. 14. Blockchain Consensus Mechanisms
12
Just as every technology, there are variations for different contexts. Blockchain architectures include (Fig. 15):
Public Blockchain: The most well-known type of block-
chain, used by cryptocurrencies like Bitcoin and Ethereum.
In a public blockchain, anyone can join the network, participate in consensus, and validate transactions. Transactions
are transparent and open to anyone to verify.
12
Consensus Mechanisms in Blockchain // Shiksha Online. URL: https://www.
shiksha.com/online-courses/articles/consensus-mechanisms-in-blockchain/ (date
of access: 27.07.2023).
38
3.2. Blockchain Technology and Web 3.0
Fig. 15. Permissioned vs Permissionless Blockchains
13
Private Blockchain: Also known as permissioned blockchains,
these are restricted to a specific group of participants. Only
authorized users can join the network, validate transactions,
and access the data. Private blockchains are often used for enterprise applications, where transparency is important among
known participants.
Consortium Blockchain: Similar to private blockchains, con-
sortium blockchains are controlled by a group of organizations
rather than asingle entity. They are typically used for collaborative projects where multiple organizations need to share data
and collaborate while maintaining a certain level of control.
Hybrid Blockchain: This is a combination of public and pri-
vate blockchains. Some data or transactions are private, while
others are public. It can be useful when different parts of anetwork require different levels of security and transparency.
13
Realizing edge marketplaces: Challenges and opportunities / Varghese B. et al. //
IEEE Cloud Computing. 2018. Vol. 5. No. 6. P. 9–20.
39
3. Modern technological innovations
Sidechains: Sidechains are separate blockchains that are in-
teroperable with the main blockchain (mainchain). They can
be used to perform specific functions or experiments without
affecting the mainchain’s stability.
Federated Blockchain: This architecture involves a group of
pre-selected nodes or validators that have the authority to validate transactions. It combines some aspects of centralization
with the security benefits of blockchain.
MultiChain: This is a specific technology that allows organ-
izations to build private blockchains for specific use cases.
MultiChain is designed for applications that require high levels of data privacy and control.
With respect to the use cases of blockchain, the technology has
many potential use cases beyond it being known to be the core of Bitcoin. These are some of its emerging applications across finance, business, government, and other industries:
Banking and Finance: Blockchain provides a way to securely and
efficiently create a tamper-proof log of sensitive activity. This
makes it excellent for international payments and money transfers. It also has the ability to streamline trade finance deals and
simplify the process across borders. Blockchain technology can
be used for regulatory compliance and audit purposes as well.
Supply Chain Management: Blockchain can be used to track
the movement of goods and ensure their authenticity. It can
also be used to track the origin of products and ensure that
they are ethically sourced.
Healthcare: Blockchain can be used to securely store and share
patient data, ensuring that it is accurate and up-to-date. It can
also be used to track the supply chain of pharmaceuticals and
medical devices, ensuring that they are genuine and safe.
Real Estate: Blockchain can be used to create a decentral-
ized and tamper-proof record of property ownership. This
can help to prevent fraud and streamline the process of buying and selling property.
Voting and Governance: Blockchain can be used to create
a secure and transparent voting system, ensuring that votes
40
Соседние файлы в предмете [НЕСОРТИРОВАННОЕ]