Technological Innovation Driven by Big Data

Abstract

Large-scale research evaluations, as well as the acquisition and analysis of massive amounts of data, in order to follow trends and models form the organization. As the world around us continues to change, many entrepreneurs are interested in learning how big data might help them enhance their businesses. While businesses have traditionally analyzed data, recent technical advancements have spawned new entrants and unlocked the potential of big data. The extent of the consequences of big data, according to an SNS research, is presently $57 billion and expected to rise further.

Full text

240
Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 13
DOI: 10.4018/978-1-6684-5656-9.ch013
ABSTRACT
Large-scale research evaluations, as well as the acquisition and analysis of massive
amounts of data, in order to follow trends and models form the organization. As
the world around us continues to change, many entrepreneurs are interested in
learning how big data might help them enhance their businesses. While businesses
have traditionally analyzed data, recent technical advancements have spawned new
entrants and unlocked the potential of big data. The extent of the consequences of
big data, according to an SNS research, is presently $57 billion and expected to
rise further.
Technological Innovation
Driven by Big Data
G. Taviti Naidu
Dr. L. Bullayya College, India
K. V. B. Ganesh
KLEF Vaddeswaram, India
V. Vidya Chellam
Madurai Kamaraj University, India
S. Praveenkumar
https://orcid.org/0000-0001-9639-
5817
Madurai Kamaraj University, India
Dharmesh Dhabliya
Vishwakarma Institute of Information
Technology, India
Sabyasachi Pramanik
https://orcid.org/0000-0002-9431-
8751
Haldia Institute of Technology, India
Ankur Gupta
https://orcid.org/0000-0002-4651-
5830
Vaish College of Engineering, India

Copyright © 2023, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
241
Technological Innovation Driven by Big Data
INTRODUCTION
What role does big data play in driving business trends, and what does the future
hold for the industry? Big data need a highly trained staff. Smart firms utilize big
data to increase consumer engagement, develop new markets for their marketing,
and improve operational performance. However, if a company has the human
resources required to take benefit of this cutting-edge contemporary technology, it
will be unable to do so. Companies are continuously pursuing a thorough grasp of
data collection and assessment companies. These expert data masters gather up the
companies that hope to plant the Fortune 500 one day, with some companies also
planning to guarantee that their personnel are adequate. While employees with a
university education are presently highly valued, the workplace of the future will
need people with more sophisticated abilities and technology.
Consider Teradata’s 2017 Development Survey, which found that over half of
global companies are experiencing a shortage of technology-skilled people. Businesses
will want more imaginative employees who are not intimidated by developing
technologies as the big data sector grows. As big data becomes more spectacular,
companies need ensure that their employees are eager to analyze it and make better
judgments. Due to the absence of productivity of workers, every creative company’s
leftovers are the polar opposite.
Because of big data’s revolutionary power, a range of major markets have been
transformed. Algorithms are expected to heavily govern the finance sector in the next
years, smashing unusually large-scale data endeavors to anticipate industry changes
and discover flaws in financial operations (Welc, 2022). Big data (Schloemer, 2022)
is also propelling emerging industries like financial processing, driverless cars, and
smart homes (Nyangaresi, 2022) forward. To stay a keep away from collisions and
modify their routes, tomorrow’s incredible smart automobiles will rely on gathering
and analyzing locally specified data. Even digital data is responsible for the market
position of many established firms. Big data is a term that refers to a large Netflix,
a well-known website, has now become self-evident, and basically re-forming the
home video industry by capturing and analyzing user data. By continually upgrading
data stages, the organization will determine which of its shows is better in particular
random markets, predict which pilots it can finance, and even predict the amount
of leisure grants it will get.
For large organizations, utilizing and understanding big data is a crucial competing
edge. Organizations can have a possibility to know about initial vision that their
opponents don’t know whether they can acquire extra data from their present clients
and technologies.

242
Technological Innovation Driven by Big Data
Various opportunities for latest advancements may be provided by big data, from
internal perceptions to direct client commitments. The utilization of technologies,
extensive investigation, and data-driven analysis are 3 important commercial potential.
AUTOMATION
By robotic process automation, big data has the capability to intensify competence
and functioning in an organization. For automated decision-taking, large volumes
of real-time data can be processed fast and consolidated into corporate functions.
Computerizing data collection and storage is achievable due to scalable IT framework
and reduction of cloud computing costs.
Detailed Insights
Big data can be utilized to expose chances that organizations were ignorant of earlier
possessing the ability to evaluate large volumes of data. Also, the forming of newer
or enhanced elements can be done utilizing composite data sets. Market-dependent
proprietary data is useful in a cutthroat atmosphere.
Superior and Quicker Decision-Making
Organizations may now rapidly evaluate knowledge and take educated, informed
choices and courtesy to the speed of data analysis frameworks and the enhancements
to survey newer origin of data.
How to Use Big Data to Your Advantage
The necessity for big data analysis is extensive; since 2015, more than 55% of major
firms made investments in big data technology. To handle the many potential data
points, it might be difficult to know where to start.
The following factors should be taken into account by enterprises before choosing
and establishing a big data solution.
Expertise in a Big Data Team
Establish a group of experts in data gathering, analysis, and technique to aid in
developing the perfect big data plan that generates profits for the business. People
who are knowledgeable about current analytical techniques, adept at working with

243
Technological Innovation Driven by Big Data
huge volume of data sets, and specialists who understand broad business objectives
should be on this team.
Select Final Objectives
For a big data solution to be implemented effectively, having the appropriate goals
is essential. The organization’s ultimate objectives must be correctly matched with
the data and analytics (i.e., huge gain, brand acknowledgement, sales figures).
Obtain Accurate Data
The next stage after exhibiting the major commercial goals is in thoroughly
comprehending the data prior to implementing it. The foundation of the whole
big data methodology is recognizing, obtaining, and tracking the appropriate data.
Utilizing the erroneous data sets may have adverse effects and steer the whole
business in an incorrect way.
Use the Right Analytical Techniques
Professional data analysts are adept at precisely and quickly turning massive data
into insightful information. Aiding squads process data analytics and build swift
marketing choices more quickly when the data is presented in digestible, visual
summaries.
TOOLS FOR BIG DATA ANALYTICS
Big data’s emergence has given birth to a sizable market for data analytics tools
that assist businesses in smoothly implementing big data solutions. Startups with
expertise working fast with major organizations to prepare data for action include
Ople and Cruz Informatics.
Ople was established with the clear goal of making artificial intelligence simple,
affordable, and commonplace. Ople quickens the data science methodology, allowing
businesses to include more problems and find solutions more quickly. Data scientists
can create ten times more manufacturing-dependent Artificial Intelligence techniques
with Ople and cut the span of stationing from years to a lesser number of days by
concentrating on the business intelligence.

244
Technological Innovation Driven by Big Data
Informatics Crux
An industry’s lifeblood is the use of data to unearth useful insights. Crux helps
businesses easily obtain, examine, and alter data so they can concentrate on what
truly matters by taking on the cumbersome portions of their information supply
chains. This makes data actionable and ready for action. Crux provides a safe, scalable
environment to store, analyze, and transform data via its built-in cloud computing
approaches with its Informatics Platform. Data is made enjoyable with Crux.
Big Data as A Tool for Business Success
If more firms and government agencies see the benefits of big data, there’s little
question that more money will be invested in it to put it to good use. If insurance
companies want to determine who among their clients is responsible for injuries,
they will utilize increasingly sophisticated technologies to detect hazard. Apple and
Google, for example, will research how their new devices will be offered to their
current customers. Big data has nearly limitless ramifications.
The success report from IBM focuses on exploiting large-scale data to identify
process flaws and draw consumer expectations about the things they purchase, such
as 3D (Goh et. al., 2022) and wearable technology (Madanian et. al., 2021). IBM’s
innovations are built on one of the most widely used technologies.
Figure 1. Big data functionalities in an enterprise

245
Technological Innovation Driven by Big Data
How CEOs and CIOs early participate in inquiry to assist manages their firms
and forecast the future is one of the most important factors for a company’s success.
Businesses may need more usable data sources for interpretation and analysis
as the internet becomes more sophisticated. There is an entire sector dedicated to
companies that are sufficiently cautious to participate in scientific forecasting.
With computerization and the ability to challenge present economies, views for
the destiny of the digital world are constantly concerning, but they seldom attain
their full potential for significant effects. In the near future, this extraordinary
method will be utilized to make, market, and deliver almost any item and service.
Individuals and enterprises that do not want to be left alone should learn about
the destiny of the knowledge sector and make plans for it. There’s a whole new
future, and it’s one that revolves on big data.
Without a question, there have been significant advancements in big data and
business intelligence (Kanan et. al., 2022). They have a tremendous power to accelerate
social and economic benefits, ranging from human well-being to sustainable urban
life. Using such gadgets to incapacitate a person, on the other hand, is absolutely
improper. Personal data collected centrally for regulated monitoring was manipulated
in an authoritarian manner by heavy pushing and resident evaluations. It is not only
contrary to human rights and democratic norms, but it is also unsuitable for leading
new, innovative societies. Enhanced intelligence and risk mitigation strategies are
plainly required to solve the world’s genuine concerns. The Data for Humanity
program (see “Digital Data for Society and Civilization”) and responsible software
studies provide guidance on how Digital Data and Business Analytics (Zamani et.
al., 2022) might be utilized to benefit society.
Big Data’s Impact on Disruptive Innovation
A large majority of individuals approve, despite the different implementation variants.
The term “disruptive development” refers to the following:
• (From a client’s perspective) low-cost
• Technology that is easier to use or that can be distributed
• There are more options available.
• Use an auxiliary factor model as well (relative to current arrangements)
The reality that each of the three companies has a hard time adjusting to competition
is one of the main reasons why these disruptive traits are important. When one or two
pieces of an infrastructure, highly qualified individuals, or an out-of-date logistics
system become obsolete, it is difficult to adjust quickly to current circumstances.
Hundreds of people being laid off, consumers being inconvenienced, and billions

246
Technological Innovation Driven by Big Data
of dollars in investment – executives find it difficult to examine these factors and
take real-world realities into consideration.
We sought for signals of impending disruptions in the low end of the scale, and
we found them. Innovative products looked to be more cramming than existing high-
end structures due to their smaller, more open, and centered on new technological
designs. Because of their lower prices, they were able to attract purchasers who had
earlier been turned down. Apple initially built a gadget that was affordable enough for
adolescents to be interested in, customers who would not have considered purchasing
a DEC device. Their cost advantage is advantageous to them. Sony is well-known
for its “compactness” in television. Before the transistor, no one realized you could
do it fairly. Emerging breakthroughs, in connection with the evolution of a business
strategy, have the basic economic benefit necessary to capture large sectors of the
industry over time.
When you return to the previous description, however, the concept that reduced
entry was accomplished from a troublesome method mill isn’t at the forefront.
Instead, it was a byproduct. Rather. What is the explanation behind this? Because a
newcomer cannot provide better value in a competitive market that has been polished
over decades, goods however, although the low-end approach was popular, it was
not what kept competitors in business. They were driving marginal profit increases
via their own pricing policies and focus, effectively keeping these companies out
of the marketplace. CEOs are condemned to battle as long as the current choice
(trying to make obsolete facilities more useful) maintains the off-base option on a
long-term posture (don’t take up new technology).
Unfortunately, the focus on low-end disruption just obscures our capacity to see
the things: lower, more open, and costly. In particular, data-driven (Dushyant et. al.,
2022) industry scientists are likewise perplexed about enabled interrupters. To get
a feel of this, look at a few hotly contested instances.
Is super upsetting to you? ‘No, since their initial offering was at the upper end of
the market,’ says the outsider. The correct approach is to acknowledge that the stage
they provided ultimately enabled them to have lower-cost drivers (like UberX) and
give more affordable, open-air transportation options, which provide a fundamental
economic advantage for both taxi companies and, most likely, automobile ownership.
The software’s simplicity is by far the easiest and most obvious feature to duplicate.
Was the transition from Google’s to Nokia’s cell phones disruptive? “No,” they
say, “since their core smart phones were more useful than Nokia’s own phones,
which dominated the worldwide scene.” The right response is that the strategy of
creating an atmosphere of utilization development on stage enabled them to operate
in a more comprehensive way, which is (all) cheaper.
Is 23andMe posing a danger to pharmaceutical companies? “No, since it is in
two independent verticals,” the foundation responds. One is found in our forefathers,

247
Technological Innovation Driven by Big Data
while the other is found in pharmaceutical manufacturing. The argument is that 23
of us have vast amounts of data that will enable them to begin developing treatments
that are less expensive, more accessible, and intrinsically useful. That is the correct
response.
In each of these circumstances, the end outcome is disruption. Both of these
circumstances encourage current managers to avoid the stumbling block and stick
with their present solutions. Taxi companies attempted to take advantage of laws in
order to maintain the reputation of their medallion and drivers. Nokia’s attempt to
safeguard its closed environment and keep thousands of Symbian-oriented employees
employed was foolish. And you can be guaranteed that Merck, Pfizer, and Roche
have better advantages than 23andMe, while choosing a totally different strategy to
making the most of their high-end R&D businesses.
In the subject of development, there is something fresh that is missing from the
conversation. New means of addressing markets are accessible in different ways
at the centre of most recent revolution. Uber built a stage in the competitive limo
industry that allows for more reliable transportation and organization. Netflix stole
your eyes and used the data required to blast the content development process in
streaming videos. Google mapped the whole planet before learning how to build
self-sufficient traffic and street forms.
There is no doubt that this is a breach of privacy (Bandyopadhyay et. al., 2021).
These companies create more affordable and useful items than their competitors. It
does not, however, begin at the bottom end of the market; rather, it emerges from
symmetrical sectors with significant knowledge synergy. It all starts with the data
base, and then the information infrastructure may be organized to target an existing
market.
Administrators, engineers, and entrepreneurs must stop disputing whether classical
disruption satisfies the requirements. The disruption that data may cause is unique to
the paradigm shift, but it’s already here—and it’s here to stay. Uber’s devastation of
the taxi business is an illustration of the present system’s extraordinary cost benefit
and lack of response. The following should be the new issues:
• “How do you think you’d be able to adapt to this new form of competition?”
• “How do you assess new threats?” says the narrator.
• “When data is a fundamental element of every new disruption, what skills do
you need and where do you acquire them?”
Challenged firms have an excellent method of seeing future risks in conjunction
with a desire to maintain long-term commitments protected – via transformational
revenue – in order to survive in this shifting atmosphere. So, how are we going to
proceed now? First and first, people’s basic rights should be protected, especially

248
Technological Innovation Driven by Big Data
in this era of digital transformation, since they are the foundations of a modern,
functioning, democratic society. This necessitates the establishment of a new mutual
trust and cooperation arrangement in which individuals and customers regard
themselves as allies rather than obstacles or instruments to be used. For this to
occur, the state must have an efficient legislative apparatus in place to ensure that
technology is created and applied in democratically acceptable ways. This should
provide substantial material self-determination, as well as other aspects of self-
determination and accountability in our lives. This is a prerequisite.
Even a replica of personally identifiable information obtained about us should be
safeguarded. It should be controlled by law in order to address their data consumption
(perhaps with the help of unique AI-based digital partners), and the like information
would have to be sent to a centralized personal data storage place. To guarantee
better protection and discourage separation, illicit data usage should be forbidden
by law. People will then decide who should utilize their knowledge for what cause
and to what extent. Furthermore, necessary actions should be made to guarantee
that data is stored and shared in a secure manner.
The accuracy of the information on which we make our judgments will be
substantially improved by advanced credibility systems with multiple characteristics.
We may perceive issues from numerous distinct perspectives and are less likely to
control them by skewed information if the user selects and customizes data filters
(Koren et. al. 2022), search recommendations (Argent et. al., 2022), and estimations.
We also demand an effective public grumbling procedure, as well as successful
acquiescence for law violations. To achieve enough accountability and trust, premier
research organizations should act as stewards of data and computations that now
elude democratic supervision. This also necessitates a reasonable, exact code that
everybody with access to sensitive data and statistics ought to be able to follow, as
a form of Hippocratic pledge for IT professionals.
Furthermore, we will need a worldwide strategy that includes the establishment
of a foundation for rising businesses as well as the development of civic culture.
Every year, we spend billions of dollars on agriculture (Pramanik et. al., 2022),
public infrastructure, schools, and universities.
So, what public structures are required for contemporary civilization to succeed?
To begin with, completely new educational concepts are required. Rather of
developing automated labor, the focus should be on critical thinking, creativity,
innovation, and entrepreneurship (whose duties are to be carried out by machines
and computer calculations in the future). People must be aware of the connectivity
between the developing world and the physical world, therefore education should
include knowledge of how to utilize new technology effectively and practically.
People must understand these approaches, as well as which uses are unlawful,
in order to defend their rights in a safe and reasonable manner. It is all the more

249
Technological Innovation Driven by Big Data
critical, then, to make information available to research, industry, government, and
educational organizations.
Self-employment, the establishment of their own businesses, the investigation of
coordinate partners, the manufacture of global market products and services, capital
management, and the supply of a structured savings pledge at government cost all need
a participatory stage (a form of shared economy for all). Furthermore, municipalities
and villages may establish new digitals centers – for example, amazing labs – where
ideas may be generated and tested for free. The openness and creativity with which
these centers operate might help to bring about tremendous social transformation.
Specialized competitions may give additional impetus for change, increase
civic exposure, and act as a catalyst for a more active digital community. It may be
particularly beneficial in bringing together common groups in order to guarantee that
their responses to global concerns are as near as possible (e.g. “Climate Olympics”).
Etapes aimed at combining limited capital, for example, might aid in unlocking the
enormous potential of the mostly untapped circular and social economy.
With a commitment to an open data strategy, governments and business will
increasingly disclose data to scientists and the general public, creating the necessary
circumstances for effective and creative knowledge and an environment that fulfils
our needs. Tax breaks, such as those offered in certain nations to encourage the
adoption of renewable technologies, may also help.
Third, building a “human-controlled digital nervous system” might open up new
Internet of Things (Pramanik, 2022) opportunities for everyone and give real-time
data measurements. The positive and negative adverse effects of our connections
with people and our ecology must be examined in order to enable effective energy
usage and minimize climate change. Processes may be steered by self-association
to create the desired outcomes when using effective feedback rings.
For successful adoption, we’ll need different incentives and sharing systems that
are available to all technical, political, and societal trend-setters. This will provide
fresh new possibilities and, as a result, greater growth. Modern pay laws and a
pluralistic financial system (e.g., fully different foreign currencies) will greatly
support the digital economy’s boundless potential for all purposes.
To deal with the volatility and distinctiveness of our future and convert it into an
advantage, we’d need personal digital employees. Computerized logic advancements
might likewise assist these automated coworkers. Numerous networks aggregating
individual and citizen awareness are expected to be freely established and altered
as needed in the future. These networks, on the other hand, should be spread with
the ultimate goal of maintaining control over our lives. In fact, you should be able
to log in and leave whenever you want.

250
Technological Innovation Driven by Big Data
THROUGH BIG DATA, REIMAGINING BUSINESS
INNOVATION, COMPETITIVENESS, AND PRODUCTIVITY
Big data has penetrated a wide range of industries and is now seen as a genuine
success driver, a cost advantage, and an increase in efficiency.
Big Data is at the centre of a technological revolution that has reached the world’s
most vulnerable reaches. The concept is defined by the 5 Vs: number, variation,
distance, actual value, and, most importantly, meaning. This final V is why Big
Data has become a partner in industries like manufacturing, retail, and healthcare,
as well as more conventional domains like government and even extortion and crime
detection. In a digitally oriented market, Big Data gradually becomes a cause of
innovation and money, as well as a tactic for gaining a strategic edge.
• Catalyst for Innovation
Advancement may be able to address several crucial issues, such as R&D costs,
customer acceptance, and the transition from early investors to the mainstream market.
• Create New Products
The foundation for studying and nurturing emergent patterns and ideas will
be big data. A corporation may identify unmet demands, niches that aren’t well-
established, and categorize marketing operations for the correct audience using
basic research equations.
The utilization of big data might assist the product in any stage of development,
from conception to delivery. The novel ideas are centered on online networking,
corporate news may recognize the necessity for the establishment of an open door
for a specific project, and existing databases can validate the concept and assist in
the definition of the template.
• Developing and Changing Model Businesses
When data becomes a commodity in and of itself, organizations may leverage
the information gathered for their existing operations to create or enhance their
value propositions. The very same sets may be valuable for competitors, comparable
businesses, and other organizations with similar consumer bases. Could a corporation
with a large data base set up a subsidiary and generate many revenue streams? For
example, a pharmacy clerk who keeps track of prescriptions dispensed may sell his
information to a health-research organization or even an insurance company.

251
Technological Innovation Driven by Big Data
• Increasing your Competitiveness
If a company’s golden customer group can be identified, its interests better
served, and its expenses are perfectly matched to its revenues, it can function to its
maximum potential. To improve speed, Big Data may change any of these variables.
• Creating Enough Segmentation
For patterns and cluster construction, big data computational knowledge streams
are used. Clients may be grouped into related sessions using this way. According to an
expert from Indata Labs Big Data Counsels, “by breaking down huge measurements
of seemingly unrelated information, calculations that detect trends and affiliations
and build customer segments separate yet pertinent for their behavior on the brand or
product from demographics or social data only”. Facebook used the phrase “lookalike
crowds” to describe a tool for finding similar consumer meets of companies that
are currently succeeding.
• Reputation on the Internet
Against competitors, a sentiment analysis of information eagerly transmitted by
consumers or admin in web-based networking sites should be available. Checking
your company’s comprehension, its capabilities, and their wants against the products
of competitors will add to new business. Scratching your brand’s online environment
may also reveal any negative comments or even unscrupulous practices that might
affect your business.
• Enhancement of the User Experience
I can only get a glimpse of who the clientele are and what they think of you by
looking at a larger picture. The genuine difficulty comes when firms seek to make
their customers happy than their competitors. You’ll study about the game both
online and offline, then adjust your bargain to their preferences based on what you’ve
learned. Cookies, external data, and census data are all permitted in this struggle.
Customers are aware of how firms collect information, and they want high quality
and low costs in return, so they are hesitant to give up any of their autonomy.
• Increasing Productivity Saving Time
Employees who keep track of their everyday routines are inefficient, but the
selecting and decision-making procedures that are based on their analysis will

252
Technological Innovation Driven by Big Data
certainly result in double-digit growth. During work sessions, certain mechanisms,
like device login identification, should be set up, so that after data collection and
analysis methods are developed, you can only operate on the basis of the guidelines.
• Cost-Cutting
Businesses will be able to see how money is spent and how wasteful decisions
may be avoided by examining cost centers using estimations. Even in highly regulated
industries like hospitals, public sector expenditures, or compliance, AI (Ghotbi et.
al. 2022) estimates will detect duplicates and propose lowering wasteful spending.
A thorough analysis of agreements might help you find better opportunities.
• Work is Flowing in New Directions
Some employees may include the use of data - driven decision as a new approach
to deal with their employment, yet it is typically more equitable, faster, and less
expensive. A business that is based on rather than management hunches, big data
has a better chance of growing quickly. Accepting the current working style might
be tough, but it can be made easier if the Association’s culture encourages self-
evaluation by providing personal record records instead of an assessment meeting.
QUESTIONS ABOUT THE IMPLEMENTATION OF BIG DATA
The biggest problem with big data right now is a scarcity of expertise. Approximately
7,000 computer tech scientists and computer engineers, as well as 9,000 support
employees including such data analysis associates and comparable openings, are
now available.
Confidentiality, external identity security, and the originator’s approval rights
are also important. These may not be hazards, but rather difficulties that can be
overcome, regardless of how long it takes for suitable anonymization techniques
and traceable data protection to be implemented.
BIG DATA: INNOVATION, COMPETITION, AND
PRODUCTIVITY’S NEXT FRONTIER
As long as the correct rules and facilitators are in place, big data will be the primary
strategic premise that drives future development in efficiency, output, and consumer
surplus.

253
Technological Innovation Driven by Big Data
Data measurements have increased as a result of research by MGI and McKinsey’s
Enterprise Technology Office, and processing enormous quantities of ostensibly
big data has become a critical prelude to innovation, which underpins new waves
of profitable growth, production, and consumer surplus. Massive numbers can be
Table 1. Technological Innovations of Big Data
Big data Features Date
Data Architecture
The feeding, cleaning, and analytics of data which is too big or
complex for conventional database systems may be managed via
big data frameworks.
Early 1990s
Data Governance
Data availability, usefulness, integrity, and security of data
utilized in an organization are processes and factors that are
managed by data governance. It covers every step from storing
the data to protecting it from any accidents. It involves more than
simply technology accountable for both the technology and the
specific dataset. Additionally, it is used in mature organizations
to guarantee the management and security of crucial data. This
improves the information’s clarity, which aids in outlining the
decision-making procedures using data. It is a lengthy, planned
procedure. For firms in the finance and insurance sectors,
particularly those that must adhere to regulations, it is crucial. To
oversee data throughout its life cycle, these companies are needed
to establish proper data management practices. Data governance
may also make it possible to authorize certain users based on
classified data.
2005
Master Data Management
The requirement for organizations to facilitate the stability and
excellence of their primary datasets, like commodity, resources,
clients, etc., gave rise to master data management (MDM).
1980s
Metadata Management
The practice of controlling the metadata regarding data is known
as metadata management. It defines and provides meaning to the
information assets in your company. Your data’s value is unlocked
by metadata, which makes it easier to use and locate. The context
that is necessary to comprehend and manage your company, data,
and systems is provided by metadata. It is simpler to identify and
utilize data and to offer the crucial data context that your business
and IT teams need when utilizing metadata management.
1986
Processing Infrastructure
Data Processing Infrastructure includes all VFS-owned, managed,
operating systems, cloud storages, networks, workstations,
servers, laptops, web applications, mobile devices, mobile apps
and websites.
1999
Data Analytics
Analyzing data collections to find patterns and take decisions
about the information they have is called data analytics. Data
analytics is achieved with the utilization of expert hardware and
software.
1962
Data Quality
Data completeness, precision, stability, dependability, etc are all
utilized to find the superiority of the data. Business firms may
find data inconsistencies which require to be established and find
if the data in their IT infrastructures is appropriate for the use by
calculating the data superiority levels.
1972
Data Integration
Bringing together data from several sources to provide people
a single perspective is known as data integration. Making data
more readily accessible, simpler to consume, and easier to use by
systems and people is the foundation of data integration.
1991

254
Technological Innovation Driven by Big Data
lived with by leaders in all businesses, not just a few innovation managers. The
volume and quality of information obtained by organizations, as well as digital
advancements, online networks, and the Internet of Things (Shahab et. al., 2022),
will fuel accelerated knowledge creation in the future.
MGI investigated big data in hospitals in the United States, the public sector in
Europe, retail in the United States, and industrial and personal field data all over
the globe. Value may be created from large data. For example, a retailer that fully
utilizes big data could see a 60 percent increase in operating output. Big data has
enormous promise in the public sector as well. If US health care were to use big data
properly and efficiently to increase productivity and safety, it might save more than
$300 billion each year. Our health spending will reduce this by approximately 8%.
Government authorities in Europe’s mature economies might save approximately
€ 100 billion by leveraging Big Data to reduce misrepresentations and errors, as
well as increase the collection of assessment taxes, alone, in enhancing operational
performance. Consumers of personal area data-enabled networks (Choudhary et.
al., 2022) will profit by more than $600 billion. The analysis provides access to
seven key pieces of information.
Data has been collected in every industry and firm, and it is a crucial growth
component, alongside labor and money. By 2009, we expect practically every sector
of the US economy to have 200 terabytes of stored data per firm with over 1000
workers (twice the size of Walmart’s data warehouse in 1999).
There are five typical methods for generating value from big data. Big data will
initially give up a great deal of value by making information more accessible. Second,
as organizations develop and maintain transactional records more consistently and
in depth on all types of product inventories and off-date, they will be able to catch
and expose volatility, hence increasing efficiency (Gupta et. al., 2022). Leading
companies use data gathering and analysis to carefully monitored testing in order
to make better management choices; others employ data for low-frequency estimate
at high frequency, and are actively adjusting market levers without free time. Third,
Big Data allows for more precise customer classification (Bhattacharya et. al., 2021),
resulting in more accurate personalized products and services. Fourth, advanced
analysis will significantly improve decision-making. Big data will finally be leveraged
to help create new products and services for the future generation. Manufacturers,
for example, utilize data obtained from sensors (Sinha et. al., 2021) in goods to
develop innovative after-sales solutions like maintenance work (preventative action
taken prior to or even detected a failure).
Individual firms’ success and expansion are predicated on the utilization of big data.
Big Data must be treated seriously by both enterprises in terms of competitiveness
and potential market gain. Electronic competitors and newcomers will employ data-
driven strategies to innovate, perform, and acquire value from precise and up-to-date

255
Technological Innovation Driven by Big Data
data in a variety of industries. In actuality, we identified early instances of such data
exploitation in every field we investigated.
The utilization of big data may allow new waves of productivity growth and market
excess. For example, we predict that merchants that use huge data in its totality will
be able to increase their operational plans by more than 60%. Customers, as well as
businesses and organizations, benefit greatly from big data. Customers may capture
an economic surplus of $600 billion through personal data suppliers, for example.
Because big data applications are cross-sectored, several disciplines are expected
to be included. We compared honest U.S. company competitiveness to those sectors’
capacity to gather Big Data (to use an Algorithm incorporating several quantitative
indicators) performance and discovered that opportunities and challenges vary by
business sector. Finance and insurance, as well as the digital and electrical products
and information sectors, are ready for substantial use of big data.
There’d be a lack of capabilities for organizations to utilize big data (Bansal et. al.,
2022). Only between 140,000 and 190,000 people with a strong sense of reasoning
will be available in the United States by 2018, while 1,5 million executives and
analysts will employ big data analysis for effective decision-making (K.aushik et.
al., 2021) objectives.
To fully use the potential of big data, a number of difficulties must be overcome.
In the big data context, security, safety, intellectual property, and even risk tactics
should be examined. Instead of just producing the necessary skills and technology
(Pramanik et. al., 2022), assemblies must now arrange work procedures and benefits
to enhance the utilization of big data. Access to data is critical to any organization,
and resources must be allocated to make it possible to incorporate data from a variety
of sources, including outsiders.
CONCLUSION
In terms of data and analysis, the industrial sector has progressed far enough, but not
far enough. “There is no potential for even a large number of managers to see and
know what Big Data can accomplish and revolutionize the way they work”, MIT
Professor Eric Byrnjolfsson wrote only this month. Manufacturers that lack a long-
term view will be significantly disadvantaged in their current business environment.
Instead of returning highly educated individuals to rival markets, you believe it is
more acceptable to keep more roles offshore. Basic information would be buried,
making it impossible to enhance or grow productivity without the presence of
equipment or staff. They are seen as less reliable development network partners.
The parade of actual horrors starts - by the end of the day; they will have left it
behind rivalry for a long time. Due to the expansion of informal network sites, search

256
Technological Innovation Driven by Big Data
engines, online distribution platforms, financial markets, news channels, and other
information sites, data is being computed rapidly throughout the globe. Big Data
is the newest frontier in science and market research. Big data analysis is critical
for organized knowledge discovery in the context of sometimes occurring patterns
and hidden rules. Big Data Analysis assists firms in making strategic decisions,
predicting and recognizing trends, and identifying prospective business opportunities.
In this article, we look at issues and challenges with big data mining as well as big
data analysis tools like Map Reduce over Hadoop and HDFS, that help businesses
better understand their customers and markets and make better choices. We do,
however, provide various large-scale data mining approaches as well as advice on
how to extract useful information from huge data. This would aid researchers in
selecting the appropriate digging instruments for their projects. We handled the many
possibilities that’d result in smart apps employing the framework to increase their
operations and efficiency by using every accessible date as a consequence of this.
We also addressed a slew of concerns and identified a number of roadblocks to the
expansion of big data applications. We suggested that the generic standards for Big
Data intelligent city apps be revised to concentrate on the discussion. It is necessary
to develop and execute effective and dependable systems. Furthermore, these criteria
often seek to resolve concerns by recommending other approaches to conquering
some of the challenges and achieving better results. Finally, we discussed some of
the basic, transparent issues that need to be further investigated and addressed in
order to have a better knowledge of intelligent towns.
REFERENCES
Argent, R., Hetherington-Rauth, M., Stang, J., Tarp, J., Ortega, F. B., Molina-Garcia,
P., Schumann, M., Bloch, W., Cheng, S., Grøntved, A., Brønd, J. C., Ekelund, U.,
Sardinha, L. B., & Caulfield, B. (2022). Recommendations for Determining the
Validity of Consumer Wearables and Smartphones for the Estimation of Energy
Expenditure: Expert Statement and Checklist of the INTERLIVE Network. Sports
Medicine (Auckland, N.Z.), 52(8), 1817–1832. doi:10.100740279-022-01665-4
PMID:35260991
Bandyopadhyay, S., Goyal, V., & Dutta, S., Pramanik, S. ,& Sherazi, H. H. R. (2021).
Unseen to Seen by Digital Steganography. In S. Pramanik, M. M. Ghonge, R. Ravi
and K. Cengiz, (Eds.), Multidisciplinary Approach to Modern Digital Steganography,
pp. 1-28. IGI Global. doi:10.4018/978-1-7998-7160-6.ch001

257
Technological Innovation Driven by Big Data
Bansal, R., Jenipher, B., Nisha, V., & Makhan, R. Kumbhkar, Pramanik, S., Roy, S.
& Gupta, A. (2022). Big Data Architecture for Network Security. In Cyber Security
and Network Security. Wiley.
Bhattacharya, A., Ghosal, A., Obaid, A. J., Krit, S., Shukla, V. K., Mandal, K., &
Pramanik, S. (2021). Unsupervised Summarization Approach with Computational
Statistics of Microblog Data. In D. Samanta, R. R. Althar, S. Pramanik, & S. Dutta
(Eds.), Methodologies and Applications of Computational Statistics for Machine
Learning (pp. 23–37). IGI Global. doi:10.4018/978-1-7998-7701-1.ch002
Choudhary, S., Narayan, V., Faiz, M., & Pramanik, S. (2022). Fuzzy Approach-Based
Stable Energy-Efficient AODV Routing Protocol in Mobile Ad hoc Networks. In M.
M. Ghonge, S. Pramanik, & A. D. Potgantwar (Eds.), Software Defined Networking
for Ad Hoc Networks. Springer. doi:10.1007/978-3-030-91149-2_6
Dushyant, K., Muskan, G., Gupta, A., & Pramanik, S. (2022). Utilizing Machine
Learning and Deep Learning in Cyber security: An Innovative Approach. In M.
M. Ghonge, S. Pramanik, R. Mangrulkar, & D. N. Le (Eds.), Cyber security and
Digital Forensics. Wiley., doi:10.1002/9781119795667.ch12
Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards
ethical issues of artificial intelligence in an international university in Japan. AI &
Society, 37(1), 283–290. doi:10.100700146-021-01168-2
Goh, G. L., Dikshit, V., Koneru, R., Peh, Z. K., Lu, W., Goh, G. D., & Yeong, W. Y.
(2022). Fabrication of design-optimized multifunctional safety cage with conformal
circuits for drone using hybrid 3D printing technology. International Journal of
Advanced Manufacturing Technology, 120(3-4), 2573–2586. doi:10.100700170-
022-08831-y
Gupta, A., Verma, A., & Pramanik, S. (2022). Security Aspects in Advanced Image
Processing Techniques for COVID-19. In S. Pramanik, A. Sharma, S. Bhatia, & D. N.
Le (Eds.), An Interdisciplinary Approach to Modern Network Security. CRC Press.
Kanan, T., Mughaid, A., Al-Shalabi, R., Al-Ayyoub, M., Elbes, M., & Sadaqa, O.
(2022). Business intelligence using deep learning techniques for social media contents.
Cluster Computing. Advance online publication. doi:10.100710586-022-03626-y
K.aushik, D., Garg, V., Annu, Gupta, A. & Pramanik, S. (2021). Application of
Machine Learning and Deep Learning in Cyber security: An Innovative Approach.
In M. Ghonge, S. Pramanik, R. Mangrulkar and D. N. Le, (Eds.), Cybersecurity
and Digital Forensics: Challenges and Future Trends. Wiley.

258
Technological Innovation Driven by Big Data
Koren, Y., Rendle, S., & Bell, R. (2022). Advances in Collaborative Filtering. In F.
Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook. Springer.
doi:10.1007/978-1-0716-2197-4_3
Madanian, S., Nguyen, H. H., & Mirza, F. (2021). Wearable Technology. In D. Gu &
M. E. Dupre (Eds.), Encyclopedia of Gerontology and Population Aging. Springer.
doi:10.1007/978-3-030-22009-9_459
Nyangaresi, V. O. (2022). A Formally Validated Authentication Algorithm for
Secure Message Forwarding in Smart Home Networks. SN. Computer Science, 3,
364. doi:10.100742979-022-01269-9
Pramanik, S. (2022). An Effective Secured Privacy-Protecting Data Aggregation
Method in IoT. In M. O. Odhiambo, W. Mwashita, & I. G. I. Global (Eds.),
Achieving Full Realization and Mitigating the Challenges of the Internet of Things.,
doi:10.4018/978-1-7998-9312-7.ch008
Pramanik, S., & Bandyopadhyay, S. (2022). Analysis of Big Data. In I. G. I. John
Wang (Ed.), Encyclopedia of Data Science and Machine Learning. Global.
Pramanik, S., Joardar, S., Jena, O. P., & Obaid, A. J. (2022). An Analysis of the
Operations and Confrontations of Using Green IT in Sustainable Farming. 2nd
International Conference on Modern Applications of Information and Communication
Technology, Al-Kadhum, Iraq.
Schloemer, T. H. (2022). Managing big data. Nature Energy, 7(2), 122–123.
doi:10.103841560-021-00972-w
Shahab, S., Agarwal, P., Mufti, T., & Obaid, A. J. (2022). SIoT (Social Internet
of Things): A Review. In S. Fong, N. Dey, & A. Joshi (Eds.), ICT Analysis
and Applications. Lecture Notes in Networks and Systems (Vol. 314). Springer.
doi:10.1007/978-981-16-5655-2_28
Sinha, M., Chacko, E., Makhija, P., & Pramanik, S. (2021). Energy Efficient Smart
Cities with Green IoT. In C. Chakrabarty (Ed.), Green Technological Innovation
for Sustainable Smart Societies: Post Pandemic Era. Springer. doi:10.1007/978-3-
030-73295-0_16
Welc, J. (2022). Financial Statement Analysis. In Evaluating Corporate Financial
Performance. Palgrave Macmillan. doi:10.1007/978-3-030-97582-1_3

259
Technological Innovation Driven by Big Data
Zamani, E. D., Griva, A., & Conboy, K. (2022). (2022). Using Business Analytics for
SME Business Model Transformation under Pandemic Time Pressure. Information
Systems Frontiers. Advance online publication. doi:10.100710796-022-10255-8
PMID:35250366