SIGNIFICANCE OF BIG DATA ADVENTS IN BUSINESS MANAGEMENT AND INNOVATION

Big data analytics 1
SIGNIFICANCE OF BIG DATA ANALYTICS IN BUSINESS MANAGEMENT AND
INNOVATION
Executive Summary
Big data analytics describes a complex procedure of assessing vast and extensive
variety types of facts. Big data analytics aims to unearth the concealed facts (Evelles,
Fukawa & Swayne, 2016). The facts may include customer preferences, hidden
correlations and market trends. It is one of the most significant tools that organisations
should have to assist the managers to make informed decisions. Big data is an
expression that is used to make a distinction amid the new type of data and the old
structured form of data. An organisation cannot rely only on its information from the
internal sources, and it must explore the external environment and gather data that will
be used to make critical and innovative decisions (Evelles, Fukawa & Swayne, 2016).
A vast number of research studies have given an attempt to provide the connotation of
the word big data. It is described with three "Vs" (Bhatnagar & Kurnar, 2015). There is
need to utilize other resources such as Big Data infrastructure to reap the benefits of big
data analytics (Chen & Zhang, 2014). The use of big data analytics can help a business
flourish in the market. As a result, there is the creation of employment opportunities
which leads to an improvement in the living standards of people in various countries
(Rajaraman, 2016). The paper begins with an introduction, followed by research rational
and research questions. A review of literature is then conducted followed by
methodology, ethical considerations and study limitations are presented.

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Table of Contents
Executive Summary.....................................................................................................................................1
The significance of Big Data Analytics in Business Management and Innovation........................................3
Chapter 1: Introduction...............................................................................................................................3
1.1 Introduction.......................................................................................................................................3
1.2 Research Topic...................................................................................................................................4
1.3 The Significance of the Research Topic..............................................................................................4
1.4 Rationale of the Research Project......................................................................................................5
1.5 Research Questions...........................................................................................................................6
1.6 Research aim and Objectives.............................................................................................................7
General Research Question.....................................................................................................................7
Specific Research Questions....................................................................................................................7
1.7 Research Hypothesis.........................................................................................................................7
2.0 Literature Review..................................................................................................................................8
2.1 Role of external data in innovation for products and services.........................................................10
2.3 Big data playing its role in keeping business from jumping to conclusions.....................................11
2.4 Role of big data in identifying patterns, correlations and trends.....................................................12
2.5 Role of big data in using to conduct predictive analytics.................................................................12
2.6 Big Data plays the role of considering the downsides of analytics..................................................13
2.7 Big Data and big data analytics........................................................................................................13
Chapter 3: Research Methodology............................................................................................................16
3.0 Methodology.......................................................................................................................................16
3.1 Research philosophy........................................................................................................................16
3.2. Data collection................................................................................................................................16
3.3 Research Design...............................................................................................................................17
3.4 Data Analysis...................................................................................................................................17
3.5 Importance of the study..................................................................................................................17
3.6 Ethics...............................................................................................................................................18
3.7 Limitations.......................................................................................................................................19
3.8 Chapter Summary............................................................................................................................19

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Chapter 4: Findings and Analysis...............................................................................................................20
CHAPTER 6: CONCLUSION.........................................................................................................................24
6.1 Conclusion.......................................................................................................................................24
6.2 Recommendation:...........................................................................................................................24
Reference List............................................................................................................................................26

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The significance of Big Data Analytics in Business Management and Innovation
Chapter 1: Introduction
1.1 Introduction
Big data analytics describes a complex procedure of assessing vast and
extensive variety types of facts. Big data analytics aims to unearth the concealed facts
(Evelles, Fukawa & Swayne, 2016). The facts may include customer preferences,
hidden correlations and market trends. It is one of the most significant tools that
organisations should have to assist the managers to make informed decisions. Big data
is an expression that is used to make a distinction amid the new type of data and the old
structured form of data. An organisation cannot rely only on its information from the
internal sources, and it has to explore the external environment and gather data that will
be used to make critical and innovative decisions (Evelles, Fukawa & Swayne, 2016).
However, the data collected from the outside sources are generally in large quantities,
and in different types. This is why there is a need for big data analytics that helps to
analyse the big data and come up with information that is helpful to the organisation.
This research project is a detailed investigation of the significance of Big Data
Analytics in today’s business management and innovation. Major purpose of the paper
is to understand and gain a comprehensible insight regarding how organizations in the
present days are dealing with innovation when managing service and business with Big
Data or how Big Data is helping the businesses today to manage innovation and quality
of service in business. Under general view, it can be mentioned that big data analytics
tend to reveal the hidden patterns, correlations and other significant understanding. So,
due to the advances of technology today, it is quite easy to make analysis of the data
and gain suitable responses on an urgent basis which is slower as well as less efficient
with more traditional business intelligence solution. It is a known fact that each type of
data tend to contain figures, facts and the generate information to take better

Big data analytics 5
organizational businesses, career as well as regular decisions. Generally, all sort of
data is significant to businesses but when it comes to innovation and creative ideas, big
data helps to enhance innovative as well as creative ideas. It is always important to
develop creative ideas in business to achieve goals and due to this reason big data is
significant to discover the appropriate type of solutions that consumers tend to look for.
1.2 Research Topic
The topic of the research will be an investigation into the significance of big data
analytics in business management and innovation.
1.3 The Significance of the Research Topic
A large amount of data available from external environments is becoming a big
challenge to many organisations in the world today (Hashem et al., 2015). It has
become difficult to make the right choices on the information to use in their decision
making. Big data analytics helps the managers of the organisations to analyse the big
data to come up with well-structured details that can be used in decision making. Big
data analytics also helps the managers to identify hidden information, especially about
other competing businesses.
In today's dynamic business environment, innovation has become a decisive tool
needed by firms to make certain their continued existence in the marketplace. The
owners of business and managers therefore, need to be modernized with the dynamic
technology (Hashem et al., 2015).
They have to ensure that they provide new services, find ways of producing and
introducing new products in the market. The only way that they can do this is by having
the ability to right of entry to both external and internal environment and make analysis
to know the requirements of the market from the organisation.
1.4 Rationale of the Research Project
A vast number of studies show that analysis on the big data have had a strong
bang in increasing the innovation of many firms. Conversely, BDA is not adequate to

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ensure that an organisation experiences increased growth and development (Hazen et
al., 2014). To create big data analytics capabilities in a firm, there are numerous of other
types of resources such BDA infrastructure and BDA personnel that the organisation
needs to employ. Big data analytics capabilities describe the effects that big data
analytics create to a firm's modernization. Also, there are other factors that a company
should consider to ensure big data analytics (Hazen et al., 2014). One of the main
factors is an organisation’s absorptive capacity. This describes the ability of a firm to
acquire, implement and exploit the information acquired to ensure growth and
development of the firm (Hazen et al., 2014). Absorptive capacity helps a company to
gain a competitive advantage against their competitors (Hazen et al., 2014). This
research project aims at investigating the significance of big data analytics in business
management and innovation. The investigator hopes to accomplish the key objective of
the study by: finding out the impact of significant data analytics capabilities on an
organisation's innovation sector; examining the importance of absorptive capacity to
organisations' innovation; investigating on the function played by absorptive capacity in
shaping the association between big data analytics capabilities and improvement sector
of an organisation. Meta-analysis methodology will be used by the investigator to come
up with the relevant information on the study. Various web pages, relevant previewed
journals, and organisational reports will be used to seek more information about the
research topic. To identify the relevant sources, the following keywords will be used:
Innovation, big data, business management, analysis on the big data, ability to absorb
and impacts of big data analytics.
Next, the researcher will identify the superlative references which counterpart the
criteria for inclusion-exclusion. Lastly, there will be monitoring of the prose for chosen
references to search on the significance of analysis of big data in business
management and innovation.
1.5 Research Questions
The objective of the study is to examine the significance of big data analytics in
business management and innovation. The researcher is aggravated by knowing that

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there is narrow study on how firms should exploit analysis of big data to achieve its
benefits on the business' profitability in the market the vast amount of information from
the external sources is becoming confusion to many companies when it comes to
making critical decisions. Organisations should adopt the use of big data analytics as
one of the most vital resources that can help create a competitive force against their
competitors. Studies have shown that despite the intense competition and many
challenges in the business world today, many firms have been able to ensure their
survival in the market (Gunesekaran et al., 2017). Most of these firms have been able to
adopt big data analytics that has helped them on how to deal with various challenges
that exist in the competitive world of business today.
Nevertheless, big data analytics is not sufficient for organisations to guarantee
big data capabilities in improving the innovation sector (Gunasekaran et al., 2017).
Organisations need to utilize other resources such as BDA technology and personnel.
Other factors such as the absorptive capacity of the firm should be considered to
enhance the capability of the organisation in exploiting big data analytics. Therefore,
this study aims at examining the impact of absorptive capacity on the organisation’s
modernization. Also, the research also objects at the discovery of the function of
absorptive capacity in shaping the affiliation amid the impacts of analysis of big data
and a firm’s modernisation.
1.6 Research aim and Objectives
This research aims to investigate the significance of Big Data Analytics in leading and
managing business innovatively. Following are the key objectives to meet the stated
aims.
 To critically investigate the impact of big data analytic capabilities on an
organization’s innovative managerial initiatives
 To identify the importance of absorptive capacity in organization’s
innovative business
 To analyse the future role of Big Data analytics in business

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General Research Question
What is the significance of big data analytics in business management and innovation?
Specific Research Questions
1. What is the impact of big data analytics capabilities on an organisation's
innovation sector?
2. What is the importance of absorptive capacity in the organisation's innovative
sector?
3. What is the function of absorptive capacity in shaping the affiliation amid big data
analytics capabilities and business innovation?
1.7 Research Hypothesis
H0-Big Data Analytics have no impact on business management and innovation
H1- Big Data Analytics have impact on business management and innovation

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2.0 Literature Review
A vast number of research studies have given an attempt to provide the
connotation of the word big data. It is described with three "Vs" (Bhatnagar & Kurnar,
2015). The first "V" is used to describe the volume of the big data. It is said to be in
huge, and it comes from different types of sources both internal and external. The
second "V" is used to describe the variety of big data. Studies show that it is of a vast
number of varieties and it ranges from structured data to unstructured data. This is one
of the main reasons why it creates a big challenge for firms when it comes to making
critical decisions. The third "V" represents the velocity of the big data. This refers to the
speed at which big data is produced, analysed and interpreted.
Big data need to be analysed to be of importance to the organisations. This is
where big data analytics arises. It involves a complicated procedure for assessing
enormous amounts of big data originating from diverse sources to expose the unseen
facts (Akter et al., 2016). The information may include consumer preferences, unknown
correlations, information about competitors and market trends. Besides, big data
analytics helps in unveiling information about what happened in the past and what is
happening today in the market (Akter et al., 2016).
This information is used by the managers and the owners of the business to
predict what will happen in the future and the possible outcomes. According to the
studies, there are three types of data analytics: Descriptive data analytics, predictive
data analytics, and prescriptive data analytics. Descriptive data analytics involves data
analysis on the occurrences that occurred in the past.
Predictive data analytics requires analysis of data to predict what is probable to
occur in the coming days (Ji-Fan Ren et al., 2017). Prescriptive data analysis involves
analysing various types of data from different sources to help the organisation make
informed decisions to achieve its goals in the future.
Besides, there is a vast number of big analytics tools that a firm can employ.
Some of them are extracted information load, Hadoop distributed file systems and Data
warehouse tools (Ji-Fan Ren et al., 2017). There is a need for organisations to know the

Big data analytics 10
meaning of big data and big data analytics to achieve the big data analytics capabilities
in improving their innovation sector. Many firms have been struggling to make decisions
with the wide variety of data from both external and internal sources (Kwon, Lee & Shin,
2014). This data may lead to wrong decisions that may lead to the failure of the
business. Organisations need to create a competitive advantage against their
competitors and increase their efficiency for them to survive in the business
environment.
Conversely, an organisation cannot achieve the capabilities of big data analytics
without using other resource. There is need to utilize other resources such as Big Data
infrastructure to reap the benefits of big data analytics (Chen & Zhang, 2014). Big data
analytics infrastructure may include infrastructure that is composed of information
technology which helps the sectors of the company to work as a team to attain the main
objective of the business. Also, BDA personnel expertise is another type of big data
analytics infrastructure. These include the knowledge, professional skills, and familiarity
with big data analytics (Cheng & Zhang, 2014). A firm can attain big data analytics
capabilities if it is able to combine all the required resources to create a competitive
advantage.
The absorptive capacity of a firm is increased by its ability to apply big data analytics in
its decision-making process. The ability of an organisation to acquire information,
analyse it, interpret and utilize it to improve the firm is known as absorptive capacity
(Kwon, Lee & Shin, 2014). There are two types of absorptive capacity: Potential
absorptive capacity and realised absorptive capacity.
Potential absorptive capacity refers to the process of acquiring information from external
sources, interpreting and analysing the obtained data (Jin et al., 2015). Realised
absorptive capacity involves the ability of a company to bring the newly received data
and the old information together. It also requires implementation of the acquired
knowledge and exploiting it to create a competitive advantage that can help ensure its
survival in the competitive market (Jin et al., 2015). Absorptive capacity is one of the
critical tools for achieving big data analytics capabilities by an organisation.

Big data analytics 11
Moreover, absorptive capacity is also one of the factors that lead to a firm’s
improvement. Modernization involves the introduction of updated goods, systems and
the firm’s structure. (Lazer et al., 2014). Absorptive capacity helps a firm to acquire
information from the external sources, analyse it through big data analytics and exploit it
to come up with informed, innovative business decisions. This shows that the absorptive
capacity has a big role in determining the big data analytics capabilities of a firm with its
innovation.
It has been identified that chief executives are shifting their attention from product
innovation to service innovation due to increasing commoditization of product items and
increasing consumer demands for customized experience of service or product. Hence,
to remain active or continue the service innovation, organizations or the businesses
often rely on big data management. Questions often arise that why it is necessary to
maintain sustainability in innovation in business as there is no good in investing in time,
money, and resources to think tank which cannot do much on producing a steady
stream of innovation on a daily basis. Hence, Lee, Kao and Yang (2014) commented
that it is quite true that every business or marketers need to achieve and maintain a
state of regular as well as consistent innovation.
Provost and Fawcett (2013) also mentioned the fact that several forward thinking
businesses often find that big data tend hold long series of answers to drive innovation
and one major reason for this is that internal data with possibility can drive
organizational innovation. Tiwari, Wee and Daryanto (2018) arguably added the fact
that innovation management is not just coming up with creative ideas. Much of
innovation is just made up of smaller or limited improvements which tend to combine to
create colossal impact. Authors of this study has also given an example that if a
business is able to minimize 10% of overall operation’s waste and make 15% of
enhancement in production time. By reducing or eliminating a bottleneck and
streamlining few process, together it can enhance operations without even
manufacturing single new revolutionary data. This is probably the major power of using
big data in your internal operations. It is certain that small innovations tend to add up to
large improvement.

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2.1 Role of external data in innovation for products and services
Just like the above studies, Braganza et al. (2017) performed a study and mentioned
the fact that big data could not only give answers to the questions regarding the market,
customers, industry and competitors, it has the ability to bringing up a series of other
similar questions that help to set innovation under the path of developing new concepts.
Wolfert et al. (2017) added the fact that big data is known for generating as many or
more questions than it could actually responds. Here, the major fact to be understood is
that while it can be frustrating for teams who are tasked with resolving specific
problems, it could be so much effective for the businesses assigned with innovation
management. Wixom et al. (2014) added the fact that big data has the ability of
delivering some effective revelations which can be used for all sort of ways.
In this context, Gobble (2013) gave an example that it is appropriate to state the fact
commuters who go to work and then routinely get home on time everyday are probable
much more happier than who drive their own cars. Authors of this study has mentioned
the fact that people in Tokyo tend to sleep less compared to the people in other nations
across globe and people living in Texas are more likely to call each other bro and dude.
So, on the basis of such data and information, innovators gain the ability of delving into
such data and discover all sorts of intriguing and fascinating facts. And, the findings
draw the implications that each of these often leads right back to few more questions.
Thus, it is mentioned that this is the major sustenance of a healthy innovation program
in business.
2.3 Big data playing its role in keeping business from jumping to conclusions
According to Ittmann (2015), it might happen that data was considered to be something
often proved to be different than it was actually considered. Authors also gave an
example that there could be sometimes the data which is originally very different from
what organizations conventionally consider to be true. Just like the markets for
desktops, Personal Computer remained stagnant that marketers assumed that this was
going to be an end of desktop computer. According to LaValle et al. (2011), Microsoft
gave a challenge on the fact that when they developed Windows 8, which was a big
disaster or more of a bad marketing initiative. However, in reality the picture is little

Big data analytics 13
different which means in reality, tablets, smartphones were new and some demographic
factors were also introduced until the market reaches the saturation. Moreover, it was
also noted that many have rejected their desktop computers for the quest of some
portable devices but it is true that some activities cannot be performed properly.
. Consequently, it was identified that several abandoned their desktop computers for the
quest for smaller or more portable devices but many activities simply cannot be done on
a small touchscreen. This happens because big data can be used to prevent several of
the assumptions as it has the ability to state what actually behind the scene is. It is
certain that data analytics could determine patterns as well as correlations that are
impossible for humans to make anticipations.
2.4 Role of big data in identifying patterns, correlations and trends
As put forward by Bughin, Chui and Manyika (2010), big data is one such thing which
can help to find discover the hidden correlations, identify patterns as well as trends
before they become obvious in their “naked eyes”. In this context, Nedelcu (2013)
added the fact that there could be some facts where innovation can be observed as the
action of combining such product with some similar concept under a one to one
process. In this context, Nedelcu (2013) added the fact that sometimes innovation is all
about combining such product with such similar concept with another. In this context,
Gandomi and Haider (2015) added the fact that big data is more an active player of
finding patterns and leading innovation team go down. Suitable example found in this
article which indicates that data analytics could examine a long series of scenarios
which are related to identify the ideal blend of watering, fertilizer as well as other
conditions to manufacture and better corn corps. Likewise, Raghupathi and Raghupathi
(2014) performed a study and added the fact that healthcare service providers could
apply big data to make analysis of different patterns such as their medical conditions
and treatment is effective for some specific patient which are in general based on
factors like, age, sex, ethnicity or the status to know how far the disease has spread and
such insight could lead to a spectacular innovation which might provide the poorer with
food, and save the life of people.

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2.5 Role of big data in using to conduct predictive analytics
As put forward by Chen, Chiang and Storey (2012) innovation is a significant matte of
anticipating what could be the next big thing, which is going to take place. According to
the authors of this study, data analytics have the ability of harnessing the power of big
particularly for enhancing predictive analytic tools which goes ahead as well as predict
the future both within as well as outside the business. In this context, Russom (2011)
mentioned the fact there is a question that whether the existing market for the product
hold steady or should just back the business off on production in coming year. In this
context, Mahmood and Afzal (2013) mentioned that predictive analytics tend tip off
business innovation team and keep your production, R&D and other informed
departments.
2.6 Big Data plays the role of considering the downsides of analytics
It has been identified that while much of big data is for the betterment of all, certainly
there could be downside to use of data analytics. Chen and Zhang (2014) performed a
study and talked about what happens when big data is so effective about at predicting
who could enhance diabetes or develop chances of having stroke that insurance
organizations refuse to provide health insurance to people. Thus, the authors have
suggested that it is always important to consider the downside to use big data for the
purpose of innovation. According to Elgendy, and Elragal (2014) it is more of ethical
responsibility of leveraging such powerful tool. In a similar way, Wang et al. (2016)
added the fact that big data can be used for all sort of innovations in businesses, the
real limit is what one could imagine when doing with the analytics. It is certain that
innovation could never the same when it comes to effective innovation.
2.7 Big Data and big data analytics
As put forward by Loshin (2013) technological advancement in the tools and techniques
of business analytics provide unprecedented access to large amount of data which goes
beyond business transactions bid data. On the other side, Bates et al. (2014)
performed a study and added the fact that big data tend to describe data which are
generated from an increasing plurality of sources with the inclusion of internet and

Big data analytics 15
mobile transactions as well as user generated content, social media and deliberately
generated content through sensor networks. Findings of this study also enable the
organizations to processes and analyse such trace data, which could involve website,
social media and online communication. Big data is widely used in business context, Hu
et al. (2014) mentioned that firms tend to use data trails from digitalized objects just as
the sensor equipped, cell phones as well as some other devices.
According Dittrich and Quiané-Ruiz (2012), web-based as well as sensor data are
widely generated in great volumes at high velocity in a much wide veracity. In this
context, Lee, Kao and Yang (2014) also mentioned the fact that huge amount of
information regarding consumers from the sources that reside inside as well as outside
the business or organization provides crucial sources for innovation in general as well
as variety of opportunities for the innovation of service in particular. For example,
insurance organizations tend to offer consumers with electronic data recorders for the
use in cars to gather detailed information (Provost and Fawcett 2013). Consequently,
Tiwari, Wee and Daryanto (2018) added the fact that in order to capitalize on these
opportunities for innovation, executives must have to understand Big Data Analytic
technologies, Nonetheless, the questions often arise like what is that potential of Big
Data Analytics in particular context of use with specific objectives like the innovation in
service with the focus of creating customer values. In contrast, Braganza et al. (2017)
mentioned the fact that materiality and affordance is referred to those properties and
features of information technology artefacts like the information technology, system
software and particular algorithm which have some abilities across the context as well
as across time. And these are often known and remained as continuants. Therefore,
Wolfert et al. (2017) in their study identified that the materiality of BDA technologies with
respect to hardware which is physical materiality and software like the digital materiality
and can include for example like in-memory technologies, data lakes and packages of
software like the Python and R allowing the predictive analytics.
As put forward by, Gobble (2013), raised the question of how material features of digital
technologies allow for innovation. Authors have concluded that the concept of
affordance can be become the predominant way of theorizing about the action
possibilities delivered by material features of information technology. Findings of this

Big data analytics 16
study certainly clarify the fact that affordance are potential for the actions that can arise
from the technical objects just like the organization that tries to innovate their service. In
this context, Ittmann (2015) mentioned that users as well as use groups tend to interpret
technical objects particularly following the objectives that are influenced by the
organizational context with the inclusion of customers, strategies, values, competitive
environment and regulations. In an extended study of this topic, Bughin, Chui and
Manyika (2010) mentioned that the material feature of business technique management
tools and dashboards for example, afford visualization of whole work process, features
of knowledge sharing, acquisition and maintenance and retrieval afford virtual
collaboration. Findings of this study give a clear indication that organizations with the
use of big data analytics technology are always few steps ahead in terms of processing
the businesses –which means collecting customer information, designing service and
designing the whole business process.

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Chapter 3: Research Methodology
3.0 Methodology
Research methodology is one of the significant aspect of research and it majorly
determines the outcome of the study. Thus, in the present study, research methods are
applied in accordance to the requirement of the topic. Following sections cover the
research methods that have been applied to study.
3.1 Research philosophy
Research philosophy is set of beliefs which helps to make judgement about a given
context with multiple perspectives. Research methods are divided into three different
categories namely positivism, interpretivism and realism. Positivism believes that reality
is stable and other means are secondary to the context; this philosophy is more of
rational and based on logics (Hughes and Sharrock 2016). On the other side,
intepretivism gives importance to other means of the world while equally appreciating
real-world facts. However, the application of interpretivism research philosophy is little
because unlike positivism in research philosophy, in interpretivism, theories are formed
at end after the observation of facts. In the present interprevism research philosophy
has been selected because this research philosophy helps to form theories and draw
conclusion regarding the significance of Big Data analytics in business after observing
the facts and findings.
3.2. Data collection
In order to collect data for the study, Google search has been used as the major source.
Some specific keywords related to bid data analytics have been used in the sources.
The key terms include: Business management, big data, improvement or innovation,
capabilities of the big data analytics and the organisation’s absorptive capacity. In the
next step, the criteria of inclusion –exclusion to come up with the most appealing
sources have been used. The data sources are selected on the basis of first twenty
sources by screening them. In the next stage, the investigator determined if the
references have wide-ranging data on analytics and business innovation. By doing this,
the references that mention the keywords of the topic but have little information about
the topic have been avoided. In the last step, the examiner monitored the pros and cons

Big data analytics 18
of each selected reference to investigate on significance of big data analytics in
business management and innovation.
3.3 Research Design
The main research design to be used in the study will be meta- analysis.
According to Saunders (2011), Meta-analysis research design refers to an approach
where important qualitative and quantitative data from a number of studies are
combined to come up with one conclusion that is more relevant to the research topic.
The main objective of the study will be to look into the significance of big data analytics
in business management and the innovation sector of organisations. The researcher will
achieve the aim of the study by looking in to the effects of immense data capabilities on
the firm's modernization sector. Also, he hopes to accomplish the primary objective of
the study by investigating the importance of absorptive capacity in an organisation's
innovation sector.
Moreover, researcher looks at the function of absorptive capacity in the
determination of the affiliation amid the capabilities of the big data analytics and
business innovation. Various web pages, previewed journals, and organisational reports
could be used for more information on the research topic.
The data used must come from convincing sources and must have widespread
prose on big data analytics and business management and innovation.
3.4 Data Analysis
Analysis of data will be done using the information found in the chosen
references according to the strength portrayed in supporting the research area. The
type of the variables to be used in analysing the data will be determined by the type of
information unruffled from various references. The researcher will use NVivo 11
software to determine the main themes of the study and carry out the coding of the
collected data.

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3.5 Importance of the study
Research area “An investigation of the significance of big data analytics in
business management and innovation" is significant to the world's economic growth and
development.
Big data analytics is an important tool that any business may require to
guarantee continued existence within the today's changing business world (Mulller et
al., 2018). It helps business managers make informed decisions in the organisations.
Managers are the top decision-makers in most of the firms. The rate of innovation of an
organisation highly depends on the strength of the innovative decisions made by the
managers (Muller et al., 2018). The use of big data analytics can help a business
flourish in the market.
As a result, there is the creation of employment opportunities which leads to an
improvement in the living standards of people in various countries (Rajaraman, 2016).
Moreover, growth and development of organisations also increase the GDP of the
nations where the organisations are located.
Besides, the research is important since the existing scarcity in the study about
the subject. Numerous businesses don’t understand how to utilise big data analytics to
generate a active institutional competence. There is a necessity to create awareness
that the capabilities of the big data cannot be produced using only the big data
analytics. Firms need to use other resources and also consider factors such as their
absorptive capacity (Rajaraman, 2016). This is to ensure that they reap the benefits of
using big data analytics by creating a strong competitive force against their competitors
in the business environment. The research study aims at providing adequate literature
on the significance of big data analytics in business management and improvement.
In the study, researcher focuses in looking in to the impact of capabilities of the
big data analytics in the firms' innovation sector. It will also investigate the importance of
absorptive capacity to an organisation's innovation sector. Moreover, the study will also
look in to the function of absorptive capacity in shaping the affiliation amid big data
analytics capabilities and improvement in various organisations.

Big data analytics 20
3.6 Ethics
Before carrying out the research study, the investigator will first present the
objectives and the topic of the study to the University. If the School gives consent to the
student to go ahead with the research work, the researcher will conduct the study.
Moreover, he will also ask for permission from different authors to be given the
opportunity to criticize and analyse their work.
Also, the researcher will go through the literature of different authors and come
up with his original report without copying their work.
At the end of the research, he will use the results of the study to come up with
literature about the significance of big data analytics in business management and
improvement. This information may be used by other researchers and managers from
various organisations to conduct their studies on the topic.
3.7 Limitations
The chosen research methodology gives the key limitations of the study. Meta-
analysis takes much time. The researcher must read a lot of literature to come up with
the relevant sources.
Moreover, the information obtained through meta-analysis may be biased and
some of it may be omitted. This is because the researcher is not given a chance to
collect primary information from various people in the field. The researchers are
subjective to their own opinions depending on their experiences; there the conclusions
made by each researcher may not be satisfying to the other.
3.8 Chapter Summary
Big data analytics has several implications on business. Data collected can be
used by companies to find out new income streams. The effective use of data analytics
in business operations can promote the performance of an organisation. The data
collected can be used to show the performance indicators in a certain sector which will,
in turn, allow decision makers to develop strategies on how to remain competitive.
Organisations can use big data to predict the future trends and upgrade performance of
a process or system.

Big data analytics 21

Big data analytics 22
Chapter 4: Findings and Analysis
Theme 1: Importance of big data analytics
According to Lee, Kao and Yang (2014), Big data analytics is quite similar to the
concept of revolution when it is considered in the context of Information Technology and
the application of data analytics by organizations is improving every year. In addition to
this, finding of this study also implies that analytics could involve the use of advanced or
developed tools on data which is sourced from multiple platform. Findings also highlight
the fact that big data has the properties of high variety, volumes and velocity and under
such context, the data set tends to come from a variety of online networks like web-
pages, audio and video devices, logos and other sources. Thus, it is worth mentioning
that when it comes to innovation in business, big data certainly plays a great role as the
business has access to large amount of information high necessary for business
management. Particularly, when business needs to apply creative ideas to gain market
access, gain competitive advantages and increase the customer base, the big data is
highly required for organization –this means on the basis of this big data, product or
service can be shaped accordingly. In the literature review, it was studied that Big data
need to be analysed to be of importance to the organisations. This is where big data
analytics arises. It involves a complicated procedure for assessing enormous amounts
of big data originating from diverse sources to expose the unseen facts (Akter et al.,
2016). Case organization A is known as the Swiss subsidiary of a multination insurance
organization agency which provides both individual as well as corporate consumers a
wide range of personal liability and motor vehicle insurance. The organization is able to
gain maximum of the market share as it has gained access to diverse regional
information about the customers.
Theme 2: Big Data Analytics in the Management of Business
Over the past few decades, the era and platform of big data has particularly descended
on many communities and they have particularly started from government, e-commerce
vendors and sports organizations. Provost and Fawcett (2013) performed a study and
mentioned then fact that in the coming decade, the overall amount of information could

Big data analytics 23
enhance by 50 times but the number of specialist in information technology often learn
about the data increased by 1.5 times.
Authors of this study has provided a contradictory fact that information overload is the
most prominent issue in managing Big Data as per this study, particularly for information
users, searching for what they need from a large amount of information perfectly is
being more challenging than it was before. However, authors of the papers have also
focussed on the fact that if the organizations or the businesses are able to gather,
process and make analysis of the database, the information which was collected are
more likely to be at the vulnerability. Moreover, in the study, it was also identified that in
the context of experimental growth, extension and acceleration of data is important.
When one is required to turn data into actionable knowledge and intelligence, enterprise
data system, search system, advanced system and some analytics could become
important. So, if the data analytics is large, the analytics can only be possible if
organizations have effective algorithm software. In the literature, it was studied that
besides the fact that here is a vast number of big analytics tools that a firm can employ.
Some of them are extracted information load, Hadoop distributed file systems and Data
warehouse tools (Ji-Fan Ren et al., 2017). Under this findings, it can be mentioned that
when the organization has access to such large storage of information with appropriate
tracking, it is worth mentioning that business will find it easy to reduce the impact of
contemporary business challenges and enhance market exposure by shaping the
business more effectively.
Theme- “Big Data-driven Innovation”
Tiwari, Wee and Daryanto (2018) performed a study and mentioned that innovations are
adopted when corporations tend to combine, collaborate them in purposive techniques
into existing operational activities. Authors added the fact that in order to comprehend
management as well as integration of innovations, there should be a proper
understanding or insight, of concept and definitions. This study has contradictory view
about the findings of a study by Wolfert et al. (2017), where it is found that there is a
common idea that innovation needs to be viewed as the technique instead of outcome.
Considering this view, Gobble (2013) mentioned that about several innovative
techniques of Big Data such as Fuzzy Front-End in which the concept of big data

Big data analytics 24
management has been aligned to the innovative technique such as idea selection,
concept definition, opportunity identification, opportunity analytics, idea generation and
enrichment. In the literature, it was studied that organisation cannot achieve the
capabilities of big data analytics without using other associated resources. There is a
constant requirement of using other resources such as Big Data infrastructure to reap
the benefits of big data analytics (Chen & Zhang, 2014). Big data analytics infrastructure
often involves the infrastructure that is composed of information technology which helps
the sectors of the company to work as a team to attain the main objective of the
business.
This study emphasised that Big Data is the frontier for innovation, competition and
productivity that big data is referring to large data sets whose size has maximized to the
threshold where the existing software tools are not able to capture, store, manage and
analyse them. Findings of this study added the fact that business or data analytics is not
a modern trend and data is often used appropriately and constantly it progresses.
Nonetheless, authors highlight the insight that evolution is significantly more obvious
irrespective of the fact that big data analytics is a new trend that start to demonstrate on
scene in the last decade, several intelligent and advanced companies try to enhance big
analytics to remain active in the race within the industrial environment. Thus, here the
major idea is about how to be agile to implement big data analytics as they did not have
required infrastructure to implement. Findings of this study has also mentioned that
exploration of big data analytics in industrialization procedures could promote the agility
as well as industrialization performance.
Chapter 5: Evaluation of results and findings
Research findings help to observe the fact that in the recent time, big data has been a
significant business trend in which business organizations are having an optimum
access to market information, customer data and information related to other parties. In
addition to this, another significant fact which was found in the analysis is the
application of innovation in business which was significantly sustained by Big Data
management. In order to enable big data potential organizations need to coordinate as
well as collaborate with other major Big Data users and actors.

Big data analytics 25
This means organizations in the business field should assist each other in development
of the data intelligence together other partners like Information Technology partners.
Findings of this study also helped to state the fact that selection of Big Data network
must have to reflect the strategic decisions as well as the long-term visions of involving
in Big Data activities. It can be also be mentioned that organizational transformation is
highly necessary according to the capability of processing Big Data in respect to
technology based capacity, the potential big data platforms to understand as well as use
external and unstructured data. The combination of technological capacity as well as
organizational change or transformation is a great as well as significant factor of using
Big Data Potential.
Furthermore, on the basis of the findings, it can be added that revealed findings on the
importance of big data management does not talk about the broad areas of big data,
which could incorporate different factors like quality, reliability, availability, accessibility,
relevance and governance and security, semi-structured, of unstructured and its
information. It can also be mentioned that Big Data has particularly changed the
decision-making process. Changes in decision-making are linked to intensified usage of
analytical technologies and process.
When it comes to importance of Big Data, organizations have begun to adopt an
optimized technique for optimal distribution of resources to carve the path of an
organization’s growth instead of relying on a trial and error technique. The effective
method of implementation is the corporation techniques of big data analysis. The
business data acquired by large corporations is extremely complex to be processed by
the traditional data processing applications. It can mentioned that there are better ways
to extract useful information which could support and enable proper decision-making as
well as help to uncover patterns. Such techniques often help to develop core of big data
analytics. Organizations, when it comes to using Big Data Analytics, organizations
irrespective of their size and structure, small and medium businesses are leveraging big
data to gain the best possible outcomes for the businesses. For example, it can also be
added that small businesses often lack the resources to go with big investment; thereby,
particularly the small businesses require a more smart strategy for joining in the big data
trend. Thus, findings of this study also help to observe the fact that rather than worrying

Big data analytics 26
about the use of small or big data sets, small businesses tend to start by investing in
small scale analytics and pay attention on using data technology analytics for
organizational decision –making by optimal business datasets.

Big data analytics 27
CHAPTER 6: CONCLUSION
6.1 Conclusion
In conclusion, it can be mentioned that big data analytics has vast role to play in today’s
contemporary businesses as due to big data, organizations or businesses have wide
access to market data, customer data and future anticipation. It is certain that
businesses today are quite able to manage the overflow of information both internally
and externally. In addition to this, findings of this study also help to understand the fact
that big data has enabled the business to apply innovation and to become more
innovative in the existing business ideas. Nonetheless, it is worth mentioning that big
data cannot be goldmine for all businesses because the use of the technology for using
and implementing Big Data to generate value is challenging. Especially, for small and
medium size businesses could find it difficult to apply or go with the ideas of Big Data
due to lack of understanding about the application of big data in business and
efficiencies of dealing with the outcome. It can also be mentioned that enterprise-wide
platform of sharing Big Data and its analytics within the firm and system tend to impose
challenges because of incompetency in the use of technology. It is learnt from the
review and findings that a major obstacle in big data is the consistency of internal as
well as external database, which implies that there is a challenge in integrating as well
as standardizing data for contrasting formats to access and use significant information
flows. On the basis of the findings, following recommendations have been provided.
6.2 Recommendation:
When applying Bid data analytics to business, organizations should be aware of some
particular factors such as the organizations should know about the use of data-driven
decision-making, use of particular type of analytics.
Data-driven decision-making
This data-driven decision-making is more of availability of voluminous data which could
allow businesses to make strategic decisions on the basis of thorough analysis of
evidences instead of the intuition. Thus, this often makes it relatively simpler to evaluate

Big data analytics 28
opportunities on the basis of the potential cost reduction and growth of revenue. Hence,
new solutions tend to allow the discovery of new businesses opportunities and
identification of proper areas for future investment.
Prescriptive Analytics
Such analytics often help businesses to find the best course of action for a particular
situation. Hence, prescriptive analytics is often associated to both descriptive as well as
prescriptive analytics. In addition, prescriptive analytics hold the ability of processing
new data to improve the accuracy of predictions, omitting guesswork as well as
delivering better decision making options. This is an effective option for the organization
because many businesses exactly from the mid-sized to large organizations are shifting
their attention to predictive analytics to enhance bottom line the outcomes as well as
competitive advantages

Big data analytics 29
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