In this topic, we have discussed about writing Dissertation in business and management for Master’s students. Researchers are encouraged to research and good dissertation writing skills are necessary. The present article helps the USA, the UK, Europe and the Australian students pursuing their master’s degree to identify the best technical conventions, which is usually considered to be challenging. Tutors India offers UK dissertation in various Domains. When you Order any dissertation at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.

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Business Analytics Dissertation Help: Defining and Identifying a Research
Agenda for an Evidence Based Management Framework
Dr. Nancy Agens, Head,
Technical Operations, Phdassistance
info@phdassistance.com
In Brief
You will find the best dissertation
research areas/topics for future
researchers enrolled in Engineering and
Technology. In order to identify future
research topics, we have reviewed the
technical conventions. (recent peer-
reviewed studies). Business analytics
refers to the systematic use of data
collected from a variety of sources,
quantitative and statistical analysis,
predictive and explanatory models and
evidence-based management to direct
actions and decisions towards the
appropriate stakeholders. Writing a
business analytics dissertations, it is
important for the researchers to have
strong knowledge related to the
approaches used and in the data science
and machine learning which will help in
writing business analytics dissertation.
Keywords: Research Proposal, Academic
Writing, Literature review writing,
Literature review help, Dissertation
writing help, dissertation writing service.
I. INTRODUCTION
Organizations employ business analytics
to make intelligent decisions, which can be
made quicker and better in order to
enhance the value of the business. Until
today, academicians and industrialists have
focused mainly on descriptive and
predictive analytics. However, prescriptive
analytics is gaining huge interest in terms
of research in the business analytics area
as it is considered as the best course of
action for future businesses. Therefore
Prescriptive analytics is often considered
as the next step in improving data analytics
maturity, which further leads to augmented
decision making improving business
performance.
Business analytics refers to the systematic
use of data collected from a variety of
sources, quantitative and statistical
analysis, predictive and explanatory
models and evidence-based management
to direct actions and decisions towards the
appropriate stakeholders (Davenport &
Harris, 2007; Soltanpoor & Sellis, 2016).
Therefore, business analytics incorporates
the use of approaches such as data science,
operational research, machine learning
and information systems fields
(Mortenson, Doherty, & Robinson, 2015).
In this context, business analytics deal not
only with descriptive models but also with
models that can offer valuable insights and
support business performance decisions.
To this end, business analysis has
developed beyond a simple raw data
analysis on large datasets with the goal
of creating a competitive advantage for
organizations (Mikalef, Pappas, Krogstie,
& Giannakos, 2018; Vidgen, Shaw, &
Grant, 2017). Business analytics is
classified into three main categories with
different levels of difficulty, value and
intelligence (Akerkar, 2013; Krumeich,
Werth, & Loos, 2016; Šikšnys & Pedersen,
2016) (Krumeich, Christ, Julian, &
Kempa-Liehr, 2016): (i) descriptive
analytics, answering the questions “What
has happened?”, “Why did it happen?”, but
also “What is happening now?” (mainly in
a streaming context); (ii) predictive
analytics, answering the questions “What
will happen?” and “Why will it happen?”
in the future; (iii) prescriptive analytics,
answering the questions “What should I
do?” and “Why should I do it?” (Lepenioti,
Bousdekis, Apostolou, & Mentzas, 2020).

2. Copyright © 2020 PhdAssistance. All rights reserved 2
Therefore, while writing a
business analytics dissertations, it is
important for the researchers to have
strong knowledge related to the
approaches used and in the data science
and machine learning which will help in
writing business analytics dissertation.
II. FOR THE RESEARCH AGENDA
AND CREATING AN EVIDENCE-
BASED MANAGEMENT
FRAMEWORK
Association to Advance Collegiate
Schools of Business (AACSB), 2018 states
that “In today’s proliferating dynamic
business environment, business schools
must react to the business world’s
changing needs by providing relevant
knowledge and skills to the communities
they serve” (Association to Advance
Collegiate Schools of Business (AACSB),
2018). An Evidence-Based Management
(EBMgt) methodology addresses highly
important business issues using effective
methodologies and implementations
(Pfeffer & Sutton, 2006). During the last
two decades, the prevalence of EBMgt in
research has increased. It was put forward
as a way of bridging the gap between
research and practice, EBMgt is a data and
theory-driven approach to decision-
making, arguing that while managers
cannot have full knowledge in ever-
changing environments, the consistency of
decisions is usually enhanced by taking
data-driven facts into consideration
(Pfeffer & Sutton, 2006; Pfeffer, 2012).
It is “about making decisions by
the diligent, clear, and judicious use of
four sources of knowledge: The classical
concept of decision support systems (DSS)
is a computer-based information system
leveraging data and/or models to support
decision-makers solve semi-structured or
unstructured problems (Turban, Aronson,
& Liang, 2004). The classical what-if
analysis of DSS is to take a question, apply
a data-driven model to evaluate an
outcome and compare that outcome with
alternative interventions. This concept of
data-driven decision making is an integral
part of the Business Analytics System
(BAF) of Holsapple, Lee-Post and Pakath,
in which evidence-based problem
identification and solutions occur in a
business context (Holsapple, Lee-Post, &
Pakath, 2014). Decisions powered by data
are evidence-based and companies step
into evidence-based analytics to obtain a
competitive advantage (Cho, Song,
Comuzzi, & Yoo, 2017; Davenport, 2006;
Wimmer, Yoon, & Sugumaran, 2016).
DSS as a business analytics program,
therefore, belongs to EBMgt's assessed by
external evidence circle in Figure 1
(Scheibe, Nilakanta, Ragsdale, & Younie,
2019).
The intersection of EBMgt and
analytics is especially important for
research on management science but is
often only discussed in passing. In
addition, this framework highlights the
importance of data, models, stakeholders
and context in developing and
implementing business analytics solutions
through the four elements of EBMgt.
Companies use analytics to succeed
through advances in automated business
processes and are enabled by predictive
analytics (Prahalad & Krishnan, 1999).
DSS research has lasted for almost
half a century, but interest in DSS
techniques, methods, and implementation
has recently increased under the umbrella
of "big data" and "business analytics"
(Holsapple et al., 2014). Therefore, EBMgt
is concerned with making decisions by
using conscientiously, specifically and
judiciously the best available data from
various sources to maximize the likelihood
of favourable outcomes.

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Fig .1 Four Elements of EBMgt
This can be accomplished through
the six A’s: (1) Asking: transforming a
real-world problem into an answerable
question. (2) Acquiring: the systematic
search and gathering of proof. (3)
Appraising: to objectively evaluate the
quality and validity of the evidence. (4)
Aggregating: weighing proof and putting
it all together. (5) Applying: Inclusion of
evidence in the process of decision-
making. (6) Assessing: Evaluation of the
outcomes of the decision adopted. The
intersection of EBMgt and analytics is
particularly important for research in
management sciences.
Therefore, writing a dissertation
in the field of business analytics can be
challenging; however, it is important to
have a thorough understanding of the
business analytics and evidence-based
management and their correlation.
REFERENCES
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Decision

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