Business Analytics Decision Making: Research Analysis Report, MGT602

This research analysis report examines business analytics and decision-making processes within the context of a rapidly evolving business environment. The report explores the significance of data-driven decision-making, highlighting the role of big data analytics in generating profitability and improving customer satisfaction. It delves into various aspects, including organizational context, data sources, and the identification of trends using data analytics. The report also provides an overview of decision-making tools, such as decision trees, decision matrices, and cost-benefit analysis, providing insights into their application. It uses BitTorrent cryptocurrency data as a case study to illustrate the practical implementation of these concepts. Overall, the report emphasizes the importance of data analytics for informed decision-making and sustainable business success.

Running Head: BUSINESS ANALYTICS DECISION MAKING
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BUSINESS ANALYTICS DECISION MAKING
Research Analysis Report
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Executive Summary
The business environment is advancing so fast and the dynamism that is manifested
necessitates continuous innovation by business enterprises to ensure that they stay afloat.
Decision making is a fundamental part of the business and an investment in ensuring that this
process is effective is important to the success of the business establishment. Recent years have
created a paradigm shift in the manner in which companies make their decisions which many of
them adopting a data driven business model. Big data analytics provides numerous opportunities
for the business world to explore the untapped value of data to transform the business
environment, generate profitability and improve customer satisfaction.

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Table of Contents
Introduction......................................................................................................................................4
Organizational Context....................................................................................................................5
Sources of Data for Business Analytics...........................................................................................6
Use of Data Analytics in Identifying Trends...................................................................................7
Visualization of the Decision Making Process................................................................................8
Decision Making Tools..................................................................................................................10
Decision Trees...........................................................................................................................11
Decision Matrix.........................................................................................................................11
Cost and Benefit Analysis.........................................................................................................12
Conclusion.....................................................................................................................................12
References......................................................................................................................................14
Appendices....................................................................................................................................15

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Introduction
The success of a business is heavily pegged on how effectively the organizational
structure addresses the specific needs of the business and the versatility of the decision making
process to reflect the dynamic needs of the clients and the developments within the market
(Turban, Sharda, & Delen, 2010). Decision making is part and parcel of the business operations
playing a vital role to the outcomes that the business registers. Recent years have seen managers
in the business world recognize the importance of this process and as such heavily invest in
making the process more effective and based on data.
For a long time business decisions were largely based on the intuition of managers with
little consideration of the available data. While data still played a role in the process of decision
making, the architecture of data analysis was largely retrospective rather than prospective and
this fundamentally reflected on the available historical information to predict the future business
outcomes. Developments in technology and data processing have resulted in a seismic shift in
the manner in which business data is handled resulting in the advent of processes that are able to
generate more value for the data owned by the company (Provost & Fawcett, 2013).
In the contemporary business environment, data is by far one of the most potent resources
owned by the business with organizations that collect and store sufficient amounts of data
proving to be at the center of business growth. Firms are increasingly leveraging the data that
they have to conduct analytics and make data driven business decisions. The advent of big data
technologies has created numerous opportunities in data analysis providing the capability to
handle both structured and unstructured data and derive meaningful insights that have proved to
be informative and to play a critical role in steering business growth across the world (Provost &

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Fawcett, 2013). Organizations that are driven by the insights that they generate from data are
better at managing risks, are in a better position to understand their clients hence develop more
customer centric products which results in client satisfaction, and most importantly record higher
revenues and profit margins compared to their counterparts who are yet to discover the true value
of data.
Organizational Context
The company under consideration is BitTorrent which is a firm that has specialized in
providing software to clients across the globe through its platform. The data provided reflects the
market capitalization of the Bit Torrent crypto currency for the last 3 months. The data generally
provides a glimpse of the performance of this currency. The objective is to use the data to derive
meaningful insights that can be used to make appropriate decisions in line with the objectives of
the company (Balakrishnan, 2018).
Summary of the
crypto currency
performance1
1Source: Available on the Excel File. The data was obtained from Money Cap Website

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Sources of Data for Business Analytics
Business analytics and by extension business intelligence is a complex process that is
heavily dependent on the technological infrastructure implemented by the company. Data
sources used for the purpose of generating insights differ according to the business needs and
objectives that the company desires to achieve both in the long and short run. Data sources are
widely classified as structured and unstructured data (Balakrishnan, 2018).The most prevalent
data sources include databases which could be administered by the company or the company
could decide to outsource data from databases managed by other entities; flat files which
basically include the information files within the organization such as excel spreadsheets
containing particular information about the consumers, the product, the market behavior, e.t.c;
webservices which make the use of cookie technology to collect information on how the
browsing patterns and trends of the visitors of the webpages and websites; other sources such as
online subscriptions to newsletters, RSS feeds also provide organizations with valuable
information essential for the process of decision making. Increasingly firms are outsourcing data
from social media entities which continue to amass huge chunks of information on the behavior
of their users. This has however raised ethical concerns over the privacy and confidentiality of
the users which some individuals believe that social media companies have been consistently
violating (Johanson & Kristron, 2018).
Use of Data Analytics in Identifying Trends
The scope of data analytics in the business environment is wide, incorporating some
aspects of artificial intelligence and some aspects that are firmly grounded on statistical
inferences. A blend of these two approaches when critically evaluating data is fundamental in

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understanding both the historical characteristics of data and its tenacity to accurately predict the
future outcomes. Machine learning is particularly important in understanding the behavior from
particular datasets through the integration of statistical models and computer algorithms to
continually improve the insights that organizations derive from their data sources (Provost &
Fawcett, 2013).
Machine learning provides systems that have the ability to learn from the data that they
are provided with hence they can be able to make future predictions with some level of accuracy
considering the dynamism and changes within the dataset (Johanson & Kristron, 2018).
Generally the outcomes generated by machine learning algorithms provide a fundamental
background for making decisions with confidence because they have taken into account all the
prevailing circumstances within the market.
Predictive analytics is another very important part of data analytics that provides
evidence to support the decision making process within the organization. This particular
technique is an integration of modelling, statistics, and data mining to predict the future
outcomes within the business environment. The data is keenly evaluated to observe whether
there is an existence of patterns and trends that can be generalized going into the future. Apart
from the patterns, data mining can help the organization determine the correlation between the
different variables within the data set and identify instances that are not expected for that
particular set of data (Provost & Fawcett, 2013). This is particularly important in identifying the
areas that require improvement and managing the risk portfolio of the company. Predictive
analytics is particularly important in understanding the customer behavior and forecasting the
revenues and prices of products in the market. Essentially, data analytics is the lifeline of a

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sustainable and profitable business venture. Some businesses invest heavily in text analytics
while others have a particular interest in using the numbers generated by the business enterprise
to derive their insights. The choice of analytics approach used by the company is heavily pegged
on the business objectives and the business culture established by the company (Provost &
Fawcett, 2013).
Visualization of the Decision Making Process
Data analytics is widely categorized into four types; descriptive, diagnostic, predictive,
and prescriptive. The complexity of the type use for analytics increases as one moves to the right
but the value generated by the analytics approach also increases.
Types of data analytics2
2Source: https://www.techleer.com/articles/227-data-analysis-enhancing-the-decision-making/

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Fundamentally data analytics is at the center of the process of making decision as
it is virtually required in every step within the cycle of decision making. Identifying the problem
that requires to be addressed essentially relies on scrutinizing the available data sources through
descriptive analytics which basically makes use of statistical inferences (Provost & Fawcett,
2013). Data mining can also be used to identify discrepancies within the data set which ideally
triggers the question of what is the cause of the identified anomaly. The whole decision making
cycle to the point of implementation captures the different types of data analytics.
Implementation particularly relies on the prescriptive analytics which proposes exactly what
needs to be done to turn around the area within the business that is not working or to continually
improve the business outcomes within the business for areas that did particularly well
(Balakrishnan, 2018).

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Decision making process3
Decision Making Tools
While conventional principles apply when choosing the tools for decision making, the
tools have significant differences in the metrics that they put into consideration while making the
decision. Usually a combination of the different tools is important as it enables the decision
makers to come to a conclusive decision after thoughtful considerations of the different business
perspectives. Relying on a particular approach could be detrimental because of the overlook of
some important elements within the business. Examples of decision making tools include
decision trees, decision matrix, and cost and benefit analysis just to mention a few (Johanson &
Kristron, 2018).
3Source: http://textbook.stpauls.br/Business_Organization/page_101.htm

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Decision Trees
These are basically models or visual representation of the choices and the possible
implications that each one of them will have. Decision trees incorporate the aspects of statistical
inferences and probabilities in analyzing the outcomes that particular decisions will have for the
firm (Balakrishnan, 2018). Decision trees are particularly useful in data mining and machine
learning. They measure comprehensively the outcomes of a business decision capturing the
expected cost and the utility that the customer will derive by choosing a particular course of
action. For the project under consideration, decision trees can be used to deduce the price when
the market is high, low, close or open. Probabilities can then be assigned respectively and the
outcome on the capitalization and volume measured by the price and the respective probabilities.
A decision can then be made to ascertain the next course of action concerning the behavior of the
crypto currencies (Johanson & Kristron, 2018).
Decision Matrix
A decision matrix is very important in assessing all the options available before making a
decision. The options are then weighed with the factors that are likely to affect them and a score
given considering the importance of the decision and the factors that affect it. Each decision
option is then given a score. The decision matrix allows for the critical evaluation of all the
factors which actually helps in ensuring that the decisions made are informed on the prevailing
circumstances and are well aware of the inherent risks before a course of action is pursued. For
this project, the application of the decision matrix will be important in assessing the factors that
affect the prices of the crypto currency for the past few months and then measure the possibility
of this factors continuing to prevail into the future. This can then inform the steps to be taken to

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stabilize the crypto currency and ensure that it generates more revenue for the company. It can
further be used to predict any extreme events that might result in the future and thus create
contingency plans to alleviate adverse shocks to the operations of the company (Johanson &
Kristron, 2018).
Cost and Benefit Analysis
The cost and benefit analysis is a rather straightforward approach to making decisions.
This approach computes the cost of each decision and then computes the benefits of the same
decision and compares the two to reach a decision. Ideally the benefits of the decision should
outweigh the costs for it to be viable for the business. Further decisions will the highest benefits
in comparison to the cost incurred are given a priority by the business establishment (Johanson &
Kristron, 2018). This method is however not very effective on its own as it could easily result in
misleading decisions. But it is a good method that can be used to sieve the options under
consideration at the initial stage of decision making. For this project, cost benefit analysis could
be used to determine the cost of the different options available for the company in stabilizing the
crypto currency or rather increasing its value to make sure that it generates more revenue for the
company (Chapman, 2011).
Conclusion
In conclusion, data is a resource that business enterprises should not underestimate.
Arguably, the value of data to a business has been equated to the value of crude oil to the
economy. Essentially what this means is that data is the driver of the business providing the
major source of energy as crude oil is the driver of economies. The panacea to the challenges in
the business world therefore lies in leveraging data analytics in business decision making. The

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future of companies undoubtedly lies in their abilities to invest in their data analytics capabilities
for the purpose of informing the decisions made within the firm. Going forward data will remain
a powerful asset and those who invest in it shall abundantly reap the benefits (Moris, Kajikawa,
Kashima, & Sakata, 2012).
References
Balakrishnan, S. (2018). Big Data in Business Intelligence. Journal of Business Analytics, 2-9.

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Chapman, R. (2011). Simple Tools and Techniques for Enterprise Risk Management. Journal of
Business Management, 2-9.
Johanson, P., & Kristron, B. (2018). Cost-Benefit Analysis. Cambridge University Press, 2-15.
Moris, J., Kajikawa, Y., Kashima, H., & Sakata, I. (2012). Machine learning approach for
finding business partners and building reciprocal relationships. The Journal of Expert
Systems with Applications, 2-12.
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-
Driven Decision Making. The Journal of Big Data, 51-59.
Turban, E., Sharda, R., & Delen, D. (2010). Decision Support and Business Intelligence
Systems. The Jornal of Data Analytics , 2-9.
Appendices

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Sample Decision Matrix4
Sample Decision Tree 5
4 Source: https://expertprogrammanagement.com/2017/09/decision-matrix-analysis/
5Source: https://www.aqa.org.uk/resources/business/as-and-a-level/business-7131-7132/teach/teaching-guide-
decision-trees