Principles of Business Analytics Report - RMIT University

This report provides a comprehensive overview of business analytics principles, covering topics such as data mining, business intelligence, and the challenges in business analytic leadership. It discusses the analytics ecosystem, including descriptive, predictive, and prescriptive analytics, and their applications across various industries. The report also delves into the intricacies of data mining, highlighting the challenges associated with big data and the development of unified theories. Furthermore, it differentiates between business intelligence and business analytics in terms of functionality. The report also includes an analytical section using a provided dataset, covering frequency distribution, descriptive statistics, and regression analysis. Desklib offers a wealth of similar reports and study tools to aid students in their academic pursuits.

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Running head: PRINCIPLES OF BUSINESS ANALYTICS
PRINCIPLES OF BUSINESS ANALYTICS
Name of the Student
Name of the University
Author’s Note

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PRINCIPLES OF BUSINESS ANALYTICS
Table of Contents
Introduction.................................................................................................................................................2
Task 01........................................................................................................................................................2
Business Analytics...................................................................................................................................2
Analytics Ecosystem................................................................................................................................2
Data Mining.............................................................................................................................................3
Business intelligence and business analytics...........................................................................................4
Challenges in business Analytic Leadership............................................................................................5
Task 02........................................................................................................................................................6
Task 02.1.....................................................................................................................................................6
Task 02.2.....................................................................................................................................................7
Task 02.3.....................................................................................................................................................8
References.................................................................................................................................................10

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PRINCIPLES OF BUSINESS ANALYTICS
Introduction
Information is an asset in today’s generation. The direction towards which the modern world is
moving is greatly dependent upon the way information are exchanged, stored as well as encoded in the
form of data and decoded again as a form of information (Sun et al., 2017). Both data and information are
used in businesses worldwide for better functioning of it. However, to earn competitive advantage and to
bring effective exchange and utilization of information along with ensuring advancement in businesses, to
forecast, anticipate and understand the rational perspectives of the customers, their satisfaction levels and
rendering products, services accordingly business analytics is a necessary (Wazurkar et al., 2017). It
improved not only the way of market research but also financial research, operational research and other
business domains.
Task 01
Business Analytics
Business analytics is the culture of iterative procedures to explore into a organizational data or
big data sets coupled with statistical analysis to process those data into a meaningful information (Yao
and Guohui, 2018). It significance exists due to the reason that it is a data driven methodology that helps
in decision making. Business analytics helps in gaining insights and optimize business procedures.
Analytics Ecosystem
In various industries different modes of analytics are important based on their nature of
usefulness (Yerpude and Singhal, 2017). Descriptive analytics uses data aggregation as well as data
mining to delve into the objective of analyzing the past and justify the present situation whereas
predictive analytics deals with the aim of understanding the future. Prescriptive analytics on the other
hand deals with the purpose of forecasting and advising on possible outcomes. Notably, exploratory
analytics works upon the data with the objective to detect the main characteristics of the data especially
by visual methods. Usage of statistical models is not compulsory as in exploratory business analytics the
aim remains, to understand what the present data is trying to tell without its past interpretation or future
forecasting and discover patterns beyond the formal modelling or testing of hypothesis (Shmueli et al.,
2017). Various industries utilize business analytics with the objective to simplify the daily to daily
complex business environment and the challenges that are being faced for earning competitive advantage.
As for example, prescriptive analytics when implemented correctly, then it have large impact upon
company’s bottom line and for making efficient decisions. It optimizes inventory in the supply chain as
well as the overall production to ensure that the deliverance of the products and services are being made
successfully to the right customers and within the right time. Descriptive analytics supports the process of
adaptive analysis regarding providence of historical insights into the operations, productions, distribution,

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PRINCIPLES OF BUSINESS ANALYTICS
sales, inventory management, customer satisfaction and financial states etc. of the businesses. Predictive
analytics helps to forecast the existing demand for the factor inputs for production and anticipate the
feasible level of outcome based upon myriad of variables and other related parametric factors. It also
helps to produce credit score which assists the financial services to determine the probability of
consumers who would successfully make credit payments on time in the future ( Seddon et al., 2017).
Apart from that predictive analytics also helps in identifying patterns in data for capturing the existing
relationship between various datasets using algorithm and statistical models.
Data Mining
Data mining is the procedure of sorting data through which patterns are identified and
relationships are established based on certain parameters. Association, path analysis, classification,
clustering and forecasting are the parametric indicators of efficient data mining (Reid, Short and Ketchen,
2018). In agile business environment the challenges that are faced by data mining is basically the
presence of significant amount of big data. For this the data analysis becomes very complex and different
algorithms becomes necessary to be formulated in order to extract information that are reflected by those
enormous amount of data (Pogue and Miller, 2018). It becomes a challenge for business analytics in agile
business environment to analyze and direct the big data towards a meaningful information. Data mining
helps to sort the data in a signified way and understand the existing relationship between various
information. Notably, the challenges can be summarized as follows:
 Development of a unified theory of data mining – The developers are faced by the challenge of
designing a structural framework that encompasses all the algorithms of data mining.
 Scaling the high speed data streams and high dimensional data – Scaling is necessary to
categorize and organize the data when the set of data are very complex and huge (Márquez,
Marugán and Papaelias, 2018).

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PRINCIPLES OF BUSINESS ANALYTICS
 Data mining of time series data and sequence data – Practicing design for predicting information
and efficiently anticipating the direction of data is a crucial challenge in case of time series data
and sequence data (Lu, 2018).
 Mining of knowledge from complex data – Data obtained from multiple relationship of attributes
becomes a big challenge in data mining and business analytics due to the reason that all the object
of interest are not independent of each other and along with that the attributes are not of single
type every time.
 Data Mining in Networks – The community and social networks as well as mining in
computer networks are very challenging in the agile business environments since it faces the
problem of rendering a good algorithm and detecting attacks (Krishnamoorthi and Mathew,
2018).
 Mining multi-agent data- Correlating data in a sensor network and mining across multiple
heterogeneous data source by minimizing the amount of data which are shipped within
various sites through the combination of game theory and data mining.
 Mining and use of business analytics in case of environmental problems – In the resource
driven world data mining and business analytics is utilized to resolve the problems in the
fields of bioinformatics, cancer prediction, biological sequence, earthquakes, landslides and
spatial data sets (Jaklič, Grublješič and Popovič, 2018).
 Data integrity – It is one of the major challenge in the agile business environment to ensure
the security, integrity and privacy of business data. Developing efficient algorithms and
estimating their impact upon the data and compare it with ex-ante and ex-post individual
patterns.

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PRINCIPLES OF BUSINESS ANALYTICS
Business intelligence and business analytics
Business intelligence is the procedure to monitor and track metrics in the form of dashboards or
reports though extracting meaningful sense out of it and correlating them with relevant factors which
renders impact upon them is business analytics (Hazen et al., 2018). It also help to understand the trends
with the usage of statistical algorithms for anticipating predictable outcomes. In terms of functionality the
difference between business intelligence and business analytics can be incorporated as follows:
Functions Business Intelligence Business Analytics
Visualization of data, its
collection and analysis √ √
Detecting the pain points and
offering solutions to optimize
the pain points within the
organizational data
√ √
Presenting and organizing data
for visualization and reporting √ √
Creating summary of historical
data for the purpose of
visualization through
(descriptive analytics)
√ ×
Determining the sources of the
issues confronted with in data
through descriptive analytics
(Diagnostic analytics)
√ ×
Forecasting upon data after
collection of data (Predictive
analytics)
× √
Providing solutions for the
issues that are being confronted
in the process of data discovery
and descriptive analytics
× √

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PRINCIPLES OF BUSINESS ANALYTICS
Challenges in business Analytic Leadership
All the problems cannot be solved by the same level of thinking and hence is to delve into the
opportunities, threats and upon other prevailing challenges that restraints the expansion of the business
and optimization of its objectives (Hasić, Smedt and Vanthienen, 2018). The major requirements of the
modern business environment is thus to identify, define, analyze and resolve the issues as soon as
possible. However, on a practical note there is lack of resources, in terms of labor, capital, land, time, etc.
and there exists a level of uncertainty that is measured in terms of risk in today’s business environment
(Barkham, Bokhari and Saiz, 2018). Moreover, the quality of the products and services along with their
acceptance level by the target consumers is a matter of concern for every business. Hence to resolve the
issues related to business at a faster rate it is essential to channelize the available resources in the rightful
manner with the incorporation of efficient business analytics techniques that will allocated the resources
optimally and ensure that the direction of the business is towards its objective (Delen and Ram, 2018). All
of this constraints in terms of risk and uncertainty associated with business activities, scarcity of
resources, actual and anticipated demand of quality of products and services, product and service
deliverance, gap between actual level of satisfaction and the expected level of satisfaction, scope of the
business, etc. and based on that determining the scope of the business as well as preparing the budget as
well and feasible within the limited span of time available for decision making, necessitates the
prevalence of leadership in the business environment by the implementation of business analytics (Duan,
Cao and Edwards, 2018).
Task 02
The analytical details are incorporated as follows:
Task 02.1
Interval Upper Interval of Bins
0-1310000 1309999
131000-2620000 2619999 Count 2132
2620000-3930000 3929999 Minimum 131000
3930000-5240000 5239999 Maximum 6370000
5240000-6550000 6549999 Range 6239000
Interval Bin Frequency

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PRINCIPLES OF BUSINESS ANALYTICS
0-1310000 1309999 1271
131000-2620000 2619999 733
2620000-3930000 3929999 105
3930000-5240000 5239999 20
5240000-6550000 6549999 3
Based on the frequency distribution, the histogram is as follows:
1
0
200
400
600
800
1000
1200
1400
Histogram
Task 02.2
Based on the raw data the computed values of the descriptive statistics are shown in the table below.
Descriptive Statistics
Mean 1327259.15

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PRINCIPLES OF BUSINESS ANALYTICS
Standard Error 15846.05555
Median 1170000
Mode 1200000
Standard Deviation 731669.2012
Sample Variance 5.3534E+11
Kurtosis 4.525459267
Skewness 1.728367035
Range 6239000
Minimum 131000
Maximum 6370000
Sum 2829716507
Count 2132
Confidence Level (95.0%) 31075.34817
Correlation Statistics
Price Distance Postcode Land size Building
Area
Price 1
Distance -0.53959 1
Postcode -0.08604 0.56696 1
Land size 0.05714 0.223449 0.02937 1
Building Area 0.30562 -0.0454 0.056275 0.096121 1
Task 02.3
The regression statistics can be shown as follows:
SUMMARY OUTPUT

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PRINCIPLES OF BUSINESS ANALYTICS
Regression Statistics
Multiple R 0.2603625
R Square 0.0677887
Adjusted R
Square
0.0566909
Standard
Error
353.93162
Observatio
ns
86
ANOVA
df SS MS F Significanc
e F
Regression 1 765174.
7
765174.
7
6.10832
2
0.01547425
4
Residual 84 105224
78
125267.
6
Total 85 112876
52
Coefficien
ts
Standar
d Error
t Stat P-value Lower 95% Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 425.7078 83.7162
1
5.08513 2.20E-
06
259.228937
4
592.186
7
259.228
9
592.186
7
Price 8.93E-05 3.61E-
05
2.47150
2
0.01547
4
1.74E-05 0.00016
1
1.74E-
05
0.00016
1

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PRINCIPLES OF BUSINESS ANALYTICS
References
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Delen, D. and Ram, S., 2018. Research challenges and opportunities in business analytics. Journal of
Business Analytics, 1(1), pp.2-12.
Duan, Y., Cao, G. and Edwards, J.S., 2018. Understanding the Impact of Business Analytics on
Innovation. European Journal of Operational Research.
Hasić, F., De Smedt, J. and Vanthienen, J., 2018. Augmenting processes with decision intelligence:
Principles for integrated modelling. Decision Support Systems, 107, pp.1-12.
Hazen, B.T., Skipper, J.B., Boone, C.A. and Hill, R.R., 2018. Back in business: Operations research in
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