Introduction to Business Analytics

BUSINESS ANALYTICS

Table of Contents
INTRODUCTION................................................................................................................................3
QUESTION 1.......................................................................................................................................3
a) Investigation of report based upon overall features.....................................................................3
QUESTION 2.......................................................................................................................................4
a) Example of effective use of regression analysis..........................................................................4
b) Practical ......................................................................................................................................4
c) Scatter Plot...................................................................................................................................5
d) Equation of Regression Line.......................................................................................................5
5. R 2 value......................................................................................................................................5
Analytic Tool...................................................................................................................................5
QUESTION 3.......................................................................................................................................6
a) Difference between classification & Prediction..........................................................................6
b) Examples of Classification methods...........................................................................................6
c) Neural Networks..........................................................................................................................6
d) Three Examples of Clustering.....................................................................................................6
CONCLUSION....................................................................................................................................7
REFERENCES.....................................................................................................................................8

INTRODUCTION
Business Analytics includes use of different skills, technologies and practices in order to
investigate & explore past performance of a business. This is important for business organisations in
decision making. This report gives overall information included in Insight report and explain use of
that insight report in decision making. Further, this report elaborate relationship among different
variables by using regression analysis. At last, this report shows use of classification and regression
in Business Analytics.
QUESTION 1
a) Investigation of report based upon overall features
This Insight Report of CommBank present all the relevant data by using graphs and tables
which is beneficial for companies. This report includes qualitative information related to Innovation
performance, innovation behaviours of retailers, proportion in which different factors drive
innovation. Benefits of innovation in improvement of different areas are discussed in this report in
quantitative terms. Further, information related to investment in different functional areas of retail
sectors and various areas in which investment n technology is being done are elaborated in this
report. At last, percentage of return generated by CommBank with the implementation of
technology is explained in this report through graph.
b) Key information derived from report and usefulness of information in decision making
This insight report of CommBank Retail Business list out following information-
Innovation Performance
Innovation Performance of CommBank shows that a business organisation cannot not
generate overall profits from Innovation retail channels which offers all of their services with online
platform are not gaining much profits Thus, with this business organisations make policies of
providing both online & offline services to customers according to requirement of area. Further,
This information is useful as with these managers are able to chose area in which implementation of
innovation and offerings of online services are not effective. For Example- Insight report 71%
multichannel retailers are innovation active than managers make decisions of organising various
programmes for retailer so more number of retail organisations become actively use innovation
techniques(Vidgen, Shaw and Grant, 2017).
Dynamics of Information
It is find out from Insight Report of CommBank that less number of retailers are willing to
invest capital & time in implementing innovations and they are not ready to take risk. Further,
retailers are not able to hire & retain talented and technically skilled employers at work lace. This
information is useful in decision making as with this companies in retail sector make decisions
related to improvement in selection process and conduct training & development programmes for
employees so that they can learn innovative techniques.
Drivers of Innovations are productivity, leveraging, technology and growth without these
factors a retail business cannot grow in long run. Hence, this information is important as with this
CommBank make decisions related to formulation of different ideas which enhances productivity
and performance of company. Further, managers of bank monitor changes in technology and make
decision of adapting most efficient & effective technology at work place. With information of
leveraging managers are able assess capital requirement and make investment & capital decisions.
Investment in Innovation
Innovation Investment in Sales & Marketing function of CommBank is more effective

and to generate more profits thus, this information is beneficial as with this they decide in which are
they have to invest more money to earn profits.
Further, details of investment different technological areas are useful to select the most
profitable and effective technology.
Insight Report also provide information related to return generated from innovation which
influence other retailers for making investment in innovation(Seddon, Constantinidis and Dod,
2017).
c) Summary of report
From the key insight report it is abstracted that retail sector in Australia are continuously
growing as companies are adapting changes made in innovation in their business. In this Insight
report of CommBank a survey is conducted from business owners and 263 retailers are selected for
collecting responses. This survey is conducted by Common Wealth Bank and respondents is
selected from different business operators having turnover more than 5,00,000 dollars across
Australia. Further, from the insight report it is concluded that Innovation play a significant role in
improvement business operations of retail sector because with this performance & productivity of
companies are improving and it is also important in generating more returns. Moreover, it is
outlined from the insight report that most of the respondents thinks that reatail sector has to
implement innovation in Sales & Marketing as with this customers gets attracted and which in turn
enhances returns.
d) Improvement in report
It is recommended to researcher to include information related to use of innovation
technology by sectors other than retail. Further, it is also suggested to collect information which
shows impact of Innovation of economy of Australia. Moreover, researcher has to include
information related to new innovations made by retail sectors of other countries in insight report
which in turn beneficial for implementing new innovation technology in business. Opinions of
respondents is required to be used in this insight report so that retail businesses can select an
appropriate innovation method.
QUESTION 2
a) Example of effective use of regression analysis
Regression analysis may be served as the most effectual statistical tool which helps in
describing relationship between two or more variables. With regards to cited case situation
regression analysis can be used for analysing the extent to which weight is influenced from height
factor. In this:
Height: independent variable
Weight: Dependent variable
b) Practical
Number
of
student
s Height
Weig
ht
1 5.2 40
2 5.5 60
3 5.6 69

4 5.4 70
5 5.10 90
6 5.5 86
7 5.4 90
8 5.5 96
9 5.1 55
10 5.2 60
c) Scatter Plot
The above mentioned scatter diagram shows that moderate relationship takes place between two
concerned variables such as height & weight(Shmueli and Lichtendahl Jr, 2017).
Regression
d) Equation of Regression Line
Regression equation
Y = a + bx
Weight = a + b * Height
5. R 2 value
Regression Statistics
Multiple R 0.338684
R Square 0.114707
Adjusted R Square 0.004045
Standard Error 18.31858
Observations 10
Tabular presentation shows that value of R square accounts for .11 significantly. Considering
this, it can be mentioned that independent variable such as height represents 11% variability of
selected dependent variable. Thus, it can be said that lower goodness to fit exist in relation to the
selected variable.
Analytic Tool
Null hypothesis (H0): There is no statistical significant difference in the mean value of height and
weight of students.
Alternative hypothesis (H1): There is a statistical significant difference in the mean value of height
and weight of students.
IV: Height
DV: Weight
ANOVA
df SS MS F Significance F
Regression 1 347.8361 347.8361 1.036551 0.338423
Residual 8 2684.564 335.5705
Total 9 3032.4
Coeffici Standard t Stat P- Lower Upper Lower Upper

ents Error value 95% 95% 95.0% 95.0%
Interc
ept
-
109.072 177.5526
-
0.614
31
0.556
074
-
518.509
300.364
9 -518.509 300.3649
Heigh
t
33.7704
9 33.16973
1.018
112
0.338
423 -42.719 110.26 -42.719 110.26
Outcome of ANOVA table shows that p>0.05 so null hypothesis is true. In other words,
it can be inferred that statistically no significant difference takes place in the average of height
& weight.
QUESTION 3
a) Difference between classification & Prediction
Classification Prediction
Classification process is used to classify existing
information.
Prediction is applied on primary information. In
this process assumptions are made as no past
information are available with predictor.
Accuracy of information depends on ability of
classifier,
Accuracy is based on assumption made by
predictor.
Both methods are used in data analysing process and it helps analyst in formulating decision
tree.
b) Examples of Classification methods
Different types of classification methods such as Discriminate Analysis, Logistic
Regression, Classification tree and neural network are used with the purpose of analysing data.
For Example- If a Bank has to classify its customers for the purpose of giving Bank Loan
than that will be done by developing classifier model with the help of building classification
algorithm(Laursen and Thorlund, 2016).
c) Neural Networks
A network of neurons are known as Neural Network. This network record single data at a
time and compare recorded data with previous classified data in order to find out a conclusion. This
network is used in process of classification.
d) Three Examples of Clustering
Cluster is a process of identifying and grouping similar type of observations. It is useful in
business analytics as it sort different types of information.
Clustering is useful for companies in selecting its market and maximises customer base. For
Example- CommBank can make group of customers which are adapting technology and offer
services through technology to those customers. With cluster Bank is also able to classify areas in
which investment is required. Clustering is also used in CommBank to classify different factors
which are having an impact on technology.

CONCLUSION
This report outlined information included in insight report and usefulness of that
information. Further, this report summarises regression analysis and its use in identifying
relationship among different variables. At last, this report concluded with methods and examples of
classification and regression.

REFERENCES
Books and Journals
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data mining for business analytics: concepts,
techniques, and applications in R. John Wiley & Sons.
Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from
business analytics. European Journal of Operational Research. 261(2). pp.626-639.
Seddon, P.B., Constantinidis, D. and Dod, H., 2017. How does business analytics contribute
to business value?. Information Systems Journal. 27(3). pp.237-269.
Phillips-Wren, G.E. and Ariyachandra, T., 2015. Business Analytics in the Context of Big Data:
A Roadmap for Research. CAIS. 37. p.23.
Hazen, B.T. and Hill, R.R., 2018. Back in business: Operations research in support of big data
analytics for operations and supply chain management. Annals of Operations
Research. 270(1-2). pp.201-211.