Exploring Business Analytics: Tools, Forecasting, and Data Analysis

This report provides a comprehensive overview of business analytics, highlighting its importance and various tools used by companies for data interpretation and decision-making. It discusses business forecasting methods, including general, financial, accounting, and demand forecasting, essential for predicting future trends and threats. The report also delves into data exploration, emphasizing the significance of data visualization through bar graphs and correlation analysis to understand data patterns and relationships. Furthermore, it explores dimensionality reduction techniques like Principal Component Analysis and High Correlation Filter to simplify complex datasets and improve model performance. Finally, the report evaluates classification models, including Naïve Bayes and k-NN Algorithms, outlining their advantages and disadvantages in predictive modeling. This document aims to provide insights into leveraging business analytics for organizational success, and similar resources can be found on Desklib.

FORMAL REPORT IMPORTANCE OF
BUSINESS ANALYTICS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Task 1...........................................................................................................................................3
A Presenting business analytical tool..........................................................................................3
B. Describing business forecasting methods...............................................................................4
Task 2...........................................................................................................................................5
A. Describing data exploration and importance of visualization..............................................5
B...................................................................................................................................................7
Task 3...........................................................................................................................................7
A Presenting Dimensionality Reduction techniques...................................................................7
C Explaining classification model...............................................................................................9
Task 4...........................................................................................................................................9
Naïve Bayes.................................................................................................................................9
Classification of Naïve..........................................................................................................10
Advantage and disadvantage of k-NN Algorithms....................................................................10
CONCLUSION..............................................................................................................................10
REFERENCES................................................................................................................................1

INTRODUCTION
Business analytical is a technological tool that is used by companies in order
interpreted and communicate the data. Moreover, this data analysis helps in making the right
decision for the organization that contributes in maintaining the stability of firm. The
present report will evaluate the benefit of business analytics to a business organization as
well as discuss on the forecasting methods. That can be used by small and medium size
business in order to predict the future threat. Along with this, it will also depict about the data
exploration tool and its importance in marinating the business. Furthermore, it will discuss
about the dimensionality redemption techniques and the way it can used in company.
Lastly, it will evaluate about the advantage and disadvantage of k-NN Algorithms.
MAIN BODY
Task 1
A Presenting business analytical tool
Business analytical too is set of technologies that is used for solving the business
issues with help of data analysis, quantities methods and statistical model. Moreover, it
involves methodological exploration for re arranging the company data as well as lay
emphasis on static analyse in order to take make decision. Along with this, data driven
organization use their data as assets and work in order to improve it so that it can
contribute in increasing competitive advantage (Ashrafiand et.al., 2019). Furthermore, this tools
provide the organization with insight that inform business decisions. There are three
types of analytical tool such as descriptive analytical that tracks key performance
indicator in order to understand the present business state. The another tool is known
as predictive analytical that contribute in analysing trend data that assess the like hood of
future outcomes.
Along with this, perspective analysis that use past performance so that it can
generate recommendation for handling the similar situation in the future. However, there
are certain advantage and disadvantage of using business analytical tool that is used by
companies. Such as it helps in monitoring the progress of the mission set by the firm.
The quantified data helps in gaining the clear picture of company performance (Aydinerand
et.al., 2019). As well as what need to be expected from the employees and what areas of

organization need to be enhanced so that it can be more productive. In addition to this, it helps
in increasing the efficiency such as it helps in gathering the large amount of data in
fast manner as well as display in formulated way. That helps in achieving a particular
organizational goals. It also encourages the culture efficiency and teamwork because it
allows the subordinates to share their insights and contribute in decision making
process (Bayrak, 2021). Moreover, it helps in keeping the company update about the
change in customer taste and preference that helps in providing with the market
insights. That will contributes in meeting the needs and demands of customer as well
as increase the profit.
B. Describing business forecasting methods
Business forecasting is the process used for predicting the future along with the
way company will respond to it. It basically involves the collection of data from primary
and secondary method. Along with this, it will analyse about the data by creating the
strategies as well as comparing the forecasting model to realize the outcomes. Thus, it
helps the organization in making the right decision and strategies that will contribute in
success of firm. The forecast can be regarding the sales that helps the company
identifying the total sales in a year (Chen. and Siau, 2020). Along with this, it can be
regarding the particular topic such as customer needs and demands regarding specific
product and services. There are four common types business forecasting way that help in
analysing small and medium size of business.
 General forecasting:
 This techniques are used in order to identify the overall performance of the firm
in the business environment. Moreover, it provides the company with overall business
climate that allows the firm to know the external and internal threats and
opportunity. Along with this, this tool can be used any type of industry and
business (Chiang and et.al., 2018). It is used for analysing the overall market
condition as well as the impact of environmental factor on the business operation.
It is best used for business that operates in influential environment such as
nations that are experiencing the political as well as dramatic seasonal shifts.
 Financial forecasting:

 It helps the company in providing with the clear picture of where the
organization is headed. It mainly consist of weighting the liabilities and asset of
the frim as well as capital structure. However, it allows the organization to give
the detail about the cash flow and general market condition. Along with this, it
is best used for tracking the future performance of the company as whole
(Chung, Mustaineand, Zeng, 2021). It is best used for keeping the company the
organization on track as well as provide with financial projection.
 Accounting forecast:
 It helps the organization in forecasting the practice the predicting the future
costs that will incurred by company. Along with this, it allows the company to
evaluate the future data with help of present estimate such as how much amount
need to be paid while purchasing the raw material, rent, insurance etc. It is mainly
used for determining the future operating costs for the business ( Conboy and
et.al., 2020). Along with this, it is used for every business that is used for covering
future cost.
 Demand Forecasting:
 This forecasting method helps the organization in predicting the market needs and
wants as well as sales forecast helps the business to capitalize on the needs with
sales. It is mainly used for determining the market and customer demands for
goods or services in the future. It is best for planning about the way invests in
raw material in order to invest in the new product development.
Task 2
A. Describing data exploration and importance of visualization
Data exploration is defining as initial step in data analysis in this process data analyst is
use data visualization and tactical techniques in order to describe dataset characterization.
Such as quantity, accuracy in order to understand the nature of the data. Along with this, it
includes both manual analysis and automated data exploration software solution. That
helps in visually explore and identify relationships between different data variables. Along
with this, data is generally collect large and unstructured volume from various source and
data analyst (Pappas. and et.al., 2018). This manual tool exploration method is entail either

through writing script to analyse the raw data into spreadsheets. However, data visualization
software helps the data analyst to monitor the data sources that contributes in performing the
big data exploration on large datasets. There are graphical display such as bar chart and
valuable tools in visual data exploration. Along with this, the most popular tool that data
manual data exploration is Microsoft Excel Spreadsheet that can be used in order to
create the basic data such a s raw material and identify the correlation between variables.
In addition to this, in order to identify the correlation the two way table method, chi-
square test are most effective. However, the most data analytical software includes
visualization tools. Along with this, it is very important as human process the data better
then numerical (Pröllochs and Feuerriegel, 2020). Thus, it was very challenging for data
scientist and data analyst to solve and find out the meaning of thousands of rows and
columns. However, in data visualization the data analyst can easily exploration the data.
With help of various shapes dimension, colour and angle. All these feature helps in
defining and figuring the data. Moreover, it helps in identifying the relation between
the variables as well as give better analysis of company performance.

From the above bar graph it has been represented that frequency measurement vary
with the age group. Such as the measurement of 0-20 age group is 6 while it is 38 for the
age group of 91-100. Thus, it can be identify that there is fluctuation in the graph and it
is not symmetrical.
B
From the above graph it has been identified that there is a strong relationship between money
spent on training and development (T&D) and sales revenue. There has been 88% relation if 2
rupees has been investing on the training then sales revenue will increase by 120. Thus, it can be
stated that if company spent higher money on T&D, then their sales revenue will definitely
increase.
Task 3
A Presenting Dimensionality Reduction techniques
It is defining as number of input feature, columns and variables that is present in
dataset is known as dimensionality and the process of reducing all these features are
known as dimensionality reduction. This techniques are mainly used in huge number of
input feature that makes the prediction more complicated. Thus, it is mainly used to
reduce the high number feature. Moreover, in other words it is a way of converting the
higher dimension into lower data se and make sure that it provides similar
information (Ramanathan and et.al., 2017). However, it is mainly used in machine learning in
order to obtain better fit predictive model while solving the regression and

classification problems. Along with this, it is commonly used in high dimensional data
such as bioinformatics and speech recognition. It can also be used for data visualization and
cluster analysis. There are various benefits of applying dimensionality reduction such as
by reducing the features the space can be required to store the dataset also gets reduce.
In addition, to this, it required less computation training Time for reducing the dimension
features. Along with this, it removes the redundant feature by taking care of multi
collinearity and it helps in visualizing the data quickly. Moreover, the common
techniques of dimensionality reduction are:
 Principal component analysis:
 The Principle components is statically process that converts the correlated features
into set of linearly uncorrelated feature with the help of orthogonal transformation.
These new transformation feature is known as principle components and it is
used for exploratory data analysis and predictive modelling. Along with this, this
technique considers the variation between each attributes because the high
attributes show the good split between the class. Thus, it reduces the
dimensionality (Seddon. and et.al., 2017). Some common example are image
processing, optimizing the power allocation in various communication channels.
 High correlation filter: It refers to the case when tow variables carry similar
nature of information And because of this factor it can lead in degrading the
performance of model. However, the correlation between the independent
numerical give the calculated value of the correlation coefficient. If this value
is higher than value than one of the variable from the database can be
removed.
Level of
Education
Annual Income
£000’s
Level of
Education 1 0.930779
Annual
Income
£000’s 0.930779 1

From the above table, it has been interpreted that there is a high correlation between
annual income and level of education. It is so because the value is lie beyond 75 and that is the
relationship between both variable is high. Thus, it can be stated that when annual income raise,
there will be 93% change over annual income.
C Explaining classification model
This model is known as predictive modelling programme where the class of label is
predicted for the data that has been inputted. Moreover, classification is known as task
that requires the use of machine learning algorithms. There are many different types of
classification model that can be used in machine learning. Such as calcification
predictive model, binary classification that is used to predict one or two classes. Another
method is multipliable classification that involves one or more classes. Along with this,
classification model can be evaluated through different range of metrics such as
classification accuracy (Ashrafiand et.al., 2019). This method showcase how many of
the prediction is correct. In some case it present how good a model is but in other case
accuracy is simply not enough. Furthermore, confusion matrix will provide with insights
into the prediction. Hence, it helps in finding that accuracy is proper or not. It also
show the truth and false prediction on each class. The positive prediction is represented
as ok while false positive is known as not Ok.
Task 4
Naïve Bayes
It is simple and powerful algorithm for predictive modelling however this model is
compromise of two types of profitability that can be calculated directly from the training
data. The first probably is related to each class while the other is known as conditional
probity method. In addition to this, by using the Bayes theorem the new data prediction
can be made easily. It is called as naïve bade because it assumes that each input
variable is independent. Moreover, this techniques are very effective on large range of
complex problems and the main reason behind this is to try the classify the data by
maximizing using this theorem (Chen. and Siau, 2020). However, Bayes theorem is simple
mathematical formula that is used for calculating the conditional probabilities. It mainly
measure the probability of an event occurring given that another event has occurred.

Classification of Naïve
It is mainly suitable for binary and multiclass classification algorithm. This
techniques mainly classified the future objects by assigning class labels to
instances using conditional probability. Moreover, in supervised classification training
data is already labelled with the class. For instance, if fraudulent transaction is
already flagged in transactional data then this type of classification would be known
as supervised. In order to make use of this method the training data base should be
adequate enough that represent the entire population and contain every combination of
class label and attributes. Moreover, it performs well in case of categorical input
variables compared to numerical variables (Conboy and et.al., 2020). Along with this, these
techniques is useful for evaluating many applications such as weather forecasting based
on temperature, pressure and humidity of a company can be predicted such as weather will
be sunny or windy. Along with this, it will also analysis the fraud that is based on various
bills submitted by an employee for reimbursement for expenses on travel and food. Thus
it helps in predicting the like hood of fraud.
Advantage and disadvantage of k-NN Algorithms
The KNN algorithm is mainly used in ML application that is used right from supervised
settings such as classification and regression. Along with this, it retrieving similar item in
application such as recommendation system.
Advantage:
 It is very simple to implement and easy to understand.
 It helps in making highly flexible decision and while using this data analyst can learn
nonlinear decision boundaries.
 This algorithm does not require training as all the work happens during prediction.
Disadvantage:
 High prediction complexity for large datasets.
 High Prediction complexity with higher dimensions.
CONCLUSION
It has been concluded that there are demonstrated the business analytics tools are need to
considered in order to explained the background and additional benefits of the business
organisation. Also, discussed the forecasting methods for small and medium sized company that

are help to predict about the future capital and revenues with capital cost of business. Moreover,
the report critically evaluated data exploration and its important and also mentioned the
correlation between income and education. Further, discussed the native represents with Naive
Bayes and also critically evaluated its classification with its application. Lastly explained
advantages and disadvantages of it.

REFERENCES
Books and journals
Ashrafi, A. and et.al., 2019. The role of business analytics capabilities in bolstering firms’ agility
and performance. International Journal of Information Management. 47. pp.1-15.
Aydiner, A. S. and et.al., 2019. Business analytics and firm performance: The mediating role of
business process performance. Journal of business research. 96. pp.228-237.
Bayrak, T., 2021. A framework for decision makers to design a business analytics platform for
distributed organizations. Technology in Society. 67. p.101747.
Chen, X. and Siau, K., 2020. Business analytics/business intelligence and IT infrastructure:
impact on organizational agility. Journal of Organizational and End User Computing
(JOEUC). 32(4). pp.138-161.
Chiang, R. H. and et.al., 2018. Strategic value of big data and business analytics. Journal of
Management Information Systems. 35(2). pp.383-387.
Chung, W., Mustaine, E. and Zeng, D., 2021. A computational framework for social-media-
based business analytics and knowledge creation: empirical studies of CyTraSS. Enterprise
Information Systems. 15(10). pp.1460-1482.
Conboy, K. and et.al., 2020. Using business analytics to enhance dynamic capabilities in
operations research: A case analysis and research agenda. European Journal of Operational
Research. 281(3). pp.656-672.
Pappas, I. O. and et.al., 2018. Big data and business analytics ecosystems: paving the way
towards digital transformation and sustainable societies. Information Systems and e-Business
Management. 16(3). pp.479-491.
Pröllochs, N. and Feuerriegel, S., 2020. Business analytics for strategic management: Identifying
and assessing corporate challenges via topic modeling. Information & Management. 57(1).
p.103070.
Ramanathan, R. and et.al., 2017. Adoption of business analytics and impact on performance: a
qualitative study in retail. Production Planning & Control. 28(11-12). pp.985-998.
Seddon, P. B. and et.al., 2017. How does business analytics contribute to business
value?. Information Systems Journal. 27(3). pp.237-269.
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