Business Intelligence: Visual DSS, Power BI and Case Study Analysis

This report discusses the role of smart, connected products in business intelligence and their effectiveness in driving business success. It also covers the use of Power BI for data validation and analysis. The report includes a case study analysis and a bibliography.

Running head: BUSINESS INTELLIGENCE
Assessment item 1 – Assignment 1
Student Name:
University Name:

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Table of Contents
Part A – Visual DSS........................................................................................................................2
Question 1 Solution.....................................................................................................................2
Results analysis........................................................................................................................3
Question 2 Solution.....................................................................................................................3
Results analysis........................................................................................................................5
Question 3 Solution.....................................................................................................................6
Results analysis........................................................................................................................7
Part B – Power BI............................................................................................................................8
Solution for Demonstration 1......................................................................................................8
First dashboard.........................................................................................................................8
Second dashboard....................................................................................................................8
Answer to questions.................................................................................................................9
Solution for Demonstration 2......................................................................................................9
First dashboard.........................................................................................................................9
Second dashboard..................................................................................................................10
Answer to questions...............................................................................................................10
Part C – Case Study Analysis........................................................................................................11
Introduction................................................................................................................................11
Smart, connected products contribute to business analytics......................................................11
Smart, connected products help to transform companies by using business intelligence.........13
Conclusion.................................................................................................................................14
Bibliography..................................................................................................................................15

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Part A – Visual DSS
Question 1 Solution
NPV model
*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = 1750000.00 '.2
Market at time (0)= 420000
Market Growth = 0.15'.2
Market Share = 0.10'.2
Total market = Market at time;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = 55.00 '.2
Cost of production = 25.00 '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
Cost of Goods sold = Sales Volume*Cost of Production
Annual overhead cost = 210000
Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate

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Model Output
Results analysis
The Net Present Value (NPV) has been estimated to be $5440551.00 based on the results
derived from the model. From the NPV value being determined in the model, it can be said that
the claim regarding NPV is correct as it has been calculated to be above $2 million.
Question 2 Solution
Monte Carlo Simulation Model
*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = UNI(100000.00,200000.00) '.2
Market at time (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2

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Total market = Market at time;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = 55.00 '.2
Cost of production = NOR(30.00,12.00) '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
Cost of Goods sold = Sales Volume*Cost of Production
Annual overhead cost = TRI(150000,215000,350000)
Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate
Model Output

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Cumulative probabilities report
Results analysis
From the results, it can said that the senior management should accept the proposed
production of the product. The decision criteria has been taken into consideration to produce
cumulative probabilities report and graph in which it has been identified that at less than 20%
chance, the NPV is $3090358.00 which is obviously greater than $1,000,000.00. Hence, the
decision to accept the proposal is correct as determined from the results.
Question 3 Solution
Monte Carlo simulation Model

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*Columns
*Years 2018,2021
*Rows
Initial investment needed(0) = 1750000.00 '.2
Market at time (0)= 420000
Market Growth = 0.15'.2
Market Share = TRI(0.05,0.10,0.15)'.2
Total market = Market at time;Total market(-1)*1.15
Sales Volume = Total Market*Market Share
Estimated selling price = UNI(45.00,65.00) '.2
Cost of production = NOR(25.00,5.00) '.2
Total Revenue = Sales Volume*Estimated selling Price '.2
Cost of Goods sold = Sales Volume*Cost of Production
Annual overhead cost = 210000
Cash Flow = Total Revenue-Cost of goods sold-Annual Overhead cost
Rate = 0.12'.2
NPV(0) = *NPV cash flow;rate

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Model Output
Results analysis
From the produced cumulative probabilities report and graph, it can be said that the CEO
will accept the proposal for production of product. The results depict that at less than 90%, the
NPV is $6619214.00 which means that there is 80% chance the net present value is greater than
$1,850,000. Hence, the decision of CEO to accept the proposal is correct and suitable.

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Part B – Power BI
Solution for Demonstration 1
First dashboard
Second dashboard

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Answer to questions
Sub-question_1
From the designed dashboard, it can be said that USA is the country that has the most
SalePrice (sum) of the DB9.
Sub-question_2
Power BI can be used to validate business assumptions in this demonstration with the
help of visualization tools. The data visualization offers functionality so that data can be
analyzed efficiently. In the particular demonstration to check which country has the most
SalePrice of DB9, the data is achieved with a single click only. Hence, Power BI is considered as
a powerful tool that can be used for making business assumptions.
Solution for Demonstration 2
First dashboard

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Second dashboard
Answer to questions
Sub-question_1
The below list is presented to illustrate the sectors that receive Research Fellowship
funding:
 Health care and social assistance
 Renewable energy
 Agriculture, forestry and fisheries
 Electricity, gas, water and waste services
 Great Barrier Reef
 Professional, scientific and technical services

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Sub-question_2
The major potential issue being faced while performing data validation based on fields is
output of wrong results. While making data validation, there may be blank data in the fields of
source data which eventually will give erroneous results. Another issue is with Data type as it
may not be retained while source data is imported in Power BI.
Part C – Case Study Analysis
Introduction
This report delineates the part played by various smart, connected products in the
business intelligence and furthermore their ease of use adequacy to drive any business towards
enormous business achievement. Because of incessant development of innovation and also items
in knowledge and associated gadgets in business applications the whole business field is getting
enhanced each day. The connection between the Business Intelligence (BI) and every single
connected product are likewise delineated in this report. With the highlights used by Business
Intelligence (BI) the business endeavors can continue enhancing their operational and practical
methodologies.
Smart, connected products contribute to business analytics
The new business abilities and enormous measure of data those are largely offered by the
smart connected products rethinks the center useful exercises of the organization. Both the cloud
based working framework and programming has turned out to be vital piece of the new items.
Joachimsthaler et al. (2015), has expressed that diverse new item creating standards are rising in
view of the assembling part and other oftentimes evolving forms. This as well as it has been
discovered that, with a specific end goal to secure the business capacities IT security is
considered as a critical part that must be kept up. So as to increase upper hands, the capacities

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must be able to open the full esteem information. Other than information open legitimate
administration, administration and information security investigation are the synchronous
capacities considering.
Besides that it is discovered that the all the individual sensor perusing are significant at
that point, over the time through recognizable proof of readings for various items the ventures
can reveal different experiences. The information accumulated from singular sensors like
temperature from the car motor, throttle position, utilization of fuel can uncover the path through
which the exhibitions are interrelated to the building detail of the cars. The explanations behind
which the issues are happening, rather the connecting mixture of the readings are useful at
whatever point the main drivers are resolved as hard to diminish (Porter and Heppelmann 2014).
The information those are created from the sensor which can quantify the rate of vibration and
warmth can conjecture the inescapable disappointment days and weeks. The application field of
enormous information investigation can join arithmetic, software engineering and business
examination methods too.
With a specific end goal to comprehend the featured examples, the huge information
investigation has in the long run utilized new extra procedures. Considering every one of these
viewpoints it is discovered that, information from the smart and connected products, inward and
outside unstructured information are huge challenge to the undertakings. As per Porter and
Heppelmann (2014), these elements can be orchestrated in an exhibit which is involves sensor
perusing, area, deals history, guarantee points of interest, temperature and so forth. Extensive
variety of information groups administration and with the customary information conglomeration
approach as far as database and spreadsheet tables are not under any condition advantageous.
One of the rising arrangements is Data Lake that can store information stream in the local

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organization. The past information and the new information can be considered with the
assistance of the investigation apparatuses that has four unique classifications, for example,
graphic, demonstrative, prescient and in addition prescriptive.
Smart, connected products help to transform companies by using business
intelligence
The business limits can be widened and the current items can be changed with the
assistance of the smart and connected products. The items those are separate and additionally
unmistakable can turn out to be a piece of the upgraded frameworks for relating the items and
segments of the framework. The organizations those have been industry pioneers from recent
decades moves the organization limits and furthermore assume dynamic part. The development
of the items and frameworks features two unique kinds of methodologies and decision in regards
to the extent of the organization (Joachimsthaler et al. 2015). The primary decision is about
whether the organization should spread their items or not and the second one is keeping in mind
the end goal to develop association between the items and data whether the organization ought to
give a stage or not. It is normal that with the assistance of one of these segments all the useful
and operational parts can be in the long run controlled.
With a specific end goal to increase enormous information openings the endeavors may
enticed to go into the important items. However, sudden passage to new items incorporates
abnormal state dangers and numerous other operational capacities too. Along these lines,
previously entering to such item the organization should recognize a clear position. Extension of
item degree is advantageous and alluring. So as to upgrade the frameworks it offers chances to
enhance execution alongside co-outlining abilities (Porter and Heppelmann 2014). The
organization should adhere to its sewing and convey open availability if their enhancement is not

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reliant on singular item configuration approach. These open doors will give propelled IT and
innovation driven condition to the organization at whatever point IT ran its course. The
organizations whose items are focal among the general items will hold the best of the situation
for entering to the related items. The makers who create lesser number of basic machines are less
competent to pull in the shoppers which are useful to take the framework in a more extensive
condition.
Conclusion
From the general discourse it can be reasoned that, enormous information examination
and Business Intelligence (BI) plays an important part to pick up business achievement. Both as
far as business achievement and upper hands enormous information investigation and business
knowledge are useful. It assembles fruitful and secured connection between the item and the
every single connected device. The business associations can use the element of these advances
to acquire the progressive changes in the field of innovation and its task. With the assistance of
cutting edge advancements the business can drive its activity and different capacities towards
enormous achievement. Aside from this, it is additionally discovered that with the assistance of
advances the organizations can quickly change their application techniques. With a specific end
goal to execute such business systems smart and connected products are likewise gainful in light
of the fact that it gives the qualities offers by the business insight. Other than these, alternate
advantages that the BI offers incorporate speedier announcing, examining and arranging
capacity. Other than enhance information quality it likewise offers enhanced buyer's fulfilment,
better business basic leadership ability, that are additionally expounded in this report.

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Bibliography
Arnott, D., Lizama, F. and Song, Y., 2017. Patterns of business intelligence systems use in
organizations. Decision Support Systems, 97, pp.58-68.
Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business intelligence
through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.
Joachimsthaler, E., Chaudhuri, A., Kalthoff, M., Burgess-Webb, A. and Bharadwaj, A., 2015.
How smart, connected products are transforming competition. Harvard business review, 93(1),
p.4.
Kowalczyk, M. and Buxmann, P., 2015. An ambidextrous perspective on business intelligence
and analytics support in decision processes: Insights from a multiple case study. Decision
Support Systems, 80, pp.1-13.
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business
intelligence beyond reporting. John Wiley & Sons.
Loya, T. and Carden, G., 2017. Business intelligence and analytics. Higher Education Strategy
and Planning: A Professional Guide, p.191.
Porter, M.E. and Heppelmann, J.E., 2014. How smart, connected products are transforming
competition. Harvard Business Review, 92(11), pp.64-88.
Power, D.J., Sharda, R. and Burstein, F., 2015. Decision support systems. John Wiley & Sons,
Ltd.

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Sallam, R.L., Tapadinhas, J., Parenteau, J., Yuen, D. and Hostmann, B., 2014. Magic quadrant
for business intelligence and analytics platforms. Gartner RAS core research notes. Gartner,
Stamford, CT.
Sauter, V.L., 2014. Decision support systems for business intelligence. John Wiley & Sons.
Sharda, R., Delen, D., Turban, E., Aronson, J. and Liang, T.P., 2014. Businesss Intelligence and
Analytics: Systems for Decision Support-(Required). London: Prentice Hall.
Sharma, R., Mithas, S. and Kankanhalli, A., 2014. Transforming decision-making processes: a
research agenda for understanding the impact of business analytics on organisations. European
Journal of Information Systems, 23(4), pp.433-441.
Torres, R., Sidorova, A. and Jones, M.C., 2018. Enabling Firm Performance through Business
Intelligence and Analytics: a dynamic capabilities perspective. Information & Management.