ITECH7406: Business Intelligence Report

This report, submitted for ITECH7406: Business Intelligence and Data Warehousing, uses a dataset of greenhouse gas emissions from 16 countries (2010-2014) to demonstrate business intelligence principles. It includes a descriptive analysis using SAP Lumira, visualizing emissions via geographic Choropleth charts and column charts. A predictive analysis, employing triple exponential smoothing, forecasts emissions for three future years (2015-2017). The report justifies the dashboard's design and concludes with recommendations for a CEO to improve organizational performance and competitiveness by incorporating environmental factors into decision-making. The report showcases the application of business intelligence techniques to environmental data for strategic decision-making.

Running head: ITECH7406- Business Intelligence and Data Warehousing
TECH7406- Business Intelligence and Data Warehousing
Name of the student
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ITECH7406- BUSINESS INTELLIGENCE AND DATA WAREHOUSING
Executive summary
The following report has been designed and compiled for the understanding of the
business intelligence procedures and the working of the data warehousing. The report
has been compiled with the help of an environmental issue related data set and a
descriptive and predictive data analysis has been done on the data set. There is an
inclusion of the justification of the dashboard that has been used for the compilation of
the data set. To conclude the report there is an inclusion of recommendation for the
CEO of the organization to follow which will help in them to increase their profit and
change their objectives in order to make their organization better in the market
competition.

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Table of Contents
Introduction........................................................................................................................3
Description of the data set used for the analysis.........................................................3
Descriptive analysis..........................................................................................................4
Predictive analysis..........................................................................................................10
Justification of dashboard usage..................................................................................17
Recommendation to the CEO.......................................................................................19
Conclusion........................................................................................................................19
References.......................................................................................................................21
Bibliography.....................................................................................................................23

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Introduction
Business intelligence is the term, which includes the use of different application
and infrastructures for the analysis of data for the improvement of decision making for
an organization to improve the performance (Arnott, Lizama & Song, 2017) (Shollo &
Galliers, 2016). The report has the discussion based on environmental issues around
the world. The report further discusses about the data set that has been used for the
analysis, the descriptive analysis of the data set created on the software, the prediction
done from the data set used, a brief justification of the dashboard that has been created
and the recommendation to the CEO of the organization (Basole, 2014).
Description of the data set used for the analysis
The data set, which has been used for the analysis in this report, consists of 16
locations and the subsequent carbon monoxide (CO), nitrous oxides (NOX), sulfur
oxides (SOX), Volatile organic compound (VOC) and carbon dioxide (CO2) gas
emissions. The locations which has been chosen for the analysis of the data are:
1. Argentina
2. Australia
3. Brazil
4. Canada
5. China
6. France
7. Great Britain

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8. Indonesia
9. India
10. Italy
11. Japan
12. Korea
13. Mexico
14. Russia
15. Saudi Arabia
16. Turkey
17. United States
The original dataset which had been found was large. For the simplicity of the
predictive analysis of the data the data set was trimmed down to the respective dataset.
The data has been collected for a period of 5 years from 2010 to 2014. This amount of
data has been collected from various sources and analysis has been done. As per the
nature of the data chosen for the analysis, the predictive research cannot be compiled
with the help of the SAP Predictive Analysis. The predictive analysis has been done on
the same data with the help of triple exponential smoothing. The prediction of the data
has been done for 3 future years.
Descriptive analysis
This section describes the data set which has been selected for the analysis of
the report. The following charts and description provide a brief insight about the data.

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Figure 1: CO, CO2, NOX, SOX and VOC gas emission for selected country by
year
The above chart shows a column chart where all the data that has been collected
for the analysis has been plotted against the relevant years. The visual representation
provides the reader to understand the total sum of CO, CO2, NOX, SOX and VOC
gasses that are emitted from the 17 countries of the world. From the visual it can be
seen that the highest amount of gas which is emitted in the world is the CO gas. The
CO is followed by NOX and eventually, VOC, CO2 and at the end with the lowest
amount of emission is SOX. The data which has been analyzed is from 2010 to 2014
and the gradual decrease in the height of the columns shows that the countries have
tried to reduce the amount of pollutant that has been produced by the respective
countries. The sum of NOX gas emission over the years has also decreased. Apart from
the value of CO2 all the rest of the gas emissions has decreased over the period of 5
years. The effect of CO2 is more than the other gasses combined and due to the recent
rise in the population of the world the amount of vehicles and the use of fuels has

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increased subsequently. This factor becomes the most important constant for the
increase in the emission of the CO2 in the atmosphere.
The following set of charts has been produced on the SAP Lumira. The charts
which has been developed is termed as geographic Choropleth chart. The chart shows
the use of saturation of a single hue of color on the locations. The locations with the
highest amount of a factor is colored into a darker hue of the color. The lower amount of
factors are colored lighter in contrast to the others. This helps in understanding a visual
representation of the data related to the respective countries.
Figure 2: CO gas emission by country on a Geographic Choropleth Chart
This chart gives the reader an overview of the amount of CO emission in the
world. The countries that has been selected for the analysis of the report has been
marked on the chart. The above chart is a geographic Choropleth chart. This shows the
values related to the location in terms of concentration of the color corresponding to the
respective location on the earth. The darker the color on the map, that country produces
higher amount of CO emission in the world. From the chart produced it can be

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determined that the highest amount of CO gas emission comes from the United States
of America. This can be due to the high population and subsequent better use of the
factories for the production of electricity and other factories in and around the country.
From the chart it can be said the next highest producer of CO is the country of Russia.
From the color of the rest of the countries from the world chart it can be said that the
other countries have a much lower production of the CO gas.
Figure 3: CO2 gas emission by country on a Geographic Choropleth Chart
The above chart shows the sum of the total amount of CO2 gasses emission that
has been produced by the country over the period of 5 years. Form the visual analysis
of the chart it can be said that the highest amount of CO2 emission has been produced
by the countries of china. This can be justified by the fact that the amount of population
has a direct effect on the production of the CO2 gasses by the country. China is
followed by the country of United States of America. From the trend in the selection of
the countries it can be said that more advanced the people of the country is the higher
the amount of CO2 is emitted by the country. The lowest amount of CO2 emission can

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be said to be by Russia, Canada, Brazil and India. This has been concluded by the fact
that the lightest color of the chart.
Figure 4: NOX gas emission by country on a Geographic Choropleth Chart
The above chart shows the emission of NOX gas by the country’s over the period
of 5 years. The highest amount of NOX emission can be said to be from the country of
United States of America. The next highest emitting country can be said to be by the
country of Russia. The rest of the country’s when compared to the highest two country’s
produce a very small amount of NOX gasses. Thus the two countries should start to
control the NOX gas emission from their countries. This would seriously help the world
in become better in terms of better atmosphere of the world.

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Figure 5: SOX gas emission by country on a Geographic Choropleth Chart
The above chart shows the sum of the total amount of SOX gas emission over
the period of 5 years in the respective countries around the world. The amount of SOX
gasses emitted in the world is the highest in the country of United States of America.
The United States of America is followed by the country of Russia. The green color of
the country of Canada, china and India has a lighter hue which shows that the countries
have a lower emission of SOX gasses in the country. The lowest amount of SOX
emission has been recorded by the country of Italy. From the legend of the coloring it
can be said that the country of Australia has a medium colored hue which makes it lie in
the intermediary position of the emission chart in terms of SOX.

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Figure 6: VOC gas emission by country on a Geographic Choropleth Chart
This is the final chart in the descriptive analysis of the data has been made on
the VOC gas emission by the country over a period of 5 years. VOC is the group of
volatile compounds which are present in the building materials used in the construction
of buildings. The gases are released slowly into the air and thus pollutes the air. The
gases can be seen to be of the highest emission is from the country of United States of
America. There can be seen a medium amount of emission from the country of Russia.
The rest of the countries though small also produce a considerable amount of VOC
gasses and contribute to the atmosphere pollution.
Predictive analysis
Predictive analysis has been defined as a branch of advanced analytics which is
often used by different organization and analysts to predict many unknown future events
(Forsgren & Sabherwal, 2015). The procedure makes use of many different analytical
applications and tools like data mining, modelling, statistics and artificial intelligence for
the analysis of the current data which would subsequently be used to predict the future

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events. The analysis uses a number of predictive modelling for helping the management
and modelling business procedure for the prediction of the future (Petermann et al.,
2014) (Wu, Chen & Olson, 2014). The use of predictive analysis models helps to
capture the relationship among different factors which help the mangers to predict the
future and eventually make decisions about the working procedure of the organization
(Isik, Jones & Sidorova, 2013). Thus with the effective use of the predictive analysis the
organization will be able to make use of large amount of data and thus make decisions
which would help the organization for their benefit.
The method used for the predictive analysis of the data set is the triple
exponential smoothing (Kulkarni, Robles-Flores & Popovič, 2017). This procedure has
been used for the analysis because of the amount of data which has been used. The
report has been compiled for 3 future years (Rausch, Sheta & Ayesh, 2013). Thus the
latest year which the report provides the data is for the year of 2017. The data would
help the organization to compose new objectives which would help them to make
prediction for their future benefit. The predictive analysis has been done on a line chart
as the software does not allow other type of charts to be used for the predictive analysis
(Laursen, & Thorlund, 2016).