ITECH7406: Business Intelligence and Data Warehousing Report Analysis

This report provides a comprehensive analysis of business intelligence and data warehousing, utilizing SAP Lumira to explore freshwater resources data. The report includes descriptive analysis using column charts and geographic choropleth charts to visualize data trends from 2011 to 2015, covering aspects like water availability, consumption, wastage, recharge, and pollution across various countries. Predictive analysis is performed using triple exponential smoothing to forecast data for the years 2016-2018. The report justifies the use of dashboards and offers recommendations for a CEO to improve business objectives. The report aims to provide insights into data analysis, predictive modeling, and the application of business intelligence tools for informed decision-making.

Running head: ITECH7406- Business Intelligence And Data Warehousing
ITECH7406- Business Intelligence and Data Warehousing
Name of the Student
Name of the University
Author Note

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ITECH7406- BUSINESS INTELLIGENCE AND DATA WAREHOUSING
Executive summary
This report deals with designing and compilation for the better understanding of the
business intelligence process along with the working procedure of the data
warehousing. The following report has been strategically assembled with the help of
an environmental related to the renewable fresh water resources data set. The data
set has been used for the descriptive and predictive analysis of the data collected.
The report includes a justification on why the dashboards has been included in the
report. For the conclusion of the report, there is an inclusion of a recommendation for
the CEO of an organization, which can be followed. The recommendation can be
followed by the organization to improve their profit margin change the objectives of
their business procedure to make the organization better in the competition of the
market.

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Table of Contents
Introduction.......................................................................................................3
Description of the data set used for the analysis..............................................3
Descriptive analysis..........................................................................................5
Predictive analysis............................................................................................9
Justification of dashboard usage....................................................................14
Recommendation to the CEO.........................................................................16
Conclusion......................................................................................................17
References.....................................................................................................18
Bibliography....................................................................................................20

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Introduction
Business intelligence can be termed as the use of different application
software’s and infrastructures. This is used for the analysis of information and data
for the use in the improvement procedure of the decision making of an organization
for the direct improvement of the performance. For the compilation of this report, a
software by the name of SAP Lumira has been used. The software has been used
for the benefit of the effect of changing the structure of the data that is being used for
the analysis. The correlation between the data can also be visualized and analyzed
for the benefit of the organization and decision making procedures. The report has
been compiled with the help of the environmental related issues of the world (Shollo
& Galliers, 2016) (Arnott, Lizama & Song, 2017). The following report has been
strategically assembled with the help of an environmental related to the renewable
fresh water resources data set. The data set has been used for the descriptive and
predictive analysis of the data collected. The report includes a justification on why
the dashboards has been included in the report. There is a further description of the
discussion based on the descriptive analysis of the data set, which has been
compiled on a data analysis software, future prediction of data set that has been
used for the analysis and a justification for the creation of the dashboard. There is
also a brief recommendation to the CEO of an organization based on the data
analysis compiled in the report (Basole, 2014).
Description of the data set used for the analysis
The data set that has been used for the analysis of the repost is based on the
concept of freshwater resources in the world. The factors which are being tallied
against are Fresh water availability, Fresh water consumption, Fresh water wastage,

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Fresh water recharge and Polluted fresh water. These factors have been selected for
a period of 5 years ranging from 2011 to 2015. The data has been collected from
various locations, which include:
1. Argentina
2. Australia
3. Brazil
4. Canada
5. China
6. France
7. Germany
8. India
9. Indonesia
10. Italy
11. Japan
12. Korea, Dem. Rep.
13. Mexico
14. Russia
15. Saudi Arabia
16. South Africa
17. Turkey
18. United Kingdom
19. United States
The original data set, which had been, acquired form the online sources were
large and would make the analysis complicated. To produce a simple yet detailed
analysis of the data the data was reduced to the data set that has been chosen. The

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data has been collected from various online sources. It was then compiled into one
data set for the purpose of this analysis. The data has been strategically plotted
against the best possible chart type to show how the factors are affecting the fresh
water resources. An extensive study of predictive analysis has also been done on
the data with the help of SAP Predictive Analysis. The data set has been used for
the predictive analysis using the triple exponential smoothing method. The data has
been predicted for 3 years into the future.
Descriptive analysis
This section of the report deals with the description of the different aspect of
the data set representation on the charts and graphs. The descriptive analysis
provides a brief overview of the working of the data set in the software used.
Figure 1: The factors of the fresh water dataset plotted against the years
This cart has been compiled using the data of the data set. The chart shows
the data set in the form of a column chart. The bars height depict the values of the
data set in a summation format. All five data has been used in this chart to show a

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comparative manner of the data set that is being used. The columns have further
been divided into groups of five into five groups. The data division is in the form of
the 5 years. From the graph, it can be seen that the amount of fresh water availability
is fluctuating over the year. In 2012, it reduced and again it rose the next year in
2013. However, there has been a constant maintenance of the consumption of the
fresh water over the period of 5 years. Thought he amount pf fresh water wastage
and pollution of fresh water is comparatively low when looked in respect of the
availability and the consumption of the fresh water, the two factors should be made
to null to make the environment better for the people to live in.
The following set of charts depict the Geographic Choropleth charts, which
has been created using the software SAP Lumira. The charts show the data in form
of saturation of a color of a single hue. The darker the color is the more amount of
value is there in the data set for the respective country. The use of this chart helps in
the visual understanding of the data set that has been used for the analysis.
Figure 2: Geographic Choropleth chart of the Fresh water availability

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The above graph shows the Fresh water availability on the earth for the
countries selected in the dataset. The darkest of the area is Russia, which shows
that they have the highest amount of fresh water available for their nation. Canada
and United States of America closely follow them. Rest of the countries with a lighter
shade of the hue can be termed as countries with the least amount of fresh water
available for their countries. This can be crucial, as the organizations in their
countries should come forward to help them to reach the point where there is
abundance of water for every one of the country.
Figure 3: Geographic Choropleth chart of the Fresh water consumption
The above chart shows the Fresh water consumption rate in the countries.
The darkest of the chart can be said to be of Russia, china, Canada and Brazil.
These countries have a high number of population and thus can be said to have a
high consumption rate of fresh water in their country. People require the fresh water
to have a better sustainable environment and to lead a good life. With the rising
scarcity of water, these countries would have to use the water strategically or else
these countries would soon deplete out on the fresh water resources. The least

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amount of fresh water consumption can be said to be done by the country of India.
This can be determined with the help of the color of the country from the chart.
Figure 4: Geographic Choropleth chart of the Fresh water wastage
From the above chart it can be seen that the amount of fresh water wastage
that is being done in the countries around the world is high. This has to be reduced.
The leader in the wastage of the fresh water is Russia. A misconception from this
chart can arise that the Russians are the leaders in the fresh water consumption as
well as the leader in the wastage of fresh water. However, it is completely all right
due to the fact that the charts are not showing the number. The number can be seen
in the figure 1 above which shows the amount of water being used and wasted. This
would help in understanding that not all the amount of water is wasted away.

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Figure 5: Geographic Choropleth chart of the Fresh water recharge
This chart shows the amount of fresh water that is being recharged into the
natural habitat the darkest of the color is in the country of Russia which shows that
the country can help in the reproduction of the available water resources in the
country. This factor should be followed by the world to make them understand the
working procedure of the water recharging mechanism so that the future can have
enough water for them to drink. Russia is closely followed by Canada and Australia.
Though Australia can be considered to be a majority of dry states still the amount
shows that they are looking forward for the betterment of the country as well as the
world.
Figure 6: Geographic Choropleth chart of the Polluted fresh water
The amount of fresh water that is being polluted in the world has been shown
in this chart. The darkest color is in Mexico followed by the United States of America.
The amount of fresh water pollution needs to be reduced by the world and made null
so that the people of the world can use the fresh water for their consumption.

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Predictive analysis
Predictive analysis of a set of data can be defined as the advanced
procedural analysis, which is often used, by an organization or a company to
determine the future effect of the same factors (Forsgren & Sabherwal, 2015). The
factors in the point of discussion can be determined for the future by understanding
the trend of the data that is being used. The procedure of predictive analysis uses
the multiple analytical tools available to the user on the applications interface. These
tools include the use of data mining, data modelling, artificial intelligence as well as
data statistical analysis. These tools can be used by the user on the software to
predict the data from the future based on the trend that the data already has. The
predictive analysis uses different models to help the management to understand the
prediction of the data set (Petermann et al., 2014). The use of the predictive analysis
procedure helps the managers to understand the relationship among the various
factors, which would help the manager to understand the trend in the data and to
make the predictive decision making based don the objectives of the organization
(Isik, Jones & Sidorova, 2013). Thus with the use of the predictive analysis of the
data the organization will be able to make sure that the large amount of data can be
used to produce better decision making process for the organization (Wu, Chen &
Olson, 2014).
The method, which has been used, for the predictive analysis of the data set
is the triple exponential smoothing (Kulkarni, Robles-Flores & Popovič, 2017). The
triple exponential smoothing has been used for the predictive analysis because the
amount of data, which has been selected for the analysis of the data, is small. The
prediction on the data set has been done for three future years (Rausch, Sheta &
Ayesh, 2013). The data, which has been used, is from 2011 to 2015. Thus, the

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prediction has been done for the years 2016, 2017 and 2018. The predicted data
would help the organization to produce new objective, which would help them to
make their organization better in terms of profit received and in terms of the future
benefit. The line chart has been used to determine the predictive analysis because
the software would not be able to predict the data on other form of charts (Laursen,
& Thorlund, 2016).
Figure 7: Prediction of the Fresh water availability with respect to the years
using triple exponential smoothing
The prediction of the data of the amount of fresh water that is available in the
world shows that there would be a rise in the amount of fresh water available as a
total sum of the world in the year 2016. However, there is also a concern about the
steep fall in the value in the year 2017. This shows that there needs to be a high
conservation of the fresh water in the coming years. However, a rise in the value can
be seen in the year 2018 but it has not reached high as the year of 2016.