Description of the Data set used for the Analysis
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).
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, 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:
- Argentina
- Australia
- Brazil
- Canada
- China
- France
- Germany
- India
- Indonesia
- Italy
- Japan
- Korea, Dem. Rep.
- Mexico
- Russia
- Saudi Arabia
- South Africa
- Turkey
- United Kingdom
- 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 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 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
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 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.
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.
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 & Popovi?, 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 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.
Figure 8: Prediction of the Fresh water consumption with respect to the years using triple exponential smoothing
The amount of fresh water consumption can be seen that the people of the world are very much concerned about it and are restricting themselves to a limited amount every year. There can be seen a slight rise in the consumption in the year 2017 but it has a reduction in the following year of 2018. The amount of fresh water consumption cannot be lowered more than this. Thus, the other factors which are there needs to be considered and reduced.
Figure 9: Prediction of the Fresh water wastage with respect to the years using triple exponential smoothing
The wastage of the water should be reduced as seen from the prediction. 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.
Figure 10: Prediction of the Fresh water recharge with respect to the years using triple exponential smoothing
Fresh water recharge needs to be increased for the world to have a better place to live in. 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.
Figure 11: Prediction of the Polluted fresh water with respect to the years using triple exponential smoothing
The polluted fresh water in the world can be said to be in a constant phase of linear continuity. This shows that the pollution of the water can be easily controlled by the world. Moreover, a small drop in the value can be seen in the year 2017, which is a good sight for the fresh water bodies in the world.
Figure 12: Dashboard on the software showing the analysis of the amount of fresh water available
Figure 13: Dashboard on the software showing the analysis of the amount of fresh water being polluted
The form of dash board has been chosen for the depiction of the different factors of the analysis of the dataset is due to the fact that the two of the most important variable of the data set is the availability of fresh water and the amount of fresh water that is being polluted every year. Fresh water resources are limited in the world. If they are not recharged periodically, then the amount of fresh water available will deplete away. The fresh water is the only source of available drinking water which can be directly consumes. Other water sources needs to be treated first with chemicals and procedures to make it drinkable. For the organisation to understand the importance of the fresh water the dashboards have been created. Though majority of the organisation is bale to implement the procedures of water treatment for their daily processes, they waste generated should be disposed off carefully so as not to harm the naturally available fresh water resources. It has been seen that many of the organisation who has a company using harmful chemicals the waste generated is disposed off into the nearby water. This causes the pollution of the water body and in turn harming the environment.
To the CEO of the organization it would be recommended that they should setup their objectives and rules to follow the decision-making procedure of the managers. The decisions made during the time should be related to the environmental factors, which are to be also considered. During the analysis of the report specific qualities have been kept in mind. The CEO should start to invest in different factors related case studies, which would help the organization to understand their position in the current market due to the impact of the environmental factors. 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. Calculation of the return of investment of the procedure that is to be taken up by the organization would help them to understand if the decision they were about to take would help them in the end or not. Understanding the sale pattern of the products of the organization based on the environmental factors would also help the organization to assess the objectives, which they have taken up for the betterment of their organization. Apart from this assessing of the similar connected factors with the ones selected would help in making the relatable factors to the sale of products and services become well.
Conclusion
From the above report, it can be easily concluded that the use of the term business intelligence has many concepts. These concepts are many times are not taken into consideration when the managers are requested to do decision making statement for the organization they are serving. 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 data set has been used for the descriptive and predictive analysis of the data collected. The analysis of the data makes use of both the structured and unstructured data. The report includes a justification on why the dashboards has been included in the report. 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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