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What are the BI reporting solution/dashboards you will need to develop for the Senior Executives of chosen data Set– You must have at least two types of analytics i.e Predictive/prescriptive/ descriptive 10 Design Mobile application using QSR code for your 
insights /solutions/dashboard – Please provide your QSR code in your assignment
Justify why these BI reporting solution/dashboards are chosen and why those attributes are present and laid out in the fashion you proposed (feel free to include all other relevant justifications using the academic articles).
Note: To ensure that you discuss this task properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis in your assignment report (i.e. the report to the senior executive team of the company).
Furthermore, the CEO would like to improve the operations. Based on your BI analysis and the insights gained from “Data Set”, make some logical recommendations to the CEO, and justify why/how your proposal could enhance company operations, sales etc. 
Include the relevant screenshots of the BI analysis, and also any assumptions that you may have made about the analysis.

Description of the Data Set Used for Analysis

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).

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
  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.

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.

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 increased subsequently. This factor becomes the most important constant for the increase in the emission of the CO2 in the atmosphere.

Descriptive Analysis

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.

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 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.

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 be said to be by Russia, Canada, Brazil and India. This has been concluded by the fact that the lightest color of the chart.

Design of Mobile Application Using QR Code

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.

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.

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 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 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.

Recommendations to the CEO

The method used for the predictive analysis of the data set is the triple exponential smoothing  (Kulkarni, Robles-Flores & Popovi?, 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).

The above predictive has been compiled using the CO gas emission data which has been plotted against the years. The green line on the graph shows the data which has already been used in the data set. The prediction has been done on the collected data set for three years in the future from the last year the data has been collected that is 2014. The prediction on the data set has produced the corresponding blue line on the graph. The graphs has produced four points on the graph each for the year 2014, 2015, 2016, and 2017. The predicted data for the year 2014has become higher than the value which is already present is due to the fact that the values of the CO level over the years has dropped a considerable amount. A drop of more than 10,000 points in the value over the period of 5 years has prompted the predication to determine a value much higher that the value already present. Then following the trend if there is a certain hike in the amount of CO in the air within a period 5 years than the hike in the year 2017 is justifiable.

The above graph has been predicted for the amount of CO2 gas emission over the years for a period of 3 years. The blue line on the graph shows the plotting of the point of the data set. The corresponding points have then been used for the predictive analysis of the amount of CO2 gas emission. The prediction of the data has been plotted on the graph using the pink line. The starting point on the prediction graph matches the end point of the original graph. This is due to the fact that the values of the graph does not fluctuate much over the course of 5 years. As there is a gradual increase in the amount of CO2 in the air the prediction shows an increase in the rise of the levels. However there is steep drop in the value of the plotted points being predicted in the year 2017 shows that there might be a slight better quality air in the atmosphere.

The above chart shows the prediction of the NOX gas emission of the countries chosen for a period of 3 years more than the data collected and used for the analysis procedure. The green line on the graphs is the points plotted against the values of the data set. The prediction which has been done on the data set. The corresponding orange line on the graph has been plotted for the predictive plots. The plots marked on the graph show a gradual drop in the amount of NOX in the atmosphere. The high drop in the values of the data set made the prediction of the year 2014 to be higher than the value which was there before hand. The predicted data has a high drop in the values in the form of a linear line. However in the year 2017 the predication shows a high rise in the amount of NOX in the atmosphere. There has been an approximate 7000 unit rise in the prediction would have a high effect in the future of the world.

The above diagram shows the data prediction for three years in the future for the SOX gas emission around the world. The blue line has been plotted for the plots of the data set. On the plotted data the predictive analysis had been done for amount of SOX in the air. The predictions has been plotted on the graph and the yellow line shows the predicted line on the graph. The data set values has a 4000 unit drop which has made the prediction to deviate away from the basics. The first predicted point is 3000 unit more than the actual value. Though the prediction shows a steep fall in the amount of more than 10000 units. However at the end of the years 2016 it can be seen that the prediction provides a high rise in the amount of the SOX gasses in the atmosphere. The prediction shows the rise of a point of 10000 points again. This rise and fall if followed will be harmful for the atmosphere.

The above chart shows the production of the VOC gasses in the air for a period of 3 years into the future. The line with a darker hue of the color red provides the plots of the data set values. The subsequent plots of the lighter hue of the color red shows the data predicted the values of the future. Though there had been a small fluctuation in the values of the data set, the value predicted shows a rise in a 3000 units. The subsequent values drops down slowly and then gets a rise in the year of 2017. The values fluctuate slowly at a constant pace rather than a steep drop like the previous charts.

The dashboard has been chosen to provide a detailed view of the use of the data set. From the analysis of the data on the application it has been found that the summation of the properties of the air pollution factor is found to be the highest in the CO factor and the lowest summation has been found in the factor named SOX. The dashboard shows the geographic Choropleth chart, the comparison of all the factors of the data set which has been plotted against the years and the predictive analysis which has been done on the data set to find the data for three years into the future. This would be helpful for the organization to understand the outcomes of the future and plan themselves accordingly.

For the CEO of the organization I would recommend to setup a set of rules of the organization to follow during the implementation of any kind of decision making procedure with respect to the environmental factors which has been discussed in the data analysis procedure. For the analysis of the report specific qualities were kept in mind for the completion. For the organization the CEO should start investing in case studies based on the different factors of the environmental factors as well as the factors which would be essential for the organization to survive in the market. Finding the return of investment of the procedure which the organization follows for the profit investment should be calculated which would help them to again produce better objectives for the organization. Understanding the sale pattern of the products of the organization would help in assessing the similar environmental factors which would get related to the factors of the sale procedures of the products.

Conclusion

From the above report, it can be concluded that the term business intelligence has many underlying concepts, which is often used by managers to produce decision-making statements for the organization they are working for. For the completion of this report, a software named SAP Lumira had been used. This has been used due to the benefit of the alteration of the data structure and the correlations with respect to the data that is being analyzed. The report has provided the user with a detailed description of the different analysis and the data set that has been used for the completion of the report. The procedure has the use of both structured and unstructured data. However the availability of the unstructured data helps in the process of analysis and decision making procedures. For the organization, the recommendation provided in the report can be followed to improve the sales and the operations of the organization.  

References

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Basole, R. C. (2014). Visual business ecosystem intelligence: Lessons from the field. IEEE computer graphics and applications, 34(5), 26-34.

Forsgren, N., & Sabherwal, R. (2015). Business Intelligence System Use as Levers of Control and Organizational Capabilities: Effects on Internal and Competitive Benefits.

Isik, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.

Kulkarni, U. R., Robles-Flores, J. A., & Popovi?, A. (2017). Business Intelligence Capability: The Effect of Top Management and the Mediating Roles of User Participation and Analytical Decision Making Orientation. Journal of the Association for Information Systems, 18(7).

Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

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