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Data Visualization Using SAS Viya FOR SALE!!

Data Visualization Using SAS Viya

 

 

 

 

 

 

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Introduction

Toy company “INSIGHT TOY Company 2017” visual analysis is the focus of this SAS Visual analytics project. As a high-performance, on-memory solution, SAS Visual Analytics is ideal for swiftly and easily analyzing large volumes of data. Patterns may be seen and chances for additional investigation can be discovered thanks to this tool. This presentation provides a high-level overview of SAS Visual Analytics’ extensive set of features and functionalities. A little amount of data has been supplied to us in order to get us up and running as fast as possible (only 3.5 million rows). We used the “INSIGHT TOY COMPANY 2017” dataset to test out SAS visual analytics capabilities. To learn more about Geo-Hierarchy, Gross Margin, and Sales across several continents, we’ve created visualizations.

Overview of Data

A made-up firm called Insight Toy Company markets itself as a retailer of cutting-edge playthings. The gathering of data pertaining to finances, production, and sales and marketing takes place over a period of 14 years at 127 different sites spread around the world such as; Financial product: Gross margin, product sales and cost of sales; Manufacturing: unit capacity, lifespan, reliability; sales and marketing: sales representative, customer satisfaction, customer distance and, sales rep rating. There are 3.5 million rows and 60 columns in this database.

Task 1

In this task, the data items are created effectively.

Task 1.1

From this activity in the task, SAS creates a “Gross Margin” factor and then uses the formula: add new data item in data column to compute gross margin.

Whereby; Gross margin = product sales – product cost of sales

A new data item is then generated and titled “Average Gross Margin” by taking the mean of the above computed Gross Margin and renaming it.

Task 1.2

From this task, we pick the Hierarchy option from the drop-down menu when you are creating a new data item, and then choose Brand, Line, Make, and Style from the drop-down menu. The following is an example that results from applying hierarchy to the element Gross Margin:

 

Product brand that is in the hierarchy on selecting ‘’Toy’’ further outputs as shown below;

Task 1.3

To this hierarchy of continents, nations, regions, and cities has been added a series of ‘Facility Geo-Hierarchy’ as well as a bar chart of facility Geo-Hierarchy spanning the Gross Margin component. The “Facility Geo-Hierarchy” factor is a new one that was generated by the same approach as the 1.2 factor.

Task 2.0

This task involves Summarizing the Gross Margin in 2017.

Task 2.1

For the purpose of displaying the information for Product Sales and Gross margin factors across continents and countries, a cross tab is generated. Therefore, only a portion of the table is shown here.

Task 2.2

A bar chart is generated to display which countries have the highest and lowest Gross Margin across the globe.

According to the bar chart that was just shown, the nation that has the greatest gross margin is the United States, which has a value of 533066.85, while the country that has the lowest gross margin is India, which has a value of 2086.85.

Task 2.3

Products sold in 2017 are compared across geographies and historical periods in this job. A Bar chart is used as a starting point.

In 2017, the most products were sold in North America, as seen in the above bar graph.

The table above shows product sales across continents during the year 2017. There’s a problem with the table’s presentation here.

Task 3.0

A drillable Geo-map is utilized to make an intelligent visual showing how item deals and shopper happiness shifts all through mainlands, nations, districts, and urban communities. A drillable geo map is created with the Facility Geo-Hierarchy component and item deals then, at that point, added on Average consumer loyalty (another information thing made involving determined thing in information section with consumer loyalty element), and afterward a drillable channel of exchange dates in 2017 for item deals and consumer loyalty. It is feasible to dive into landmasses, showing their item deals and normal consumer loyalty level, as well as the time span, then for the chose country, and in conclusion into a particular locale and its urban communities. The output sample for a specific continent, country region and city is as shown below:

It is a color-coded map that depicts the various regions of a given country, continent, or world. The values are shown as a color scale, and we can add optional hover-text to regions by clicking on them. We were able to show their product sales and their average customer satisfaction level for different continents by using this visualization. After that, we’ve chosen a country, a region, and then individual cities within that region to display product sales and customer satisfaction levels on a regional basis.

Task 4

In this task, we are determining the correlations between the facility attributes and customer satisfactions or distance:

Task 4.1

Continents and facilities are both filtered in this job, such that when a continent is selected, it displays the facilities in that continent, as demonstrated in this figure;

The African continent’s facilities are shown in the bar above. There is a bar chart below that shows the average customer satisfaction factor across nations, as well as a list of table objects for each country’s unique items.

Task 4.2

There are three plots in this page: a correlation matrix, a list, and a time series. The correlation matrix is used to show the connection between customer satisfaction, distance, and other dataset variables; the list bar shows the average distance between customers in different countries; and the time series plot shows how customer distance changed over time in 2017.

Task 4.3

Tasks 4.1 and 4.2 are linked to each other through a page link in this example. When a country’s bar is selected in the bar graph, a list of its products and the correlation between the Customer Satisfaction Index and the Customer Distance Index for that country’s bar are shown. This page also shows the average customer distance for that country and the distribution of Customer Satisfaction in that country.

 

Task 5.0

References

Abdelhafez, H. A., & Amer, A. A. (2019). The Challenges of Big Data Visual Analytics and Recent Platforms. World of Computer Science & Information Technology Journal9(6).

Uhl, A., & Gollenia, M. L. A. (Eds.). (2014). Digital enterprise transformation: A business-driven approach to leveraging innovative IT. Ashgate Publishing, Ltd..

Postolov, K., Josimovski, S., Pulevska-Ivanovska, L., Magdinceva Sopova, M., & Janeska-Ilieva, A. (2014). Managing with organizational environments-strategy of e-business. Economic Development16(3), 103-122.

Abousalh-Neto, N. A., & Kazgan, S. (2012, October). Big data exploration through visual analytics. In 2012 IEEE Conference on visual Analytics Science and Technology (VAST) (pp. 285-286). IEEE.

Styll, R. (2013, May). Fast Dashboards Anywhere with SAS® Visual Analytics. In Proceedings of SAS Global Forum.

 

 

 

 

 

 

 

 

 

 

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