IS428 AY2019-20T2 Assign ANG WEI XUAN DION

Overview

Every two years, SMU libaries conduct a comprehensive survey in which faculty, students and staff have the opportunity to rate various aspects of SMU library's services. The survey provides SMU libraries with input to help enhance existing services and to anticipate emerging needs of SMU faculty, students and staff.

The 2018 Survey Results can be found here and the full report is available by following this link. However, the past reports are mainly made-up of pages of tables, which are very difficult to comprehend. Therefore, the objective of this project is to apply appropriate data visualization and create an interactive dashboard that allows SMU libraries to easily view the results of the survey and gain useful insights.

Objectives

The interactive dashboard aims to achieve the following:

1. Allow users to get a quick overview about the survey results (who were the people taking the survey, what was the overall satisfaction, and how likely they will recommend the Library to others
2. Allow users to see the distribution of scores for each survey question, and allow for gap analysis by comparing performance and importance
3. Allow users to see what are some comments made by visitors from each of the different groups (undergraduates, postgraduates, faculty, and staff), and by the Net Promoter Score groups (detractors, neutrals, promoters)

With this dashboard, SMU library will be able to gain a better understanding of their visitors and find out which are the areas where they need to improve on.

Data Preparation

The survey results were stored in a excel sheet in transactional form, where each row represented one respondent in the survey. For each respondent, the following information was captured

• Which library they used more (Li Ka Shing or Kwa Geok Choo Law Library)
• Whether they were undergraduate, postgraduate, faculty or staff
• Their area of study (business, economics, etc.)
• How often they came to campus, used the library, and accessed library resources
• Their responses for each of the survey questions, represented by one column each in the dataset
• Comments about the library

The format of the data makes it difficult for analysis, such as comparing scores between survey questions, or to perform gap analysis between importance and performance. In addition, many of the categorical variables were number coded into a legend, so they have to be translated.

Pivoting the Data

Pivoting the data transforms the data by placing questions under a single column, so we can compare between different questions or filter them out by question categories. However, we also want to allow for comparison between the importance and performance of a particular question, instead of treating them as different questions. Therefore, Tableau Prep was used to achieve this.

The relevant survey questions for Importance and Performance were pivoted separately. Afterwards, the letters from the question codes were stripped so that I01 (importance score for question 1), and P01 (performance) was treated as the same question. Afterwards, joins were used to combine the different tables so that each question has an importance and performance score associated with it.

Lastly, before loading the data into Tableau, Excel lookup functions were used to replace the number coded values in the data to their actual values. This reduces the amount of renaming work that needs to be done in Tableau. After that step, the final form of the data looks like this:

Screenshot	Description
	The data comes in 2 sheets: SMU and Legend. SMU contains all the actual responses, while Legend contains all the codes for the questions and responses.
	Upon importing the SMU sheet, we can see that while the legend and ratings are available, the actual questions are not. We must proceed to import the Legends sheet as well, but first, we must treat this data. Tableau does not like this format where every question has its own column, therefore we need to pivot it.
	We select all the columns from I01 to NPS1 and pivoted them into 2 fields as shown in the second image. This will allow tableau to manipulate the data easier when we build our likert scale and various other graphs. We shall rename 'Pivot Field Names' to 'Code' and 'Pivot Field Values' to 'Rating'.
	We can now drag in the 'Legend' sheet and inner join them by 'Code'. This will allow us to have the 2 columns, Item and Rating, that will enable us to plot our visualisations.
	In addition, to better evaluate the services the library provides, each question has been grouped into respective categories that evaluate the computer facilities, overall satisfaction, resource access, service and the space of the library. The 5 groups are Computer Facilities, Resource Access, Service, Space and Overall. These categories will help us to evaluate different aspects of the libraries' service.

Interactive visualization

The interactive visualization can be accessed here: https://public.tableau.com/profile/dion.ang#!/vizhome/Assignment1_15840879937550/Home