Talk:Lesson01

Visual Analysis for Everyone

The whitepaper, published by Tableau, provided me with a great insight on the field of visual analytics. The examples illustrated in the paper strongly demonstrated the need for a data analyst to have an interactive visual interface in order to perform their job well. Prior to the course, I have been using Microsoft Excel as a main tool for data presentation and analysis. I felt that it was one of a few de facto software for visualizing data quickly and easily. However, after reading this white paper, I have a clearer understanding of what visual analytics actually entails and having the flexibility for users to express their thoughts freely on a user interface is indeed a powerful tool in the field of visual analytics. In this case, Microsoft Excel does seem constrained and restrictive for the analyst to perform analysis on the tool itself. Of course, if an analyst knows the end result of his/her analysis, Microsoft Excel can be a useful tool to generate those charts for data presentation. However, during the process of analysis where one has to frequently change data and views based on different context, it does not seem to be as useful as other visual analytics tools out there (e.g. Tableau, Qlik Sense etc.) where users can interact with the data/views directly.

To resolve existing data visualisation and analysis problems, Tableau implemented 5 principles into their software. Together with VizQL, it allows flexibility for users to express and change visualizations to answer questions as they analyse on the fly. The use of visualization best practices further allows an analyst to display their data effectively. In my opinion, one limitation of having best practices and standard charts/graphs may constrain user’s creativity to think of newer and better ideas to illustrate their analysis results. Although best practices are followed by many and it is understandable by everyone, there could be better ways to represent data better. This could not be easily achieved by the tool itself and requires time and effort on the analyst to decipher ways to do so. Despite so, Tableau is undeniably a good tool for all visual analysts to begin with analysing and exploring data to discover new insights and create additional business value for their organizations.
--Gwendoline Tan Wan Xin

Demystifying Visual Analytics

In this paper “Demystifying Visual Analytics” by Christian Chabot from Tableau Software. The paper focused its arguments to debunk the common “stereotypes”/misconceptions on visual analytics. The paper also brings about the point on which visual analytics reduces the cognitive load required to for analysis. Visual analytics is widely believed to be used on massive data volumes. In response to this misconception, the author rebuked that the use of visual analytics on small data set is just as effective as that of enormous data volumes. The key argument was that the complexity of answering questions often rises faster with the dimensionality of data rather than the number of observation. Visual analytics is exceptional in handling multidimensional data, regardless of its size. Therefore, even with small data set, the use of visual analytics is just as effective. The author also mentioned that visual analytics tools typically contains new visual paradigms, which can be a major put-off for new users whom have to spend time to learn a new analysis tool, is held untrue. Rather, new visual analytics software does not necessarily mean new paradigms. In the case of Tableau, it focuses on proven visual paradigms. The notion of using visual analytics to uncover hidden insight is contested by the author as well. Visual analytics is concerned with improving the process of analytical reasoning. Thus, uncovering of insight is merely part of the journey, but not the destination itself.

Use of visual analytics is not restricted to just complex problems, but to simple questions as well. It is also claimed that the use of visual analytics is designed to answer questions, be it complex or simple. It was also put across that simple questions are answered much more quickly using visual analytics. However, in relation to this argument, it can also be argued that simple problem can be solved without the use of visual analytics technology. Simple spreadsheet application would suffice to solve everyday problems. As much as we would want to use visual analytics to solve everyday problem, I believe that there are 2 main factors which hinders the use of visual analytics to solve “simple” problems. Firstly, it’s the cost of such visual analytics software. Would the cost of using such software, justify in relation to solving a simple problem, which can be replaced by other software that does more “inferior” job in data visualisation, but still yielding the same result. Secondly, for questions be it simple or complex, to be answered more quickly depends on the analyst’s ability to interact with software itself. A skilled analyst trained on the platform would undoubtedly yield better result compared to an amateur one, regardless of platform. In my opinion, rather than focusing on the notion that visual analytics tackles simple or complex problem, a better argument is that how visual interaction with data compliments humans’ biased visual ability (we understand information better through graphic presentations). It gives the user an unprecedented ability to break down the complex problem into simpler, solvable parts. Simpler problems would be made simpler with these visualisations. With much argument put forth in the use of visual analytics, it is indisputable that visual analytics allows the analyst to uncover and understand hidden insights. However, the most critical aspect in the process of analytics is the skill of the analyst himself/herself. Only the analyst can bring out the full value of the visual analytics.
--Tan Kee Hock