This week we participated in a Tableau viz competition held by Salesforce called Viz Master in a group of two. The dataset was released on Monday 10am and we were expected to submit our dashboard on Thursday 2pm. Though our team didn’t manage to get into the final round, I’ve definitely learned a lot throughout this experience.


Data Cleaning and Investigation

When given a dataset with a topic I’m not familiar with, it always takes me some time to understand what the data is about. And this is the case in this experience. It took me a while to figure out the relationship between each data source, and come out the idea about how to analyse and what story to tell. And with hundreds of field in the data, we’ve also tried to filter those needed only, so it won’t be an endless scroll bar when we input data to Tableau.


Time Management

For me this is the most critical and challenging thing. Often we find ourselves saying “if I had more time I would’ve done better”, or “I didn’t have enough time to…”. But the reality is we are usually on a tight timeline. So it is important to allocate time wisely and prioritise the tasks. This time I feel we should’ve put more time in creating viz and building dashboard. I’ve heard of the upcoming dashboard week and client project weeks, in which we’ll be facing tighter timeline. Hope I’ll be better by then!



When working in a team, sometimes we might have disagreement between each other. In this case, it could be the analysis points, the selection of charts, or the style of dashboard. We have to try to communicate with each other, and reach an agreement that we are both happy with. This process is actually idea inspiring. Another important thing is tasks allocation. People are good in different fields, so great teamwork can bring better result than working individually.


Feedback on Friday Presentation

We’ve got valuable feedback from our coaches after the presentation on Friday, including what we’ve done well, and what we can improve. In our case, we were suggested to build more interaction by using parameters, add more analysis points, and have additional charts to interact with the map to show the comparison clearer. This is one of the great things about The Data School. The coaches are supportive, and we can always expect constructive comments from them.


Actually two groups in our cohort made it to the final run, and one of them received a podium position! (Check their great works here and here.) So proud of them! And this is another great thing about the Data school – you get the chance to learn and work with smart and talented people!


Ming-Hsuan Lee
Author: Ming-Hsuan Lee