The final day of dashboard week! Unlike previous days where we presented the next morning, today we know about the topic of the challenge at 9 am and need to complete it by 3 pm to be presented. In addition to that, we also need to present the dashboard we created the previous day.

We get the datasets on ‘County Business Patterns’ between the years 2017-2020 from the US Census Bureau website, feel free to check them out here.

I will walk through the visualisation I created along with the screenshots.

Figure 1: Page 1 shows the title ‘Which Industry Should We Focus in The US?’ 

I didn’t have enough time to write the problem statement to start the visualisation. So instead I went with a question as a title and narrated the reason behind the question in the first place when presenting. This time I tried to attempt to answer which industry should be focused on to alleviate unemployment in the US.

Figure 2: Small multiples of scatter plots showing the relationship between the number of establishments and employment level per state and industry

Probably industries that are labour intensive can be looked into as a great avenue to improve employment levels. The steeper the trend line may possibly suggest that for every unit increase in the number of establishments, there is an even higher increase in employment. Interestingly, it’s not the manufacturing industry that has the steepest relationship, it was the management of companies and enterprises industry.

Figure 3: Pop-up bar charts illustrating the employment per establishment for the top n industries in the US

Please ignore my typo when typing employment in the viz. I added an additional bar chart that shows up when clicked. But in the end, it’s still difficult to compare with scatter plots. True enough the bar chart also shows that the industries in the top 5 are those with the steep trendlines, with the management of companies and enterprises as the number one as previously seen in the small multiples.


Figure 4: Payroll per employment for the industries, highlighted in red is the health care and social assistance industry

Knowing that these industries employ a large number of people is not enough. We may also want to know whether they are earning well. We can see that the health care and social assistance industry lies in the middle for the payroll per employment. On the other hand, the management of companies and enterprises industry is at the top or within the top 2 between these years, possibly hinting how ‘business people’ tend to be overpaid.

Figure 5: Bar charts showing the comparison between 2 states

Lastly, I wanted to do a comparison between 2 states. I selected New Mexico which has the highest unemployment level and Nebraska, with the lowest unemployment level. Although the utility industry seems to be providing the highest earning per person employed for both states, there is a difference in the following positions.


Key Takeaway

I intended the visualisation to be more like a presentation. So it doesn’t really have any text explanation. Chart choices are simple – just bar charts or scatter plots, but interactivity can make them more interesting. And especially if it complements your presentation. It’s not heavily designed because of the time constraint. But a simple design can still look good when it’s clean (use ample of white spaces).


Feel free to reach out to me if you have any questions on Twitter or LinkedIn.



Johanna Josodipuro
Author: Johanna Josodipuro

Johanna completed her Master of Commerce degree in Business Analytics and Marketing from the University of Sydney. She was introduced to Tableau during her studies and it wasn't long before she used it to participate in Tableau community initiatives, such as Viz For Social Good. She loves how it enables non-technical audiences to make sense of their data and help guide their decisions. At the same time, it also provides her with an avenue to tap into her analytical and creative side. In her free time, you can find her scrolling through Tableau Public while listening to music and sipping hot tea.