Data tells us a story no author could ever compose. It shows us never before observed patterns that may slip through the crack. The power of data analytics is more important than ever in the rapid-paced market, where the slightest difference is enough to save or cost a company millions of dollars in revenue. This high level of data analytics was often hidden behind layers of complex programming languages and frameworks. Still, since Tableau released its product in 2003, it has helped thousands of companies visualize billions of rows worth of data.

Tableau is a powerful data visualization and business intelligence tool that allows users to analyze and present data visually, engaging, and interactively. With a user-friendly interface, Tableau enables individuals and organizations to easily connect to various data sources, whether spreadsheets, databases, or cloud services. It offers a wide range of visualization options, including charts, graphs, maps, and dashboards, which enables users to explore data from different angles and gain valuable insights. Tableau and its lesser-known associate, Tableau Prep, provide a low-code application to import, clean, and optimize your data sources from a central data lake before using them for visualizations. In this article, we will discuss an example dataset I cleaned using Tableau Prep and then visualized using various KPIs and graphs on Tableau Desktop.

Getting to know the data

This specific dataset is a collection of datasets I found on data.gov.in. It contains data about the percent distribution and absolute number of foreign individuals that entered the country in various years (2001 - 2020), the amount of money (USD and INR) spent by foreign visitors, and area-specific domestic and foreign foot traffic. Samples of the data have been provided below, but you can download the data from these sources [1][2][3].

Year	FTAs	% distribution by Age- Group (in years) - 0-14	% distribution by Age- Group (in years) - 15-24	% distribution by Age- Group (in years) - 25-34	% distribution by Age- Group (in years) - 35-44	% distribution by Age- Group (in years) - 45-54	% distribution by Age- Group (in years) - 55-64	% distribution by Age- Group (in years) - 65 & above	% distribution by Age- Group (in years) - Not Reported
2001	2537282	7	10.8	20.1	21.1	19.4	11.9	6.7	3
2002	2384364	9.2	10	19.4	21.6	19.4	11.5	7.7	1.2
2003	2726214	7.2	10	19.5	21.6	19.4	11.5	7.7	3.1
2004	3457477	8.5	9.8	18.8	21.3	19.4	12.8	8.2	0.2
2005	3918610	8.6	9.6	18.8	21.3	19.5	13	8.7	0.5

Circle	Name of the Monument	Domestic-2019-20	Foreign-2019-20	Domestic-2020-21	Foreign-2020-21	% Growth 2021-21/2019-20-Domestic	% Growth 2021-21/2019-20-Foreign
Agra	Taj Mahal	4429710	645415	1259892	9034	-71.56	-98.6
Agra	Agra Fort	1627154	386522	371242	2810	-77.18	-99.27
Agra	Fatehpur Sikri	454376	184751	107835	574	-76.27	-99.69
Agra	Akbar Tomb Sikandra	229270	19625	99509	321	-56.6	-98.36
Agra	Mariam tomb Sikandra	22517	414	9765	31	-56.63	-92.51

Year	FEE in terms -Crore	FEE in ` terms - % Change over previous year	FEE in US$ terms - US $ Million	FEE in US$ terms - % Change over previous year
1991	4318	NA	1861	NA
2001	15083	-3.5	3198	-7.6
2002	15064	-0.1	3103	-3
2003	20729	37.6	4463	43.8
2004	27944	34.8	6170	38.2

Data Cleaning 🧹

After loading the data, the first step of any Data Visualisation Project is to clean it so that your visualizations can be neat and convey all the relevant information you extract. Of course, this is possible using Python and accessory modules like Pandas and Numpy, but Tableau Prep provides a low/no-code experience. The most you'll ever code is when writing basic SQL queries. Our complete "Data Cleaning Pipeline" is strictly no-code and, in its entirety, can be seen below.

Here, I've labeled each step to understand better what it does. Still, the basic gist includes renaming columns to more accurately portray their meaning, Altering and regrouping these columns to negate outliers in data better, and then joining the two data sources (via Inner Join) to get our final output. Below we can see one of the two final data sources.

Year	FTAs	Age % 0-14	Age % 15-24	Age % 25-34	Age % 35-44	Age % 45-54	Age % 55-64	Age % 65+	Age % Not Reported	FEE in INR Crore	FEE in % Change over previous year (INR)	FEE in US $ Million	FEE in % Change over previous year (US$)
1/1/2001	2537282	0.07	0.108	0.201	0.211	0.194	0.119	0.067	0.03	15083	-0.035	3198	-0.076
1/1/2002	2384364	0.092	0.1	0.194	0.216	0.194	0.115	0.077	0.012	15064	-0.001	3103	-0.03
1/1/2003	2726214	0.072	0.1	0.195	0.216	0.194	0.115	0.077	0.031	20729	0.376	4463	0.438
1/1/2004	3457477	0.085	0.098	0.188	0.213	0.194	0.128	0.082	0.002	27944	0.348	6170	0.382
1/1/2005	3918610	0.086	0.096	0.188	0.213	0.195	0.13	0.087	0.005	33123	0.185	7493	0.214

Time to Visualize, Visualize, Visualize 📊

Quoting Daniel Bourke, a personal hero, let's begin visualizing the data we just created. Luckily, Tableau Prep extracts can be opened directly into Tableau Desktop as a .hyper, .csv or a .xlsx file. Here we will also use our second data source, available as download file 2. Getting straight to the point, we see all our data sources and relevant column names on the left-hand pane after we import our data sources.

The names in blue are known as discrete values, while the ones in green are known as continuous values. More information can be found in this article by Tableau, but to explain with a table:

On the right of the pane, we see our workspace, where we can drag and drop our columns to create KPIs, graphs, and dashboards. I won't be going through how to make every KPI or visualization on Tableau, but we'll construct basic graphs based on the available measures and dimensions. Below are some of the more interesting plots.

Something interesting I found was the year-on-year growth for 2001-2019, but because of the COVID-19 Pandemic, we can see the money spent in 2020 was equivalent to 2008, a 12-year deficit.

Even though Agra is 4th in terms of the number of monuments, it is the city where the most amount of foreign income is generated (because of the Taj Mehal and surrounding Monuments)

It's shocking that even though Mumbai has the highest number of monuments, its gross income from foreign and domestic tourists places it close to the middle of the total rankings.

It isn't surprising to see how strong a hold the Taj Mahal has compared to other monuments in terms of International and Domestic earnings. It is about 20% of the international income from tourism.

Final Thoughts

Tableau is a fine piece of software that makes data visualizations easy to make and, with its interactive menus, ensures that little to no code is required to complete the toughest visualizations. From simple bar graphs to parsing GeoData via coordinates or location names, Tableau can speed up the data analyzing task. It even provides ways of importing your data from Google BigQuery or Amazon Redshift. But it does lack the satisfaction of coding, which I severely missed while working on this project. The complete data visualization can be found here on Tableau Public.