When I first heard about Tableau, I was excited to try it. Tableau is a data visualization tool that helps people visually analyze and understand their data. It allows users to connect to different data sources—such as spreadsheets, databases, and cloud services. I knew it was a powerful tool that could build beautiful and interactive dashboards, graphs, and charts and I was eager to explore its capabilities. I’ve learned to use a few complicated software programs, so I figured Tableau would be another tool I could quickly master and add to my skill set. However, it turned out to be more challenging than I expected.
Below are four reasons I ultimately decided that Tableau wasn’t a good fit:
1. Learning Tableau requires complicated statistical education
Tableau offers many free tutorials and articles that give users resources to help them learn the program. However, the learning curve for Tableau is steep! I spent hours watching and reading Tableau’s free resources and I still found myself getting lost in the jargon. Most training videos used acronyms and phrases like “BI” or “Dynamic SQL,” making it challenging for me to understand how to perform basic steps like importing data into the program.
I also found it difficult to make headway in my understanding of the software. Many tutorials claim that a user can gain mastery of Tableau in as little as 8 weeks, but this assumes a fundamental understanding of statistical concepts and data representation. For each simple task, there were increasingly smaller steps I needed to understand before moving forward. Even after learning the basics, I found it challenging to customize basic graphs and charts, making it hard to create the specific visuals I needed for my projects.
2. Tableau takes a lot of time
Tableau works best with clean and organized datasets. Taking the time upfront to organize and label your data helps Tableau during the import process. As a new Tableau user, I found this process a universal tool that serves various very time-consuming. Very often, it took me more time to clean my data sources than it would have taken me to create data visuals in the first place—this was especially true for large datasets.
3. Features are more robust than needed
Tableau is touted as containing functions. For example, one of the main features of Tableau is that it allows you to create interactive dashboards. Users can manipulate this contained data in such a way that they can layer it over other datasets. An example of this might be if someone created a contain all the playgrounds within a community that contain a data map displaying slides and monkey bars. But, while learning to use Tableau, I realized that only some data visualizations require this level of sophisticated analysis. Most often, I only need to create a simplified version of a graph and I found that the time it took to create these visuals in Tableau outweighed the value of the result.
4. Tableau is costly
Tableau offers a free one-year license to teachers and students. For others, a Tableau Explorer plan costs $42 monthly per user at their most basic license level. This can be expensive for many organizations, particularly for smaller businesses or those on a tighter budget; and this price point likely doesn’t make sense if your organization doesn’t have a dedicated data analysis team.
What to Use Instead
If you can’t justify the cost of Tableau, or if you don't have the resources to maintain the software and use it to its full potential, it likely won’t be a good fit for you. If that’s the case, alternative tools or approaches may be more appropriate and cost-effective--such as:
Microsoft Excel: Spreadsheet software that can organize, manipulate, and visualize data
Google Data Studio: A free data visualization tool that allows you to create interactive reports and dashboards. I haven’t tried Google Data Studio, but I have heard great reviews.
Canva: A free graphic design software that allows you to create charts, reports, and infographics
It All Comes Down to Need and Expertise
While Tableau is a powerful data visualization tool, it wasn't the best fit for me. It was hard to learn, I found the customization options limiting, and it wasn't cost-effective for what I was trying to achieve.
Ultimately, the decision to use Tableau or any other data visualization tool depends on the needs and expertise of the user. If you’re comfortable working with structured datasets, have a quantitative background, or are familiar with business intelligence tools, Tableau could have more be a great fit for you. But if you’re like me, unfamiliar with business metrics, and have more strengths in qualitative analysis, Tableau’s learning curve was more trouble than it was worth.