Tableau Limitations and What We Can Do About It?

Harshit Jain • Last Modified: December 28th, 2022

tableau limitations

For decades, businesses have been investing in Business Intelligence tools such as Tableau, Looker, and Power BI hoping to streamline analytics internally and gain business insights. If you are traveling down the same road, one of the first questions you need to ask yourself is this:

“Is a Business Intelligence Software alone enough to solve all the analytics challenges faced by my organization? Is this tool enough to streamline analytics and unleash the power of my data?”

The answer in most scenarios is no. Let’s deep dive into Tableau Limitations.

Table of Contents

The Problem of Data Silos

Often, businesses have their data stored in different systems. For example, transactional data is stored in databases like MySQL, PostgreSQL, MongoDB, marketing data is stored in Google Analytics, Adwords, behavioral data is in Mixpanel, and sales data is in Salesforce. Similarly, each department from technology to customer service would have similar applications on which they operate and store critical data.

In order to get a holistic picture of your business and answer questions like “What is the retention rate of users acquired by Facebook?” or “What locations do we get the maximum sale from?”, you will have to combine information resting on multiple systems and build reports.

Sadly, the data stored in these siloed systems is often not suitable for analytics. Integrating data from different sources is not a straightforward process. There are various challenges.

  1. Transactional systems store data in a normalized format. But, the data you need to get meaningful insights will require you to perform complex data joins involving multiple tables in the database.
  2. The data you need does not exist in the transactional systems. For example, you would store the IPs of all visitors in your database. You will have to transform this into geographical locations to make the data meaningful.
  3. The database might contain old or invalid data that may not be meaningful in the current scheme of things. You will want to remove this piece before making reports on this data.
  4. You would want to merge data not only from a single source but from multiple disparate sources to get the bigger picture. For example, you will want to merge the data from Facebook ads with your transactional database to retrieve ROI from Facebook ad campaigns.

To sum it up, there is a huge chunk of data preparation to be done before you send the data to a reporting tool like Tableau.

At this stage, if you have already explored Tableau Limitations or even grazed through the website, you might say that it is already connected to a variety of data sources in addition to building reports. Doesn’t it?

I would say, not quite.

What BI tools like Tableau and Power BI do allow is to point to individual sources, connect them separately and build reports on top of it. Although Tableau Limitations to support data preparation and mash-up of data from different sources, it is not very effective and does not serve the complete purpose.

Due to these Tableau Limitations, you anyway might be required to manually prepare and mash-up data from multiple systems on simpler tools like Excel. However, when you have huge volumes of data consumed by different departments, Excel does not scale.

Tableau Limitations

Here are some of the main limitations of Tableau listed below:

  • Tableau mainly focuses on the visualization part. One of the Tableau Limitations is that it cannot work with uncleaned data and requires additional work to properly clean the dataset first.
  • Another Tableau Limitations is that it lacks the Data Modeling and Data Dictionary capabilities for Data Analysts. You have separately defined metrics elsewhere.
  • Many people say that Tableau Limitations also include its poor support. Many times people have to solve their issues by themselves.

The Solution: Data Integration from Multiple Sources

There are many approaches to solve this problem. Let us take some inspiration from modern businesses like and Meesho (Here is the link to and Meesho’s complete story). This is what their complete Analytics stack/infrastructure looks like:

  1. Data integration and preparation system that can transform data from source systems into an analytics-ready state.
  2. A central data warehouse that stores the transformed data.
  3. Reporting tool (Business Intelligence tool) that helps visualize this data.
Tableau Limitations : data analytics stack

The businesses who have seen success with analytics set-up have followed a 3 component structure. They run reporting tools like Tableau, Power BI, or Metabase on top of the warehouse.

A system like this has multiple advantages. For one, you will be able to build a single source of truth for the entire organization. By using a tool for data integration you will be eliminating all the ETL and data preparation hassles. Individual departments can then point the prepared data to any reporting tool of their choice to build beautiful reports that aid decision-making.


On a closing note, before investing in a single reporting software like Tableau, make sure you evaluate your overall approach towards Analytics after reading about Tableau Limitations. If you are strongly considering leveraging analytics to build a better business, it would be best to get your analytics infrastructure sorted.

Investing in the right resources for analytics can not only help you be a data-driven organization but also beat the cut-throat competition in the market.

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