How to Use Accelerator Data Mapping in Tableau

Tableau Accelerators are pre-built dashboards that answer common business questions with relatively few data columns (usually less than 15). They are designed so that any Tableau user can download a workbook and substitute their data, making the speed to insight as fast as possible. 

With Tableau’s latest release, this process became much easier with their new Accelerator Data Mapping feature. 

In this blog, we will guide you through everything you need to know to take full advantage of this new feature and cover a few common test examples you may encounter when mapping data in Tableau.

Step 1: Requirements

As with every latest and greatest Tableau release, you must install this latest version (2023.1) on your machine to use the Accelerator Data Mapper. If your company has Tableau Cloud or Tableau Server, it is generally recommended that you stay on the same version as the Cloud or Server. 

If your company is behind in version updates, communicating the benefits of using accelerators may convince your leaders to get up to date on the latest version sooner rather than later.

The only other requirement is that you have access to data similar to the accelerator you want to use. More on data preparation later.

Step 2: Downloading an Accelerator

Tableau has excellent documentation on where to find accelerators and how to use them, but I’ll summarize the process here.

Where to Find Accelerators

On the Tableau Exchange

Note that if you want to use the Data Mapping feature, you can filter the accelerators shown by clicking the ‘Data Mapping enabled’ box highlighted below:

In Tableau Desktop

Click on the More Accelerators button on the homepage after opening the Tableau Desktop. This will pop up the same list of accelerators in the Tableau Exchange and allow you to filter just to those where Data Mapping is enabled.

In Tableau Cloud

When opening a new workbook from Tableau Cloud, select Accelerator in the Connect to Data window, and you will be shown a limited set of the accelerators on Tableau Exchange.

Once you have found your chosen accelerator, download the workbook by clicking the blue download button at the top of the page. Make sure the feature section says “supports data mapping.”

Step 3: Prepping your Data

This is the hardest step of the process because it is very unlikely that your data will match exactly what the data looks like in the Tableau accelerator. You may be missing columns, have slightly different columns, or store your data at a different level of aggregation. 

Luckily, the accelerator documentation on the Tableau Exchange makes it very clear which data fields and types are needed to successfully swap out the data sources.

For example, let’s look at the Financial Statement dashboard in the Tableau Exchange. As a side note, the description is worth reading in full to see if the metrics and business questions align with what you are trying to achieve. Several accelerators also have demo videos that walk you through the dashboard. 

However, we must jump to the Required Attributes section above the visualization for data prep purposes. This gives us a complete list of the fields we will need, the data types required, and any other expectations, such as expecting only positive values. For the financial statement dashboard, our data will need to have 11 fields:

  • Month (date): Month
  • Company (string): Legal Entity, Company, Cost Center
  • Revenues (numeric): Sales, Revenues
  • Operating Costs, COGS (numeric):  Operating Costs, COGS (Cost of Goods Sold)
  • Operating Expenses, OpEx (numeric): Operating Expenses, OpEx [positive value expected]
  • Depreciations & Amortizations (numeric): Depreciations + Amortizations [positive value expected]
  • Interests & Taxes (numeric): Interests + Taxes [positive value expected]
  • Current Assets (numeric): Current Assets
  • Current Liabilities (numeric): Current Liabilities [positive value expected]
  • Total Assets (numeric): Total Assets
  • Total Liabilities (numeric): Total Liabilities [positive value expected]
  • Pro Tip: To see the extract of this data, you can download the workbook and open it, then, in the Data menu, go to Financial Statement Data -> Export Data to CSV. This may make it easier to see in Excel or any other tool you may use for data preparation.

Step 4: Using the Data Mapping Tool

When opening the accelerator workbook in the Tableau Desktop version 2023.1 or a later version, a Data Mapper popup should appear in the lower left corner. If this doesn’t appear, or if you have already closed it, you can go to Data -> Open Data Mapper.

This will prompt you to connect to your data. The mapping should be seamless if your data has the same column names as the existing data. However, you may need to manually change some mappings if columns do not have the same naming conventions.

Below are some screenshots of what you should see as you move through each step in the process.

Step 1:
Step 2:
Step 3:
Step 4:

Data Mapper Testing

While Tableau has made this process easier than past alternatives, I was skeptical that this would perform well in the wild when dealing with real data. I ran a few tests to better understand this new feature and to see what the mapper would do in different scenarios.

Test 1: Same data, extra columns

I created a data set with the same column names and data types but had some extra columns. As anticipated, Tableau had no problem with this, which means you can easily use the accelerator as a starting point and add more of your own metrics/fields. 

Test 2: Same data, missing columns

I wanted to check and see if any missing columns would make this process fail or if it would still do the mapping, so I took away the columns ‘Depreciations Amortizations’ and ‘Interests Taxes.’ The Data Mapper allowed me to replace the data with only 9 of the 11 fields mapped. After mapping was completed, any sheets or calculations using these fields had errors, as expected. 

I like this result because if there were fields that I truly did not have in my data and I wanted to remove them all from the accelerator, after doing the mapping, it is obvious which sheets with errors now need to be removed or which calculations need to be updated.

Test 3: Same data, slightly different data types

To see if Tableau would be able to adjust based on some slight differences in the data intelligently, I changed a few of the columns in different ways:

  • Changed the ‘Total Assets’ column from a decimal to an integer
  • Changed the ‘Current Liabilities’ column from a decimal to a string (although still numbers)
  • Changed the ‘Month’ column from a date to a datetime

Surprisingly, the only one that caused a problem was the last change–trying to substitute a date for a datetime. However, the error that appears when hovering over the grayed out ‘Month’ column is beneficial in guiding the user to adjust the data type before proceeding, and even mentions this can be done on the Data Source page.

While there are certainly more tests that could be performed, just going through these exercises shows that Tableau has developed an excellent feature that will save a ton of time in Tableau development.

In Conclusion

We hope this guide helped you get started with Tableau Accelerators. To see more accelerator examples, visit phData’s Dashboard Library. If you want more information about developing in Tableau or need help implementing an accelerator, contact our team of experts!

Data Coach is our premium analytics training program with one-on-one coaching from renowned experts.

Accelerate and automate your data projects with the phData Toolkit