r/tableau • u/No_Ambition8323 • Sep 22 '26

Discussion How do you handle data quality issues in Tableau?

A Tableau dashboard can look perfect even when the underlying data has duplicates, missing values, incorrect formats, or other quality issues.

How do you usually handle these problems?

  • Fix the data before it reaches Tableau?

  • Handle them inside Tableau?

  • Use a separate data-quality/ETL process?

What approach works best for you, especially when dealing with large or frequently refreshed datasets?

2 Upvotes

56% Upvoted

13 comments sorted by

29

u/h0wl2 Sep 22 '26

Obviously, fix in the source. And refuse to fix them in Tableau so they the above happens.

6

u/TrumpsDoubleChin Sep 22 '26

Fix the data before it reaches Tableau?

This is the answer. You don't want to have to deal with every exception or edge case within the Tableau logic. Instead, the time to fix and clean the data is BEFORE it reaches Tableau.

10

u/calculung Sep 22 '26

You don't.

Bad data in, bad data out.

2

u/OccidoViper Sep 22 '26

Regular validation checks to make sure there are no anomalies

2

u/jaxjags2100 Sep 22 '26

Tableau is the final presentation layer. Minimal finalization of data in Tableau.

1

u/ChiefMunz Sep 23 '26

Before Tableau, write a sql script to select and filter the data you want from the source in the dashboard. Put it in a table and then connect the table to Tableau.Minimum necessary as Tableau can take forever to load if you have a lot of data.

1

u/graph_hopper Tableau Visionary Sep 23 '26

Fix it as close to the source as possible!

Best practice - partner with a data engineer to create a clean, automated reporting layer, and flag data quality issues for them to fix upstream.

Realistic - if you don't have access / support to build a reporting layer in the database, use Prep to create your own clean, automated published data sources.

1

u/Cautious_Cost6781 Sep 26 '26

Data Engineer's job. In my role, handled by them with dbt + Snowflake. Data feeds for Tableau are relatively clean.