How to fix broken blends in Looker Studio: 5 real scenarios and solutions

How to fix broken blends in Looker Studio is something every data practitioner will eventually need. Blends are one of those features that look simple on paper, but become messy the moment your datasets aren’t perfectly aligned. You add two sources, everything looks fine, you drag a metric to a chart… and suddenly your numbers don’t make sense.

If you’ve ever looked at a scorecard and thought, “That cannot be right,” this guide is for you.

Looker Studio blends are powerful because they allow you to join different sources visually without writing SQL. But the tool also has strict rules. When those rules are violated, you see missing values, duplicated rows, or inflated metrics without obvious error messages.

In this guide, I’ll walk you through 5 real scenarios where blends break, why they break, and how to fix them. We’ll use two datasets I often use in my tutorials: Spotify 2023 and Superstore Products.

What is a Looker Studio blend?

A blend in Looker Studio is a way of combining datasets based on a shared key. Think of it as a visual JOIN. You pick the dimensions that align both tables, and Looker Studio merges them on those keys.

The most important rule is that the join keys must match both in content and granularity. This is where blends fail.

How to fix broken blends in Looker Studio – Examples

Scenario 1 – Different field types (text vs numeric)

This is one of the most common causes of broken blends.

Imagine blending Spotify streams with another dataset where streams are stored as text. Even if the numbers look identical, Looker Studio considers them different types.

Symptoms

• Blank metric values
• Rows that appear correct but don’t aggregate
• Charts showing “No Data”

Solution

Convert both fields to the same type before blending.

Example:

CAST(streams AS NUMBER)

or

CAST(Streams Category AS TEXT)

Tip: Do this in each source, not in the blended model itself. Transform early → blend later.

Scenario 2 – Granularity mismatch

Let’s say you want to blend Spotify streams grouped per song with a separate table that contains streams grouped per artist.
You blend on artist(s)_name, but your Spotify table is at row-level per track.

What happens?

You get duplicated rows or inflated totals, because multiple tracks join to the same artist.

Fix

Aggregate first, then blend.

Create a calculated field:

SUM(streams)

Group by artist(s)_name.

Blend artist-level → artist-level, not row-level → aggregated-level.

This one step resolves half of “my numbers don’t match” tickets.

Scenario 3 – Missing join keys

Superstore Products is a perfect example. You might have one sheet with revenue and another sheet with margins. Both include Category and Sub-Category, but one of them calls it “Subcategory” and the other “Sub-Category”.

Looker Studio treats these as different fields.

Solution

Rename fields so the label and content match. If the naming is inconsistent, create an alignment field:

LOWER(Subcategory)

Do the same on both sides:

LOWER(Sub-Category)

Then blend using the alignment fields.

Scenario 4 – Blending on a field that has duplicates

This is extremely common with fields like Superstore product names or Spotify track names. Two different products can be called “Chair”. Two tracks can be called “Home”.

If you blend on these fields, you multiply rows by accident.

Fix

Pick a unique identifier. For example:

• Spotify → track_name + artist(s)_name
• Superstore → Product Name + Category

Or even better, if your dataset includes IDs (Store code, Row ID, Transaction ID), always try to use them first.

Scenario 5 – Date granularity mismatch

Looker Studio doesn’t “understand” that daily data belongs to weekly or monthly buckets unless you tell it. If Dataset A is daily and Dataset B is monthly, blending on date produces chaos.

Solution

Make the granularity consistent before blending.

Example:

DATETIME_TRUNC(Order Date, MONTH)

Do the same to the other table:

DATETIME_TRUNC(Delivery Date, MONTH)

Blend at month level. Your numbers instantly align.

Final recommendations – How to fix broken blends in Looker Studio

Here is the mindset shift that changed everything for me:

Fix the data before you blend. Don’t ask your blend to fix the data.

Think of blending as a finishing step, not a rescue tool. When fields are clean, joins are consistent, and granularity aligns, Looker Studio blends behave beautifully.

FAQs – How to fix broken blends in Looker Studio