Looker Studio (formerly Google Data Studio) has become a go-to tool for building dashboards and visualizing data especially for marketers, analysts, small teams and business owners. It's free, integrates well with other Google products, and offers an easy drag-and-drop user interface.
While it looks simple on the surface, working with Looker Studio can sometimes be frustrating.
From slow-loading reports to limited blending options and timestamp errors, many users eventually hit roadblocks that aren't so easy to solve without workarounds or deeper technical knowledge. In this article, we'll break down some of the most common challenges users face with Looker Studio and how to navigate them without losing your cool (or your client's patience).
Challenge 1: Handling date integration
Have you ever uploaded data to looker studio and discovered that the date column range presented in looker studio was different from what you saw in your source data.
Example: In your google sheet or CSV file, the maximum date is –05–10' but in looker studio you are seeing –10–05' or even something else as the maximum date.
This is because looker studio's intuitive design automatically converts date columns from source data to date but it can sometimes be wrong.
Here is how you an fix it:
Step 1: Convert the date column to text data type.
Step 2: Add a new calculated column.
Step 3: Apply this formula in the newly created calculated column using the date your converted to text.
Take note of the date structure from the source data, if the data comes as month/day/year then use "%m/%d/%Y", if the date comes as day/month/year then use "%d/%m/%Y".
Once this formula is successfully applied, your date should be correct.
Challenge 2: Importing data from csv files
If you have ever tried to upload a csv file to got "error".
It might just be because looker studio is not ANSI compatible, when uploading a csv file, ensure to convert it to utf-8 character encoding before uploading.
By the way, you can find multiple looker dashboards I have created via my PORTFOLIO << link
You can do this by opening the file with a TXT editor like notepad and then converting to utf-8 as seen below.
Simply click on the ANSI icon and pick utf-8
Challenge 3: Upload error due to spaces empty rows.
Sometimes uploading a csv file having blank spaces like the one seen below will lead to error during upload.
Simply replace the empty spaces with a place holder like 0, null or a default number to prevent this error.
Table new look.
Check out other related medium articles I have created via the links below:
How to load data into big query using python
How do you solve this problem as a data analyst
Challenge 4: Blending and joining related tables
Blending data from multiple tables is sometimes inevitable when creating dashboards, looker studio blends are just like blends in SQL. You must blend properly else your final results and skewed and incorrect.
1. Common Key Requirement To blend two tables, they must share a common key, if you trying to blend sales table with customer table, then both tables must have a related or common column such as customer id or customer name. Even though the columns may have different column names, as long as they contain matching values (keys), they can be used to establish a relationship between the tables.
2. Matching Data Types The common key used to join the tables must be of the same data type in both tables. If the data types differ, Looker Studio will throw an error when you try to use the blended data in visualizations.
3. Select on data you need When creating blends in looker studio, you might be tempted to import all columns from all related tables into your final blended table but importing too many columns can affect the performance of your dashboard, avoid this by importing only tables need for analysis.
Challenge 5: Validating your blends
If your blends or joins aren't validated in looker studio, you might be presenting the wrong data to your stake holders.
Here is how to ensure your blends are done properly:
Step 1: Identify the fact table and the dimension table, the fact table is usually the table with measurable metrics and facts like order date, order amount, discount rate, quantity ordered etc while the dimension table contains attributes describing metrics present in the fact table. Example, Sales table is a fact table while Customer details or Product details table are fact tables. Once your fact table is identified, count the number of records present in your fact table. In the image presented below, the fact table has 120,000 records.
Next, also count the number of records in the dimension table, number of records present in the dimension table are usually way less than number of records present in the fact table, as far as the number of records present in the dimension table are less than in the fact, you can validate your blends using this method.
Step 2: Blend your fact table with the dimension table using the LEFT OUTER JOIN and count the number of records in the new blended table. If the new table has more records than the original fact table then your blend is wrong, this is usually because some records occur more than once in the dimension table. Example, you are joining sales table to product table using the product_id column, if there is more than one occurrence of product_id 5 in the dimension table then the blend result will be wrong, in this situation the source data needs to be checked for duplicates or wrong entry.
On the other hand, if the number records present in the original fact table and the blended table are the same then your blend is correct.
Note: This does not apply when blending two fact tables
You can learn more about this from this article: Writing proper SQL joins
Challenge 6: Version control
You just added a feature which has affected the way your dashboard looks and you need to revert to the previous version before this change, simply use VERSION CONTROL.
Here is a video showing how you can implement version control in looker studio:
Challenge 7: Visualizing metrics with significantly different scales
When visualizing two metrics with significantly different scales — like NUMBER OF SESSIONS and BOUNCE RATE (%) — on a single line chart in Looker Studio, the smaller values can easily appear flat.
Here is a simple fix to help make both variables clearly visible, even when their scales differ significantly.
Simply fix both metrics on different axis as seen in video below.
Thanks for reading, if you found this article insightful, remember to give it a clap and if you feel there is anything I missed, please feel free to air your opinion in the comment section below.
Here is a link to some other medium articles I have written:
How to load data into big query using python
How do you solve this problem as a data analyst
God bless!!