All tagged Data Preparation
Do you want to use Tableau to look at running headcount over time? For example, you work with employee level data which has Start Dates and End Dates and you'd like to be able to see how the total active headcount at the company has changed over the last 8 quarters.
That's something Tableau can do, but it isn't going to work out of the box. It's going to take some creative data structuring and calculations to get things working.
In 2020, Tableau unveiled a new way to combine multiple tables of data in Tableau Desktop: a Relationship. Billed as "smart joins", relationships provide the capability to merge multiple tables of data horizontally on shared dimensions (like a join). However, they don't duplicate data when the tables being combined have different levels of granularity.
So, are they better than joins? Not always! Relationships can reduce data duplication and improve performance, but a data set that is generated from a relationship might present blind spots if the data isn't perfectly clean.
Unions are an incredibly powerful tool in our data prep stage when working in Tableau Desktop. But how do you know when to use a Union and when to use a Join?
The general rule of thumb is: when you want to add rows of data to your data source, use a union!
Getting the structure of our data correct for Tableau Desktop can make a huge difference. Tableau is pretty picky about the way it wants our data to be formatted. Generally, each column should be a new dimension or measure.
Joins in Tableau Desktop are one of the primary methods for combining multiple tables of data horizontally. One of the great things about joins are their flexibility! You can choose if you'd like to keep all data from both tables in your result set, or if you prefer to keep only matching records.
A huge benefit of using joins in Tableau is that we can combine data from multiple tables into a single table for analysis. When those tables are in different databases, it can be a little tricky to work out where to start. If you know how to join tables within the same database, that's great! Cross-database joins only take a few more steps to execute, and I'll walk you through them in today's video.
How do you join two tables together in Tableau when they don’t share the same common field? Or what if that common field is slightly different in both tables? A Join Calculation can help solve a lot of those problems! In this video, we take a look at how to use a join calculation to join tables with mismatched fields.
Are you having trouble getting your data to work in Tableau the way you want? Join us to learn how to prepare data for analysis in Tableau!
You can find a thousand tutorials on how to use Tableau, but they're all worthless until your data is structured for analysis in the format that Tableau likes. Don't get stuck in your analysis journey because Tableau doesn't like your data.
6 data formats to avoid, 8 key rules for structuring data, and 1 video you can't miss. Check out the recording to learn about the key concepts you can utilize to prepare for your data for Tableau.
Blending is nearly as old as Tableau Desktop itself. Executing a blend in Tableau is a method for relating data from multiple different tables so it can be analyzed together. For instance, a sales value and a quota.
The superstore data that Tableau provides is a helpful tool for practice, mockups and trying out concepts. However, one of the frustrations is that it can get “out of date” so to speak so the latest date in the data set might be December 31, 2021 even though today’s date is month’s later.
When joining two tables in Tableau, have you ever wondered any of the following questions?
Is the join working?
Is the join resulting in data duplication?
Am I losing rows of data due to the type or argument of the join?
In its natural state, survey data is rarely in a format where it is optimized for analysis. If you need to visualize survey data, it's likely going to require some preparation and reshaping.
Tableau's default method for relating tables of data on shared columns is now Relationships. The previous default was joins. Despite looking outwardly similar, these approaches have some key differences.
Groups are an incredible feature in Tableau, but they are limited. For instance, you can’t leverage a group for a blend or cross-database join.
If you need to use the output of a group for data preparation, you will probably want to turn it into a calculated field. Rather than doing it slowly by hand, why not leverage a quick calculation which can speed up the process?
Check out the video below to learn how you can easily turn a group into a calculated field in Tableau.
The numerous grouping options in Tableau Prep are one of the best parts of the tool. In addition to manual grouping, you can group based on common characters, pronunciation and spelling. However, scrolling through existing values and grouped values to determine if the groups which Tableau Prep created were a good fit can be cumbersome.
No! This post is not a mistake. It really is our third blog post in the last three years detailing how to transpose values in Tableau Prep. The tool is rapidly evolving, and the good new is that the process of transposing values is becoming more seamless each time.
