The time has almost come!
It has been an incredible three months at The Data School, and I have learned so many new things that sometimes it is hard to wrap my head around it all. But this coming week feels both special and stressful because my cohort will be taking the Tableau Data Analyst Certification Exam this Friday.
In preparation, I have decided to revisit some Tableau fundamentals that can be deceptively tricky, starting with Date Functions.
If you have ever tried to build a time-based dashboard in Tableau, such as sales over months, year-over-year growth, or shipping trends, you know how powerful and sometimes confusing date calculations can be.
When I first started, I thought, "How hard can it be? Just drag Order Date onto Columns!". Then suddenly, I had four different date field, duplicate axes, and a line chart that looked like a spider web. Not exactly the clean insight I was hoping for.
So how do we make sense of all those dates?
The answer lies in Tableau’s built-in Date Functions, which help you clean, calculate, and control how time appears in your dashboards.
As always, we are not reinventing the wheel.
We will use our favorite dataset, Superstore, and answer a few practical business questions:
- How fast do we ship orders?
- Which weekdays have the most sales?
- How does this month's sales compare to the same month last year?
To get there, we will explore five key date functions that make all the difference.
I. DATEAPART()
Purpose: Extracts a specific part of a date, such as year, month, or weekday.
It returns that value as an integer.
Syntax:
DATEPART(date_part, date, [start_of_week])
Use case: Find which month drives the most profit, not by full date, just by month.
Example:
DATEPART(’year’, #1985-06-11#) = 1985
DATEPART(’month’, #1985-06-11#) = 6
DATEPART('month', [Order Date])II. DATENAME()
Purpose: Numbers are great for computers to work with but as humans we do like to have names, this is exactly what DATENAME() does for us, it turns those numeric parts into words. For example instead of 1 we get "January". It returns the name of the date part as discrete string.
Syntax:
DATENAME(date_part, date, [start_of_week])
Use case: This function is useful when we want to change our axis labels or dropdowns in visuals to read more naturally.
Example:
DATENAME(’year’, #1985-06-11#) = "1985"
DATENAME(’month’, #1985-06-11#) = "June"
DATENAME('month', [Order Date])III. DATEDIFF()
Purpose: Calculates difference between two dates.
Syntax:
DATEDIFF(date_part, date1, date2, [start_of_week])
Use case: We want to see how long something takes to ship. The result is the number of days between order and shipment.
Example:
DATEDIFF('day', [Order Date], [Ship Date])
IV. DATEADD()
Purpose: Sometimes you want to shift time, for example, "orders placed in the last 30 days". This is where we can use DATEADD() it adds given number of date parts to the string date. We can add n-days or n-months to a string date.
Syntax:
DATENAME(date_part, inerval, date)
Use case: Create rolling time windows, set deadlines, or calculate forecast periods.
Example:
DATEADD('day', -30, TODAY()) // adding condition to the above statement
// will show updates to reflect rolling-month activity
[Order Date] >= DATEADD('day', -30, TODAY())
V. DATETRUNC()
Purpose: Truncates a date to a specific level, such as month, quarter, or year, returning the first day of that period.
Syntax:
DATETRUNC(date_part, date, [start_of_week])
Use case: Group daily data into months, quarters, or years for trend charts.
It is also great for month-to-date calculations or weekly summaries.
Example:
DATETRUNC('month', [Order Date]) This turns 2024-10-27 into 2024-10-01, grouping all orders from that month together.
Date functions might seem technical at first, but they are what transform raw timestamps into meaningful stories. Whether you are building a rolling 30-day view, comparing monthly growth, or exploring seasonal patterns, mastering these functions gives you complete control over time in Tableau.
As I prepare for the Tableau certification exam, I am realizing that understanding why these functions work, not just how to write them, is what truly builds confidence as an analyst.
Here is to a week full of studying, dashboards, and maybe a little caffeine.
Wish me luck for Friday, and if you are preparing too, remember: Tableau’s Date Functions are not just formulas; they are the clockwork behind every great story your data tells.
