Most dentists recommend visiting every six months for a routine cleaning. Have you ever wondered if you really need to go in that often? Why not every two months? Eight months? Two years? There are several indicators that determine how often you should visit the dentist
A few years ago I was working with a Fortune 500 restaurant chain that many of us frequent. What you probably don’t know is how often the rats frequent their restaurants too. The chain was trying to reduce the number of pest incidents at the worst offending stores, but was having trouble determining which stores were worst because their data was so messy.
I’ve pulled a set of data for the top pitchers in Major League Baseball in 2017. Let’s say we want to do an analysis to see which of the best pitchers got extra help from their teams and which didn’t.
I’ll start by creating a scatter plot displaying wins and losses by each pitcher. Pitchers with dark blue circles that are farther toward the top did not get much help from their teams. Pitchers with lighter circles that are closer to the bottom did.
Data blending is a great tool to have in your Tableau arsenal, but it has its quirks. It's one of the oldest forms of data preparation in the product. There are a lot of unique properties to the data blending feature which are important to understand if you're going to use this method to prepare your data for Tableau.
I was working with a client recently and they wanted to create a calculation in Tableau allowing them to compare performance this month to date versus the same time period last month.
Let’s say we are comparing sales from this month to the previous month. We’ll start by creating a calculation to return sales for just this month:
When creating averages on a measure in Tableau, null values aren’t factored into that average...
Did you play professional basketball? I didn’t either. You know who else didn’t? Daryl Morey. In 2007 he became General Manager of the Houston Rockets and in 10 years at the helm has yet to have a losing season.
Some Tableau dashboards have performance issues. Commonly people will assume it’s because they have too many rows or columns of data, but that’s not always the primary issue. Your dashboards might be hindered by memory, data source type, computer memory, or a number of other factors. Check out the video below to learn about some of the common issues plaguing Tableau dashboard performance and how you can rectify them!
A couple weeks ago I was teaching a course and received a question from a student. Her question was “How can I use Tableau to only show the most recent 3 transactions per customer?” I thought I’d have an answer for her quickly, but I was wrong.
My first thought was, let’s just use the “RANK” function to accomplish this. We’ll use it as a Table Calculation to determine the most recent transactions by customer. I was feeling confident until I saw this:
A student recently asked me how she could create dynamic, color-changing labels based on whether a field passed a threshold. My first response was “Tableau can’t do that.”. My second thought was “How can I make Tableau do that?”
Most data isn’t stored in a manner that is optimal for analysis. It’s frustrating when Tableau doesn’t like your data structure. If you are new to Tableau or working with a new data source, you might run into issues getting columns and rows to display the way you want.
Tree maps are a data visualization used to communicate hierarchical values in a systematic way with nested rectangles. A lot of the tree maps I see look something like this:
I don’t know about you, but I don’t find this to be particularly informative or compelling. I prefer to use tree maps as a way to highlight a few relevant data points. Notice in the dashboard below how I use a tree map to highlight the top 10 items sold.
We have done hundreds of business intelligence (BI) projects. We often come into the middle of projects that are headed for failure and stakeholders are looking for a magic bullet. There is no magic but we do have repeatable processes that assure success.
First, how do we measure success for our BI projects?
If you’re like most people in the world of analytics, you probably didn’t get a formal education on the topic. Sure, we all know a few data scientists, but most of us came from other backgrounds. When semi-familiar terms are tossed around, it’s easy to nod your head along with the crowd while in the back of your mind wondering, “does that mean what I think it means?”.
I was given a challenge by a student recently. They wanted to color territories on their map by a region, but they wanted the intensity of the color to represent the amount of sales that occurred in an individual territory.