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Year-End Review: Best Blogs of 2022

Updated: Nov 7, 2022

Our staff loves all things data. From 9-5 they're developing dashboards, architecting cloud environments, transforming data, and doing all the things needed to modernize analytics for our clients. And then they sign off work and...keep thinking about these things.

I imagine our Tableau Power Couple - The Moore's - sitting around the dinner table. Chatting about their day, making sure their little guy eats his veggies. "THAT'S IT! I'M DRAWING A LOTUS FLOWER WITH TABLEAU!"

The Simpsons
Real Moore's Not Pictured

In all seriousness - the staff has provided some amazing content this year. If you're looking for Tableau tips and tricks, Python code to try out with dbt Labs, or a modern analytics roadmap, check out the links below.

Cleartelligence Data & Analytics Articles 2022

In recent years there have been multiple scientific studies designed to confirm what many of us in the Data Visualization Community have already suspected; when it comes to art, people are drawn to curves. Think about some of your favorite pieces of Data Art. I am willing to bet that the majority of them contain some type of curved element. Not only are curves more aesthetically pleasing than straight lines and sharp corners, but they have that ‘WOW’ factor, because as we all know, curved lines do not exist in Tableau. They take effort, and when it comes to drawing curves, most people don’t know where to start. But you don’t need to be an expert in Tableau to create beautiful radial charts, or to add some impressive curves to your dashboards. You just need to know the math, you need to know how to structure your data, and you need to know how to bring those elements together in Tableau. That’s the goal of this series. To hopefully demystify some of this work and make it more approachable, and to provide some examples. This series will focus on three types of curved elements; Circles, Bezier Curves, and Sigmoid Curves.

There are many types of Bezier curves varying in complexity from very simple to ridiculously complicated. One commonality with these types of curves is that they rely on ‘control points’. This post is going to focus on quadratic Bezier curves, which have 3 control points. An easy way to think about these points is that there is a starting point, a mid point, and an end point, creating a triangle. The starting point and end point are simply the start and end of the line. The other point, the mid point, will determine the shape of the triangle, and in turn, what that curve is going to look like. Now let’s see how the position of that mid point (creating different types of triangles) will affect the curve.

Whether you realize it or not, these curves are everywhere on Tableau Public. Sankeys? Sigmoid Curves. Curvy lines on a map? Sigmoid Curves. Curvy bump charts, or curvy slope charts, or curvy dendrograms, or curvy area charts? All Sigmoid Curves. The main difference between this type of curve and the Bezier curves we discussed in Part 2 of this series, is that only 2 points are needed to draw a Sigmoid Curve. If you have the start point, the end point, and the right formulas, the math will take over and create that nice symmetric s-shaped curve for you. So what formulas should you use? The answer is, as with most things in Tableau, it depends. We’re going to cover two different models for drawing Sigmoid curves, we’ll call them the ‘Standard’ model, and the ‘Dynamic’ model.

Think about most of the chart types that use sigmoid Curves. Sankeys, Curvy slope or bump charts, dendrograms…they all have something in common. The start of the lines and the end of the lines are uniform. If they are running from left to right, the start of all of the lines share an X value, and the end of all of the lines share an X value (columns). If they are running from top to bottom, the start of all of the lines share a Y value, and the end of all of the lines share a Y value (rows). When these conditions are true, which they will be in 99% of the applications in Tableau, you can use the ‘Standard’ model. If either of those conditions are not true, you can use the ‘Dynamic’ model.

The thing that I love most about Tableau is the incredible flexibility. No matter what you are trying to do, there is a way to do it. And more often than not, there are actually several ways to do it. That’s where this series comes in. There are so many incredible hacks and techniques floating out there in the Tableau Universe, it can be difficult to figure out which ones to use and when. In each installment of this series we’ll be focusing on one specific ‘question’ and discuss the pros, cons, and use cases of various techniques. And our first question of the series is… “How do I turn off the default highlighting in Tableau when I click on a mark?”. And the answer is, of course, “It Depends”.

You may have heard people talk about Figma or Illustrator, or maybe you’ve heard people talking about wireframes or prototypes. Perhaps you’ve seen dashboards with custom backgrounds. Some questions seem to come up often: What do you use Figma for? What are wireframes? Do I need prototypes? Should I use background images in my dashboards? Are these tools just something to use for flashy dashboards for Tableau Public? Why wouldn’t you just do your mockup in Tableau?

These are all really good questions to be asking, especially if you haven’t used these tools in your work before. In this installment of the “It Depends” series, I’ll unpack how and when I use design tools in my dashboard development process.

We’ve developed a proven methodology to help industry-leading organizations modernize their analytics. It starts with a capability / maturity assessment where we help your organization envision a future state and its associated benefits. Roadmaps generally include four important steps: Data Ingestion, Data Curation, Premium Analytic Content, and End-user Enablement.

Have you ever tried to export a table from Tableau to Excel, only to realize your fields with leading zeros were losing those leading zeros? Pretty frustrating!

Don’t worry, I’ve figured out a simple trick that will allow you to keep your zip codes and account numbers in their proper format so end users can VLOOKUP to their heart’s content (without ever learning how to format cells to include leading zeros themselves).

Growing up I watched a lot of ESPN. Probably too much.

I'd wake up before school, throw on Stu Scott and Rich Eisen, and watch highlights until the last possible second, before running out the door. On a sick day, instead of cozying up with The Price is Right, I would happily watch SportsCenter all day. Stats, trades, "BOO-YAs," the top 10 plays, no Skip Bayless. A simpler time.

Suffice it to say I was PUMPED when, browsing through Twitter this morning, I saw this post by the hugely talented Brian Moore.

Imagine you're moving into a new house. You've got to pack up all your stuff, load it into the truck, then unload it at the new house. Your friends pretend beer and pizza is fair compensation for their herniated disks. A good time is had by all.

In the ETL methodology, imagine having to pack the moving truck in exactly the way your stuff will appear in the new house. Couches and dressers and dozens of kitchen gadgets you had to get (but have used twice) all need to be precisely curated before you pull away from the curb.

Welcome to our new series, Totally Useless Charts & How to Build Them. In each installment of this series we’ll look at one very custom chart, something with almost no real use cases, and we’ll walk through, step by step, how to build it. The purpose of this series isn’t necessarily to teach you how to build these specific useless charts, it’s more about talking through the techniques, the approach, and the thought process behind each chart. Our hope is that seeing how we went about building these will help you with your own custom charts. But if you do somehow find a great use case for one of these charts, by all means, please download the workbook and use it as your own.

Welcome to installment #3 of the “It Depends” blog series. If you’re not familiar with the series, each installment will cover a question where there is no clear definitive answer. We’ll talk through all of the different scenarios and approaches and give our recommendations on what approach to use and when. The question we are tackling this week is “How can dashboard actions be used to filter on multiple selections in a sheet?”. Pretty easy right. Just use a filter action…or a set action…or set controls…or a parameter action. There are clearly a lot of different ways to accomplish this, but which one should you use? And I think you know the answer…It depends!

In this installment we’re going to learn how to build “hand-drawn” bar charts in Tableau. These of course aren’t actually hand-drawn, but using some interesting techniques, and a lot of random numbers, we can kind of make them look that way. If you would like to follow along, you can download the workbook here, and the data here.

This post describes the first of two methods of creating an Org Chart in Tableau. If you haven’t read the post where I give a short summary of the pros and cons of each method, you can find that here. If you would like to download the data, and follow along, you can download my files to follow along here.

In order to create an org chart (or any hierarchy chart or dendrogram) you will need to establish the relationship between each employee and every level of the hierarchy above them, as well as to their own direct reports.

To do this, using only Tableau Desktop, we will create a join for every relationship in the hierarchy. One for each level of management, all the way up to the CEO, and one for employees they directly manage.

Before I was a data visualization consultant or an analyst, I was an art student. I want to share some of the lessons that, whether I knew it or not, have shaped how I approach my work.

I didn’t have my start in analytics. Honestly, when I was a college student, analytics, data science, and data visualization majors weren’t a thing, and analyst was not one of the jobs that were introduced as a possible career path (maybe I’m aging myself). I don’t know if 20-year-old me would have picked the major, anyway.

To automate tableau refreshes once a dbt job is complete, we can use the dbt Cloud API, Tableau server client, and python to create a simple program that can trigger a dbt job and, once completed, can immediately trigger a data source/workbook refresh. This program would live inside a lambda function and can be put on a schedule to run every hour. Some may wonder why python was used, and the main reason for this is that the Tableau server client package is only available in python. If you prefer to use another language to build this program, then you will need to use the Tableau REST API. The Tableau server client has links to the equivalent API call in its documentation.

When it comes to visualizing data, it’s no secret that the Mercator Projection has it’s issues. Certain countries, especially in the northern hemisphere, appear much larger than they are in reality, and it gets worse the farther you move from the equator. Just look at the difference in the size of Greenland between the Mercator and Robinson projections.

Chart Champ returned to Fenway Park this September for it's first in-person event since 2019. As is tradition, the centerpiece of the day is the data visualizations competition, where five finalists demo their Tableau dashboards to the judges and audience. We also took a deep dive into all things data, with presentations about data governance, dashboard design, data transformation, cloud analytics, and much more.

In this post we’re going to look at two different ways to use BAN’s to swap KPI’s in your dashboard. If you’re not familiar with the term “BANs”, we’re talking about the large summarized numbers, or Big Ass Numbers, that are common in business dashboards.

When I build a KPI dashboard, I like to give my users the ability to dig into each and every one of their key metrics, and the techniques we cover in this post are a great way to provide that kind of flexibility. Here is a really simple example of what we’re talking about.


An enormous thank you to Brian and Jacqui Moore for their tireless efforts on their Doing Mo(o)re with Data Blog. And of course for letting us repurpose that work here.

In addition to blog content, we've also published videos to our YouTube Channel. Check out our new explainer video - How Does Cloud Migration Work? as well as on-demand webinars from earlier this year including Advice from a Tableau Visionary and The Evolution of Data Visualization.

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