Create Rounded Bar Charts in Tableau

Part 1: How to Make Rounded Bar Charts in Tableau

In this post you’re getting two videos for the price of one (considering they’re all free for now, that’s a good thing). I put together a relatively simple dashboard to help illustrate a few intermediate level concepts. In this first video I take a look at the number of total assists by NBA players during the 2017-2018 season. In case you were wondering, Russell Westbrook led the league in assists during that season. If you don’t know who Russell Westbrook is, then skip this Tableau stuff and watch the last video immediately (and then come back to the Tableau stuff).

In the first Tableau dashboard video, you’ll learn two concepts:

  • How to make rounded bar charts;
  • How to filter the number of bar chart marks via use of a parameter;

Part 2: Apply Custom Sorting in Tableau

In the second video I build upon the dashboard built in the first video by showing you how to add a custom sort. The custom sort relies upon the creation of a parameter and a calculated field. The parameter and calculated field enable the user to select either a dimension (e.g., Player Name) or a measure (e.g., sum of assists) from a drop down box and the visualization will sort ascending or descending as requested.

The calculated field relies upon the RANK_UNIQUE function.

In this context, RANK_UNIQUE returns the unique rank of each player’s assist total. The key with RANK_UNIQUE is that identical values are assigned different ranks. As an example, the set of values (6, 9, 9, 14) would be ranked (4, 2, 3, 1), as no tied rankings are allowed.

Part 3: Interact with the Dashboard

Bonus: Russell Westbrook on the Attack

For those of you who do not know who Russell Westbrook is, I’ve got you covered. These aren’t assists but in these situations, he didn’t need to pass!


Thanks to both the Tableau Magic blog for outlining the concept of rounded bar charts and the VizJockey blog for the custom sort methodology. Check out and support these  blogs!

As always, do great things with your data!

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.


The Ultimate Tableau Slope Graph Video

In this video I tackle the subject of slope graphs also known as slope charts. I had some fun putting together this dashboard that illustrates the changes in wins for NBA teams during the 2016-2017 and 2017-2018 seasons. From the video you’ll discover that Chicago, Atlanta and Memphis are on a Hindenburg-like trajectory, while trusting the process in Philadelphia led to huge season gains in overall wins.

Here’s what you will learn from this video:

  • How to create a parameter that enables a user to select which win statistic measure to visualize;
  • How to use Table calculations like, LOOKUP(), FIRST() and LAST() to calculate period over period change;
  • How the impact of Mike Conley’s injury affected the Memphis Grizzlies last season;

Click the pic to interact with the Tableau Public visualization, also download the workbook and data to dissect as needed.

For your convenience the calculated fields that I used to create the measures are listed here. Note that [Selected Measure] is a parameter that you need to create that lists all of the measures.

Calc Select Measure
CASE [Selected Measure]
WHEN “Home Losses” Then [Home Losses]
WHEN “Home Wins” Then [Home Wins]
WHEN “Overall Losses” Then [Overall Losses]
WHEN “Overall Wins” Then [Overall Wins]
WHEN “Road Losses” Then [Road Losses]
WHEN “Road Wins” Then [Road Wins]
WHEN “vs East Conf Losses” Then [vs East Conf Losses]
WHEN “vs East Conf Wins” Then [vs East Conf Wins]
WHEN “vs West Conf Losses” Then [vs West Conf Losses]
WHEN “vs West Conf Wins” Then [vs West Conf Wins]

Better or Worse
IF [Selected Measure] = “Home Wins” OR
[Selected Measure] = “Overall Wins” OR
[Selected Measure] = “Road Wins” OR
[Selected Measure] = “vs East Conf Wins” OR
[Selected Measure] = “vs West Conf Wins”
//WIN MEASURES: Negative delta treated as “WORSE”, Positive delta treated as “BETTER”
//LOSS MEASURES: Positive delta treated as “WORSE” (more losses are worse), Negative delta treated as “BETTER”
LOOKUP(SUM([Calc Select Measure]),LAST()) – LOOKUP(SUM([Calc Select Measure]),FIRST())
Delta ABS Value
ABS(LOOKUP(SUM([Calc Select Measure]),LAST()) – LOOKUP(SUM([Calc Select Measure]),FIRST()))
<Team> Trend: <AGG(Better or Worse)> by <AGG(Delta ABS Value)>
During the <Season> Season, the <Team> had <SUM(Calc Select Measure)> <Parameters.Selected Measure>.

I have to give thanks to Ben Jones at the Data Remixed blog for the inspiration!

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Free SQL Data Profiling Tool: Idera SQL Data Profiler

In this tip I will provide an overview of a completely free data profiling tool (at least as of the time of this post) that is easy to use if you are in the Microsoft database stack. You can download the IDERA SQL Profiling tool and immediately put it to work to perform basic column profiling and analyses. The tool will display a summary of the data contained in a selected table and each of its columns. 

Use this tool on the following systems:

  • Microsoft SQL Server: 2008 R2, 2012, 2014, 2016; 2017 Windows & Linux (provisional); Express, Standard, Enterprise editions
  • Microsoft Azure SQL Database
  • Amazon Relational Database Service (RDS)

IDERA SQL Data Profiler has some minor quirks but you can’t beat the price. Check out my review in the video above.

Just remember that data profiling should always be done initially before you start analyzing a new dataset or designing a new visualization. Always start with the basics.

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Filter Top N Values with a Slicer in Power BI

In this video you will learn how to filter the top N values shown in your bar chart visualization using a slicer.

  1. This technique uses one measure that generates a number 1-10, that will be applied to a slicer.
  2. Another measure will basically rank all of the values associated with your data bars and only return the values that are less than or equal to the number you select in the slicer.

The comments that I apply to the DAX function should help make it easy to understand. I have to give a shoutout to GilbertQ from the PowerBI community for coming up with the  initial approach which I tweaked for the video.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel!

How to Compare Actuals vs. Forecast in Tableau


Forecasting in Tableau uses a technique known as exponential smoothing. This where an algorithm tries to find a regular pattern in your data that can be continued into the future.

In this video I’ll share some helpful tips to help you determine which options you should select that will enable Tableau to make the most predictive forecast for your data. By the end of the video you will be able to differentiate between an additive and multiplicative data pattern and to evaluate MASE to measure the accuracy of the forecast.

I’m not talking about this Mase:

Harlem World

Rather, you’ll learn about the mean absolute scaled error (i.e., MASE) and how it helps you judge the quality of the model.

In addition, you’ll also also learn how to compare your actual data to the Tableau forecast in order to judge if the model is doing its job.

If you’ve used the forecasting capabilities in Tableau without knowing about these concepts, you might have generated an inaccurate error riddled forecast. Don’t just set a forecast and forget it. Watch this video and generate better forecasts in Tableau!

Here is additional reading from Tableau on the forecast descriptions (including MASE).

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.


How to Generate a Forecast in Power BI

In this video I’ll demonstrate how to use the forecasting analytics option in Power BI. Although Power BI’s forecast algorithm is a black box, it’s more than likely using exponential smoothing to generate results. At a very high level, exponential smoothing is an algorithm that looks for patterns in data and extrapolates that pattern into the future. To help exponential smoothing perform at an optimal level, it is very important to pick an accurate seasonality estimation, as this will have an outsized effect on the time series forecast.

If your data points are at the daily grain, then you’d use 365 as your seasonality value. If your data points are at a monthly grain, then you’d use 12 as your seasonality value. Generally, the more seasonality cycles (e.g., years) that you provide Power BI, the more predictive your forecast will be.

Without giving away the whole video, here is a pro and a con of using forecasting in Power BI.

Con: As I stated earlier the exact algorithm is a black box. Although based upon a Power View blog post, we can reasonably assume exponential smoothing is involved. Furthermore, the results cannot be exported into a spreadsheet and analyzed.

Pro: The ability to “hindcast” allows you to observe if the forecasted values match your actual values. This ability allows you to judge whether the forecast is performing well.

Check out the video; I predict you’ll learn something new.

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

2018 – The Most Popular Posts from

Two thousand eighteen was the third year in which I’ve been actively blogging here on It’s interesting to look back on my first full year of activity in 2016 and compare the blog’s growth in views and visitors.  For my first year of posting back in 2016, I had a little under a 1,000 visitors for the whole year, as compared to 2018 where I had about 28.5 thousand visitors. The past year was also momentous for my Youtube channel as I hit the milestone 1,000 subscribers mark.

I thoroughly enjoy sharing what I learn with people and that’s what keeps me coming back to share more. The positive feedback and the comments I receive from readers and followers make this pastime of mine worth carrying on.

Since I finally have a good sample size of views to draw from, I wanted to share a year in review of the most popular articles here on for 2018. Although I have a number of Tableau video posts, my most popular posts are related to companies and/or strategy. To my surprise, there seems to be a sizable audience for technology related strategy at Wal-Mart.

I want business intelligence practitioners to come here for their Tableau fix, but I also want business school, management information systems students and anyone else occupying the dual spaces of business and technology (like I’ve done throughout my industry and consulting career) to read my posts on various companies and their uses of technology.

Without further ado, here are my 10 most popular blog posts over the past year.

1. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 1

Popularity Index Score = 100

By far this was my most read blog article of the year and the most popular of all time. It is the first part of a three part series that I wrote that takes a look at a few different areas related to Wal-Mart’s use of technology. This post specifically relates to technology infrastructure and IT staffing.

2. Michael Porter’s Generic Cost Leadership Strategy Explained

Popularity Index Score = 90

This is the first of two posts where I cover the famous business professor’s generic strategies. In this specific post I describe the cost leadership strategy and its advantages and disadvantages. The cost leadership strategy is employed when a company aims to be the lowest cost producer in the market. It enables a business to reap higher than average profitability.

3. Michael Porter’s Generic Differentiation Strategy Explained

Popularity Index Score = 84

At the opposite side of the generic cost leadership strategy is the differentiation strategy. A differentiation strategy advocates that a business must offer products or services that are valuable and unique to buyers above and beyond a low price.

4. How to Conditionally Format Text Cell Color in Tableau

Popularity Index Score = 82

Of all of my Tableau related videos, this is my personal favorite. I spent many hours researching how to perform this trick for a thoroughly ungrateful party I should add (but I won’t get into that). Tableau is not Excel and table data should be used sparingly in Tableau, but if you have to display table data then do it with style. The upside of my struggle to solve this problem is that I was left with a great video to share with my followers. This video is the 2nd most viewed video on my Youtube channel.

5. How to Fix an Import Specification Error in Microsoft Access

Popularity Index Score = 75

This post hearkens back to my days as a data analyst for General Motors where I heavily used Microsoft Access. I have a love/hate relationship with Access in that it can be an effective tool for light data work but it has the ability to frustrate you with seemingly nonsensical errors. In this post I share my findings regarding how to overcome an Import Specification Error (Run-Time error ‘3625’).  One would think that “import steps” and an import specification can be referenced and used the same way in code, but that is not the case.

Articles 6 -10

6. Strategic Analysis of ADP (Popularity Index: 69)
7. Costco’s Underinvestment in Technology Leaves it Vulnerable to Disruption (Popularity Index: 68)
8. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 2 (Popularity Index: 63)
9. The Definitive Walmart E-Commerce and Digital Strategy Post (Popularity Index: 45)
10. More Than You Want to Know About State Street Bank’s Technology Strategy Part 3
 (Popularity Index: 40)

I want to thank everyone who follows and who also subscribes to me on youtube for their visualization fix. May you have a prosperous year in the making!

Since I’m writing this post on the Martin Luther King holiday I’ll have to close with a quote from Dr. King.

Life’s most persistent and urgent question is, ‘What are you doing for others?” – Dr. MLK 

I repost some of my articles on Medium
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