My brain has been assembling the thoughts discussed in this blog post for the past year and all these thoughts are mine. I completely own them and they are strictly my perspectives. No one has ever asked me to write this blog post or any other post, but I will readily admit that I have hesitated to write these thoughts for a number of reasons.
I don’t want the message of this post to be about me because it is not my intent. I do not want to sound like I am only talking about me and my own capabilities. However, I can only tell this story from my perspective because I only have first-hand knowledge of me and my own experiences.
I want this article to explain my multi-year journey with data and Tableau, and how this journey has both surprised and enlightened me. I could not have predicted this journey apriori, but looking backward in time, I really understand and appreciate the journey. I want to share insights I have gained while taking this journey.
I want this story to convey two additional messages. First, I want everyone to know that they can take a journey similar to mine if they are so inclined. If you are willing to try new things and to study new ideas, you will be able to take your own special journey. Secondly, I want to thank the Tableau developers that have allowed me to take this journey.
I believe that we learn from each other more than we realize. Many times new knowledge comes to us because we collaborate and share insights. The journey I describe in this post was only made possible because of the work and vision of some key people including bloggers, software developers, and the Tableau company.
I now realize that I respect, admire and appreciate the teams of creative people that build Tableau software. So I offer this article as a way of thanking them for helping me take this journey into the “Tableau Data Zone”.
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How Did My Journey Take Me Into the Data Zone?
With those words as a background, I now realize that I have arrived at a special point in space and time in my career. I am now in a career “sweet spot” because of outstanding professional opportunities, years of practice working with data, and a continuous and focused drive to learn new technologies and techniques.
The feeling I now have when working with data in Tableau, is like being in the “zone” in a basketball game. This occurs when you hit about every shot you take, running and breathing is easy and effortless and you don’t hear the crowd while the game is going on. When great athletes get into “the zone”, the game slows down for them and we as spectators get to appreciate their skill and mastery of the sport and wonder how they can accomplish what they do.
I now feel like I am in that kind of “zone” when it comes to working with data. I see things in data without even realizing how I see them. I do things automatically now that I used to have to stop and think about.
Some people that watch me work think that what I do looks easy. What they might fail to realize is that when someone makes something look easy, it isn’t necessarily due to a lack of effort. It might look easy because that person has sufficient experience, skills, knowledge, and has practiced extensively to make it look easy. For an excellent example of what I mean, go to the 1 hour and 11-minute mark of this podcast with Aaron Rodgers and listen to what he says for a few minutes. Especially listen to what he says about throwing the “hail Mary” pass in 2015 at about 1 hour and 15 minutes.
This story is for people that have struggled through the years to create computer programs to analyze large amounts of data. It is also for people who have tried to do data analytics without great software. My hope is that this story might inspire you to take your own journey into the “Tableau Data Zone”.
My Earlier Years With Tableau
The path I took to get here was built upon a strong foundation of education and continuous work experience in science, math, and computer programming (click for my background), coupled with a never-ending quest for professional skill improvement. For me, this path changed suddenly and dramatically in February 2008 when I discovered and began using Tableau Software.
Within a short time of first using Tableau, work that previously took me days to complete was now being done in hours, or sometimes minutes. Computer codes that had to be written were no longer needed. An explosion of ideas fired in my brain throughout the first few years of working with Tableau, followed by a few years of deeper insight and technique development. Looking back through time, I realize that nothing has done more for me to get me into the “zone” than using Tableau software.
Tableau has unlocked my potential to explore and understand data better than any other software technology that I have used. Quite simply, Tableau is magnificent, it is magic at times, and it allows me to discover in minutes some amazing things that lie hidden in vast quantities of data from all types of businesses and industries.
These insights might have been possible for me to discover years ago but would have taken me hours, days, or weeks to uncover, with a lot of additional work needed to get to the answers. How has this been possible? This answer is by using Tableau and exploring its full complement of capabilities on a day to day basis over many years and using it on many types of data sets.
Tableau is My Data Engine
Over time I have realized that Tableau is my data engine, rather than just my artistic canvas. Sure, I love Tableau visualizations as much as anyone and I create them and use them every day. I am in awe of the artistic talent of people like Kelly Martin, Jewel Loree, and others that create dashboards so beautiful that they should be entered into art shows. Tableau should have an art gallery at their office that highlights these amazing pieces of work.
These people have artistic capabilities that are better than what I possess. They turn data into works of art that are not only visually appealing but also have the ability to tell a story. Creating masterpieces like these, however, is not how I primarily use Tableau.
I use Tableau to explore and understand data, to find connections between things that people did not know existed before I began looking at the data. In particular, I look for things that help and hurt processes, and since most everything we do is a process, I find factors that lead to improvement in business and life in general.
I don’t want to downplay the role of the visual component of what I do. Although I use Tableau to explore and understand data, the visual output is still a large component of my work. I get excited when I am able to create a picture that tells a story that is so significant that it literally blows me out of my chair.
Great visualizations happen more often for me now than it did in my early days of using Tableau because I have more experience and I am quicker to find the answers. Sometimes when I go home after a break-through day at work, my wife listens to me for a few seconds before redirecting the conversation to something else because she can sense the enthusiastic discussion that I’m about to send her way. Creating a great visualization can be intoxicating and gives you the feeling of wanting to share the information.
When a visualization is created correctly, the story explodes from the screen. When this happens, you can do things like predicting what the results of regression modeling will be before performing the actual modeling. However, I do not always focus on the art of the viz, which is contrary to what many people strive to accomplish in Tableau. Beautiful visualizations are why many people are so enthusiastic about the software. What I am enthusiastic about, however, is the ability that I have to examine big, rich, complex data so easily, almost effortlessly in Tableau in a very short amount of time. It is almost unfair to competitors of Tableau.
Using VizQL To Uncover A Story
In my daily work, I focus on the story that is hidden in the data. The Tableau Data Engine is a V-12, very-high horsepower engine that I can take for a drive anytime I want. I fire it up every day and burn some rubber. I like to floor it, to challenge the engine and to find out what it is really capable of doing.
When I say that Tableau can do just about anything I want it to, I really mean that. The behind-the-scenes work that is completed by the Tableau data engine is simply stunning and it takes the burden off us for handling and managing the data. The brilliance of VizQL is in my opinion, the biggest unheralded feature of Tableau that makes it what it is. VizQL does the work for us so that we can sit back, relax and ask questions of the data.
Finding answers to multiple data questions is what I love about using Tableau. I can ask any question I want, and Tableau can deliver the answers. To understand what VizQL is and how it allows us to find answers to data questions, I have included at the end of this post some text that I found from May 2006 that describes VizQL.
Learning to Work With Big Data and Tableau
There are limits to everything, of course, and I’m starting to experience a bit of a bumpy ride as I speed down the multi-billion line Big Data super highway with Tableau. In a future blog post series, I will explain some of the limits I have experienced in using Big Data in Tableau and I will explain some of the strategies I have developed for overcoming these limitations.
For normal business cases, sometimes I have to invent some techniques or approaches to find answers to my questions. This blog contains these types of techniques. When I need help, I call Joe Mako or I read Jonathan Drummey’s amazing work, explore the work of the other Tableau bloggers, or I watch the Tableau training videos. There are so many ways to learn how to get things done in the Tableau community that there are nearly no limits to what is possible.
Developing Your Own Style and Understanding
Eventually, you learn how to do things in Tableau that make sense to you. You develop your own style and way of interacting with Tableau. When you spend enough time working with a large number of examples, you begin to enter the “zone of unlimited possibility” when you are working with your data.
With experience, you become a mechanic that is capable of fine-tuning the V-12 engine. You learn how to get more horsepower out of the Tableau data engine because you have learned to drive it better than when you started. These abilities do not happen at once, but you can slowly fill your toolbox of techniques if you are willing to put the work into getting better with the tool. If you stay dedicated to learning Tableau, one day you will wake up and realize that if there is a story in the data you have to examine, Tableau will help you and allow you to tell the story.
The Value of Experience
Being able to tell a data-based story is where I am today. I can tell the story hidden in data. It takes me less time to create the story. Sometimes I may have to work like a mechanic on my data before sending it to the data engine. For more complex cases, I might use Alteryx to prepare the data for Tableau. Although there will always be a trial and error approach to working on the “edge of possibility” in Tableau, I find myself having to spend less time in that mode and more time in getting directly to the answers.
With experience, you understand things like the importance of data structures and formats. You learn how to import and export data efficiently and effectively. You learn how to accomplish amazing things through multiple steps by sequentially importing data and exporting results from Tableau. Experience buys you efficiency, immediate insights, and comprehension that allow you to fly freely through your data like a bird soaring on a thermal. It is an amazing flight indeed.
There are many things that I do with Tableau that I cannot directly demonstrate in my blog because they are work-related and/or proprietary. For these reasons it makes it hard for me to explain to you why I am so enthusiastic about using Tableau. Much of what I’m eluding to in this particular blog post resides in this closed (“proprietary”) domain. However, if you take the time to read other posts I have written, you will see some of the magic explained in particular technique articles that have been used on non-work related data. When I develop a great technique, I try to share it if at all possible.
Sharing Insights and Helping Myself By Writing This Blog
About one year ago I decided to begin writing this blog about Tableau techniques to demonstrate how I use Tableau to get things done. I did not expect that anyone would actually take the time to read anything that I have written, so I am surprised that people actually read this blog. Nearly all of the techniques I have written about have come from my experience gained through the years while trying to solve real-world problems. In fact, this blog has been evolving into a problem-solving platform for me to share ideas with an anonymous audience.
There is no road map that I am following to create the articles for this blog. I simply decide on a day to day basis what I should write. If I figure out how to solve a particular problem in Tableau and it took me more than a few minutes to solve, I realize that this technique is probably worth documenting in a blog post. This realization usually occurs late at night, after my work day is over. This is when I write these blog posts because it is the only time I have to do it. My wife thinks I’m crazy for writing about Tableau when everyone else is asleep.
As the months go by, I find myself having to go back and read my own work to remember how I did things, and sometimes I am pleasantly surprised by what I wrote. All along, my goal has been to help others learn to use Tableau more effectively by giving examples and stimulating creativity. What I didn’t expect was that I would help myself in the process. By taking the time to write these things, I learn more techniques and become more efficient in my work. By writing these posts, I have to be sure that I understand the material, which leads to better comprehension. Those two results have made me happy and keep me focused on continued learning and exploring the platform.
As the Tableau software develops, so do I. We are partners. I’m committed to using this tool for the duration of my career, and as far as I can tell, there is no reason for me to look elsewhere for a comparable platform. I’m now using and exploring the R-interface with Tableau. I am also beginning to use Alteryx to create more complex data sources for visual analysis in Tableau. These two items are sure to be topics in upcoming blog posts, so stay tuned if you are interested in these technologies.
What Tableau Can Do for You
Tableau is still early in its product life cycle and there are many great advances that are sure to come. If you are new to Tableau, it is not too late to get started learning this platform. If you get started now, it will be a great ride for you to see what happens over the next few years. You will be able to learn a lot and will be able to make great discoveries with your data. You can be a data pioneer for your company if you are willing to learn. Take the time to explore the online tools Tableau provides to us for learning their product. You will be glad that you did. Finally, thanks again to the great staff at Tableau software for all your hard work and accomplishments. I appreciate all that you do to improve the software for us to use.
After completing this post, another series of thoughts occurred to me. There were so many thoughts, in fact, that I just wrote part 2 of this blog post. Click here to read the next post on this topic.
If you don’t like reading, you can listen to me read this article by clicking the audio player below.
Footnote: What is VizQL? – click here for the link to this source
Although databases and visual interfaces are two of the success stories of the computer revolution, their synergy to date has been modest. Structured databases contain homogeneous data that require powerful query languages such as SQL and MDX to make the subtle distinctions that many tasks require. However, these languages are often difficult for people to use. What is needed is another layer of software that contains formatting and visualization capabilities that help people work with their structured data. We fill this gap with VizQL, a formal language for describing tables, charts, graphs, maps, time series and tables of visualizations. All these visual representations are unified into one framework, lowering the overhead of switching from one visual representation to another (e.g. from a list view to a cross-tab to a chart). Unlike current charting packages and like query languages, VizQL permits an unlimited number of picture expressions. Visualizations can thus be easily customized and controlled. VizQL is a declarative language. The desired picture is described; the low-level operations needed to retrieve the results, to perform analytical calculations, to map the results to a visual representation, and to render the image are generated automatically by the query analyzer. The query analyzer compiles VizQL expressions to SQL and MDX and thus VizQL can be used with relational databases and datacubes. The current implementation supports Hyperion Essbase, Microsoft SQL Server 2000 and 2005, Microsoft Analysis Services 2000 and 2005, MySQL and Oracle, as well as desktop data sources such as CVS and Excel files. VizQL enables a new generation of visual analysis tools that closely couple query, analysis and visualization.