Separating and Analyzing Dataįirst, let’s think of the benefits to downstream users. As an example, let’s look at the sample workflow below provided by Tableau. Using Tableau Prep, you can isolate and identify all the cleaning and preparing work the data source needs. This is where Tableau Prep is invaluable. What I needed was a way to separate the steps I took to clean and prepare the data versus those I took for analytical or visual purposes.
Understanding such a workbook would be difficult, not to mention maintaining it.įurthermore, it was difficult to then identify what changes the client should make to the underlying data source. By the time it was done, the workbook was cluttered with numerous calculated fields, special formatting, data reshaping tricks, etc. Then, the analysis they wanted required the use of many Tableau tricks and obscure features. I had just wrapped up a couple of client engagements where I spent a lot of time cleaning the data. The next two primary uses for Tableau Prep I found were highlighted by some recent client work I had done. Getting this insight from exploring directly in Tableau Desktop would take too much time, and this method of data exploration is much faster and easier than any other tool I’ve used. All this information-and much more-about the dataset is easily found in the profile pane. Moreover, there are useful features that can’t be shown in an image, such as the ability to sort fields and the highlighting of associated records when a value from one field is selected. And it appears that Order ID would be an excellent primary key (high cardinality), but there are a lot of nulls that need to be explained. Arnold has at least five different variations. I can detect that the Approver field has inconsistencies that need to be cleaned because C. I can see that it takes anywhere from zero (0) to seven (7) days for an item to ship, with four (4) days being most common.
I can tell that most orders are not returned due to the high No count in the Returned? column. But now, Tableau Prep has given me a way to perform much of this exploration.įor example, in the image below, I can see that there are only three product categories, making it an excellent candidate to slice or filter data. In the past, I used the Data Discovery feature in Power Tools for Tableau Desktop by InterWorks, Inc. Answering these questions directly in Tableau Desktop is time consuming. What is the primary key? Are there columns that are mostly null? What does the value distribution of a measure look like? So on and so forth. As a consultant, I am always being given new datasets to work with that I must quickly understand in order to begin dashboard development. The first thing I noticed was the data exploration features in the profile pane. I recently had some time between client engagements and decided to give Tableau Prep a more thorough examination. I could see how Tableau Prep would be helpful in a narrow set of circumstances, but I thought it wasn’t that impressive or useful of a tool altogether. It didn’t do enough data science to replace Alteryx or Dataiku, and it didn’t do enough data reshaping and iteration to fully replace SQL queries/views or any other scripting language. Furthermore, it didn’t seem to do enough other things to replace any of the tools I was already using. It mostly seemed to do what Tableau Desktop was already capable of doing. To be honest, I was a little underwhelmed after my first look at the tool and reading up on some reviews. At that time, though, I briefly left the Tableau world to do some non-profit work, but when I returned a year later, I was excited to learn what Tableau Prep had become. When Tableau initially introduced Tableau Prep, I was hopeful that maybe it would replace the need for so many other tools.
This means that sometimes I view a new tool as more of a burden than something with which to get actual work done. I only care about whether the tool helps users do their job.
Moreover, I come from a business background instead of a tech one, and as a result, I could not care less about how “cool” a new tool is. It enables multi-step data preparation and calculations.Īs an analytics consultant, it is exhausting to keep up with the endless stream of new tools and technologies that are flooding the analytics market.It acts as a log of recommended changes for the data source.It neatly separates data cleaning from data analysis.It creates an easy way to explore what your data looks like.However, it may be tl dr for some of you so here are the highlights. The rest of this article will tell the story of my Tableau Prep examination and go into further detail of my findings. Tableau Prep has been out for a while, but I had not yet given it a hard look until recently. I don’t blog often, but when I do, I blog about something awesome.
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