• Understand the many options for connecting to data.
• Understand the Tableau interface / paradigm – components, shelves, data elements, and terminology. The student will be able to use this knowledge to effectively create the most powerful visualizations.
• Create basic calculations including string manipulation, basic arithmetic calculations, custom aggregations and ratios, date math, logic statements and quick table calculations.
• Able to represent your data using the following visualization types:

• Use Trend Lines, Reference Lines and statistical techniques to describe your data.
• Understanding how to use group, bin, hierarchy, sort, set and filter options effectively.
• Work with the many formatting options to fine tune the presentation of your visualizations.
• Understand how and when to Use Measure Name and Measure Value.
• Understand how to deal with data changes in your data source such as field addition, deletion or name change.
• Understand all of your options for sharing your visualizations with others.
• Combine your visualizations into Interactive Dashboards and publish them to the web.

Course Includes: This course will include extensive hands-on activities to re-enforce the skills and knowledge attained.Click here to go and book the Course! Tableau Fundamentals 2-Day Class

1. You get to meet the data ninjas behind the scenes

What better reason to attend than to get to meet those responsible for Tableau? Not only do Christian Chabot (CEO & co-founder) and Chris Stolte (CDO & co-founder) each give a keynote at every conference they also make themselves available throughout the entire week. Whether you’re at breakfast or wandering the halls during a break between sessions, you’ll be hard pressed not to find them fully engaged with a customer, learning about their data story and how they can improve Tableau to further help make a difference in their day to day lives.

In addition to Tableau’s leadership team, you’ll find plenty developers and data analysts each there to answer any questions you may have no matter how simple or complex.

2. Learn what others are doing throughout the world, across various industries

It’s easy to get lost in the details of the day to day. At times, it’s refreshing to take a step back and see what others like yourself are doing with their data and Tableau. Each day is broken out into multiple sessions that fill the gaps between an early morning and afternoon keynote from industry superstars. Some sessions are centered around industry best practices while others are customer stories presented by the customers themselves. This year InterWorks had the pleasure of sponsoring one of our European customers – Europol – providing them the opportunity to share how they leverage Tableau to help fight crime across Europe.

3. Learn from industry professionals

Alongside customer stories are sessions focusing on industry best practices delivered by Tableau developers and partners from around the world. You can learn more about fast storage through partners like Teradata and Vertica, the secrets behind Tableau Server optimization from the professionals who help shape the product and even how to optimize your dashboards for better performance using Tableau’s generated log files.

4. Find out what’s coming next

And finally, my favorite – finding out what’s coming next. True to last year’s form, Tableau announced a handful of items to look out for in the coming months, some of which include:

• Native Android application
• Direct connection to SAP BW
• Support for Spanish & Portuguese
• Support for multiple customized views
• Even easier sharing in Public

The opportunity to see new functionality demonstrated by the professionals who helped shape the product has always been one of my favorite moments of any customer conference, and Tableau’s dedication to constantly improving their product once again shines through. For those of you unfamiliar with Tableau’s release cycle, each year typically produces both a major version release along with a minor version release. The European conference typically highlights features coming in the next minor release while the US conference showcases the major release. That’s right, an Android application is Tableau’s idea of a minor release. This is on the heels of unveiling the iPad application during last year’s conference.

Above all else, each conference gives you the ability to connect with your peers and build relationships with those you may only get to interact with digitally otherwise. So whether you’re a newcomer or a conference veteran, stop us when you see us roaming the halls. We’d love to hear your data story. Your next opportunity is right around the corner.

A special thanks to Giedre Aleknonyte (@googlea) for capturing and sharing the event photos used in this entry. More photos can be found on her Flickr site.

First: Open Tableau and connect to Superstore Sales – Excel. 

Second: Double Click on Measure Names in your Data Dimensions window. This will invoke Measure Names to the Columns shelf, and Measure Values to the Text Shelf creating the following view:

To get the crosstab to look like you were expecting, click Swap in your Menu Bar. 

You now have a Crosstab of your data: 

Tableau has defaulted the aggregates in your view when you invoked Measure Names and Measure Values.

You can view these defaults in the Measure Values pane under your Marks Card. 

To Adjust the calculations, Right Click on the Measure Value and change the aggregate. (i.e Avg(Discount) to Max or Min, etc…)

You may also want to exclude some values from your crosstab.

To accomplish this, use the filter pane.  Drag Measure Names to the Filter shelf and drop. Now right click on the pill in the Fitler shelf, and select Filter

A dialog will appear allowing you to exclude the fields you do not want included in your crosstab. 

Previous versions of the Tableau Data Reshaper tool could cause multiple Excel windows to open even though a single file had been opened. Version 7 of the reshaper tool seems to have fixed this issue.

Head over to Tableau’s knowledge base article on installing the add-in and download the latest version. The attachment will still be named “tableau_data_tool.zip” but once the setup window opens you’ll see it’s version 7.0.

Out of the box, Tableau is installed with an online map connection to a set of custom maps provided to Tableau by a third party. These maps are not from a Web Map Service (WMS), although they closely resemble WMS maps.

However, Tableau also supports connections to any Web Map Service that supports the WMS 1.1.1 standard. You can connect Tableau to any WMS server, and use its maps for your visualization.  Remember though,  online maps require you to have an internet connection, and your Tableau performance speed is dependent on the reliability and speed of the WMS server.

Follow the example below to connect to a WMS server:

Step 1: You have a view built that you would like to use a WMS for the Background map. The map below is the default from Tableau. 

Step 2: To connect to a WMS go to your toolbar Map > Background Maps > WMS Servers 

       A dialogue for WMS will appear. 

Step 3: Click the Add button and paste your WMS URL

Step 4: Once Tableau connects to your WMS, your maps are now available, but you must tell Tableau to use them. Go back to Map > Background Maps > Select your WMS Server Name

Step 5: Your Map Options Card will now give you the options for your WMS Connection. Choose the Map options you would like for your view

The steps below will show how to add a reference line, a reference band, and a reference distribution in Tableau:

Open Tableau and Connect to the Sample – Superstore Sales (Excel)

Drag the Profit Ratio to the Column Shelf and the Customer Segment the row shelf. Select Bar in the Marks Shelf and you should see the graph below:

1. A.      To Add a Reference Line
   1. Right Click on the Profit Ration Axis and select Add Reference Line
   2. Your Add Reference Dialog will appear.
   3. The first option to decide on your dialog is Scope. Do you want Entire Table, Per Pane , Per Cell. For more information on Scope see post for Understanding Scope when Adding Reference Lines in Tableau. For this example we will use Per Pane as we only have one pane/table in this example. 
   4. For the Reference Line Option: Here you can select to use Avg, Constant, Max, Median, Etc. For the Value, Type in the desired value for constant, or select the field you want to query on.
   5. Label can be left blank by selecting None, or Use the label of your choice from the drop down to have it appear in your view.
   6. Formatting is also up to you. You can select solid or dotted lines and your choice of color. 
   7. You can also tell Tableau to Fill Above or below.
   8. The options I have chosen are illustrated in the picture below.  

Your View would then look like this. I now have a Reference Line at 10% for my axis Profit Ratio. 

1. A.      To Add a Reference Band
   1. Right click and duplicate the sheet you are working on.  
   2. You can remove the reference line you just created by right clicking the Profit Ratio Axis and selecting Remove Reference Line.
   3. I will not be removing the line, because I will add a band to enhance the line. The band I will add will be from 9% to 10% with 10 being the target shown by the reference line before.  
   4. To add the band right click on the Profit ration axis and select Add Reference line.  
   5. Click the Band option at the top of the dialog.  
   6. All option for Reference Band are now available to you.  The selections I have made are shown below. 
   7. You will select Band From and Band To. 

You will then have a Band from 9% to 10% on your view with the 10% Target Line we have already created. See below:

1. A.      To Add a Reference Distribution             
   1. Right Click on the Profit Ratio Axis and Remove the two Reference lines that you have created.  
   2. Right Click again and select Add Reference Line
   3. Select Distribution at the top 
   4. Select your computation options (i.e. Confidence Interval, Percentiles, Percentages, etc.) 
   5. Select your field to compute the computation on. Here we are using AGG(Profit Ratio) with Percentages of 30, 60, and 90. 
   6. Select your color options. You can fill above and below, Symmetric or Reverse. You can then select colors for the fill based on your options. 

You then will have added a Reference Distribution to you view. 

Common uses for Parameters are What-If Analysis and User Input Analysis. 

Four Key Things That Must Be In Place For Parameters:

1. Create the Parameter

2. Use the Parmeter in a Calculation

3. Show the Parameter Control for the User

4. Use the Calculated Field in Your Visualization

Follow the step by step instructions below:

Let’s get started by opening Tableau and connecting to the Superstore Sample Excel Data Set.

1. Drag Order Date to the Columns Shelf and Sales to the Rows Shelf.

2. Right Click the Year Pill on the Column Shelf

3. Go to More > then Custom 

4. When the Custom Date box appears, Click the Month/Year option in the dropdown. 

You then will have a graph that looks like this:

Step 1: Creating the Parameter

The scenario we want to create is a what-if scenario. If Sales were 3% more for example. To create a parameter, do the following;

1. Go to Analysis Menu and select Create Calculated Field.
   1. You can also right click within the Measures pane and select Create Calculated Field from there as well
2. Before we create the calculated field that will use our parameter, we have to create our parameter. So on the lower half of the dialog window there is a section titled Parameter and a link next to it that says Create. Click Create.

3. Fill in the dialog window exactly as above.  0 to 100 Step size of 2 on Data Type Integer. Name your Parameter IF_Sales Param

4. Click OK. Your Parameter now shows up in your Parameter box. 

Step 2: Using the Parameter in a Calculation:

For our scenario, we now want to use the parameter to create a calculated field  to add to our graph  see its effect on our data. While still in the Calculated Field dialog window, create your calculated field:

1. Name your Caluclated Field IF_Sales(Calc)

2. Formula: [Sales] * (([IF_Sales Param]/100)+1)

3. Click OK

** Notice that in the calculation the parameter we created is going to interact with the Sales Measure. 

Step 3: Show Parameter Control

Now back on the Tableau main view, you will see your Calculated field in the Measures pane, and the parameter in the Parameters pane of your Data Window. Right

1. Click on the Parameter IF_Sales Param

2. Select Show Parameter Control

3. Your Parameter Control Filter shows at the top right of your view

Step 4: Use Calculated Field in Your Visualization:

1. Drag the Calculated Field IF_Sales(Calc) and drop it on top of your Sales Axis. You will see a transparent equal sign as you hover.  After dropping you will notice Measure Names has now been added to your color shelf, and a Color legend is showing both Sales and IF_Sales.

2. Because your Parameter control is at 0, your lines are on top of each other.

3. Click on your Parameter control, and you will see the two lines appear. 

Is the secret in the shrink-wrap?

Not so fast, say the smart business people who still trust their own brains and those of the people they hire. Something else comes first — and it’s messy. Not just in the sweat and spit that may fly, or the fingerprints all over hard copies of sensitive data that someone always leaves in the lunchroom.

The messy part is the sheer inefficiency of it all — the false starts that come before discovery, the requests for more data from analysts suddenly shot through with determination, and the ensuing clamor about what it all means to the business. It all takes time, tends to drift and snag on tangents, bogs down in thoughts of cutting losses — until the big insights finally break through.

The good news is that some tools really can make it all work better by satisfying their natural drive to talk about problems and to solve them. In the last few years, some makers of business intelligence tools have begun showing they care about two crucial aspects of decision making: interactive data and collaboration.

Three key features give data exploration and collaboration a real boost.

Visualized data that keeps up with thought. Anyone who’s ever pleaded for information from an Italian postal clerk knows how some BI tools make you feel. You have a simple question, and there the answer man sits, behind bulletproof plastic, meting out information in drips.

People who enjoy thinking do it best at full speed. Good tools — Tableau being the best — keep pace. Unlike the little clerk behind the window, these tools give it to the user as fast as the user asks for it. People stay interested.

Data that goes straight to the brain. Analyzing data displayed in rows and columns might be all right for accountants, old-school data analysts, and monks under candlelight. But that method was a make-do method left over from the command-line era. Modern visualization, pioneered by film animators at Pixar (“Toy Story,” etc.) who went on to launch Tableau, goes direct. Users recognize and respond much more quickly.

Besides, the average business analyst prefers visualized data, and would rather head down to Starbuck’s again than endure ugly data. Let’s face it, we like to look at pretty things, and when we find one, we engage. We think about it, and we talk about it.

Tools that go where you steer them, and let you back out, too. Traditional BI tools seem to have a mind of their own. Using them, the analyst is as freewheeling as driving a cement truck. You may steer, but you feel like something else is steering against you. Good tools and steer like a Porsche: Go this way, go that way, and maybe go back to try it again.

Tools that work just get out of the way. The best BI tools are as easy to use as telephones, as conducive to interaction as beer, as nice to look at as a first date. That explains why so many analysts prefer Tableau. Let’s face it, we’re human. We’re social.

And yes, that was a trick question about “the right tool.” We are the right tools ourselves, and the software tools that reflect our nature really can help fact-based decision-making work.

Thanks to Google’s Analytics Core Reporting API, you can get access to that data directly.  I found a great program put out by Keplar, called “Google-Analytics-export-to-CSV” which does exactly that, (you can download  it here).  It does require Java Runtime Environment, so make sure you have it installed before you try to use it.

The program comes zipped together, so extract the files to the directory of your choice.  Once in that directory, you’ll notice that it comes with a readme as well as a PDF file explaining how to use the program (which I thought was rather well done)  Also, you’ll see two folders:  recipes and output.  Recipes contains .xml files that lay down all the necessary information for the data pull-down (login information and the metrics/dimensions you want)  These are predefined templates designed by Keplar; you can adjust them to suit your own needs. Or you can use the file called “query template” which leaves all the fields blank.  Here’s the visitors-by-page.xml file (open this in the text editor of your choice):

<?xml version=”1.0″ encoding=”UTF-8″?>

<orderly xmlns:xlink=”http://www.w3.org/1999/xlink”>

<configuration>

    <query>

        <tableId>{ENTER YOUR TABLE ID HERE}</tableId>

        <dimensions>ga:pagePath</dimensions>

        <metrics>ga:visits,ga:visitors,ga:pageviews</metrics>

        <segments></segments>

        <filters></filters>

        <sort>-ga:visitors</sort>

        <start-date>2011-10-01</start-date>

        <end-date>2012-01-10</end-date>

        <start-index></start-index>

        <max-results></max-results>

    </query>

    <login>

        <userName>{ENTER YOUR GOOGLE ANALYTICS USERNAME HERE}</userName>

        <password>{ENTER YOUR GOOGLE ANALYTICS PASSWORD HERE}</password>

    </login>

    <app>

        <appName>google-analytics-export-to-csv</appName>

        <url>https://www.google.com/analytics/feeds/data</url>

    </app>

</configuration>

</orderly>

If you read through each of these tags, you’ll see some that stand out:

-tableId:  this is the Google Analytics number given to each of the sites that you have tied to your account.  The best way to obtain that information is to use Google’s Data Feed Query Explorer.  The first field, ids, is where you’ll find what goes in this field.

Pick whichever website you want to analyze and type in that code between those tags.

    dimensions:  these are the qualities by which you’ll filter your data

    metrics:  these are your measures with which you can perform calculations

    You can see a list of all dimensions and metrics GA has to offer here.

    start-date and end-date:  naturally, these are the boundaries over which you’ll pull your data.  Note that both are in year-month-day (YMD) format. 

    userName: put in the account name associated with your GA account

    password: naturally, your password for your GA account

When you’ve put in all the fields, you are ready to run the program itself.  The actual java program is the file called ga2csv.0.1.jar (.jar stands for JAVA archive) The program is designed to run on a command line, so we’ll be using PowerShell for that.  You can access PowerShell by typing powershell.exe in the run window (for a great PowerShell cmd line primer, check this site out)

Navigate to the directory where you downloaded the program.  It might resemble something like this:

Now, to actually run the JAVA program, you will enter a command that fits this form:

    java -jar ga2csv.0.1.jar [yourqueryname.xml] [youroutputname.csv]

Obviously, you’ll replace yourqueryname.xml with the actual name of your query; the same goes for youroutputname.csv.  You can also put in a location relative to the program’s directory as well.  Here’s a more specific example:

    java -jar ga2csv.0.1.jar recipes/visitors-by-page.xml output/visitors-by-page-output.csv

Assuming you’ve configured visitors-by-page.xml correctly, this will pull all the data down into a .csv located in the output directory.  Now you have a .csv that you can pull into Tableau and do some serious data analysis.

***Two important caveats***

1)  If you rerun this, you’ll have to delete the preexisting .csv or pick a new name because the program cannot overwrite the preexisting file. 

2)  You may find yourself with a screen that looks not unlike this:

This is due to a heap error; the program is trying to pull so much data down that it doesn’t have the memory to handle it and crashes based on its current configuration.  To get around this problem, increase the max heap size by altering the command to look something like this:

    java -Xmx1g -jar ga2csv.0.1.jar recipes/visitors-by-page.xml output/visitors-by-page-output.csv

That extra parameter, -Xmx1g, tells the program to use 1 GB of RAM to get the job done, as opposed to its default 128 MB.  Another way you can encourage faster performance is also run the program multiple times over smaller time intervals.  I had to do that because I was pulling ~20k rows for each month.  Running as a single pull overwhelmed the program, even when I bumped up to 1 GB max heap.

This is a great tool for Google Analytics users, and Keplar is awesome for making the tool publicly available.  Data nerds and web marketing analysts: rejoice.  You can bring GA data into Tableau now.  My heart is smiling; how about yours?



If I try to chart this data to get a trend over time I will see I have a problem …

The values for sensor ‘a’ are skewing the scale of the readings for sensor ‘b’, thus flattening the trend for sensor ‘b’ all the way to the 0 line.  As Mike pointed out, there is a simple fix here.  In this case, the readings for sensor ‘a’ are (on average) 100x the readings for sensor ‘b’.  The following calculated field will level the playing field for these two sensors and give me the opportunity to see if there is any correlation between the sensor readings across time.

Now when I use the new calculated field in my chart I get a much clearer picture of the relationship between these two sensors over time.

Note:  If you use this technique, it’s important that you tell your audience that you’ve altered the scale of sensor ‘b’.  In this example I modified the label for the Y axis (see screenshot above) to tell the consumer of this report that the readings for sensor ‘b’ have been adjusted.