Mar 302015
 

Overview

Tableau 9.0 includes a built-in data prepping tool that makes reshaping survey data so it plays nicely with Tableau a much smoother experience than using the Tableau Excel add-in.  While this new feature won’t replace by trusty copy of Alteryx (for reasons that I explain later in this post) there are many occasions where Tableau’s new pivot feature will be more than adequate.

In this post I will walk through using the new pivot feature along with “temporary” blending to create a solid framework for using survey data with Tableau.

Special thanks to Susan Baier for bringing this to my attention and Jonathan Drummey for showing me Tableau’s Create Primary Group feature.

So, what do we have here?

Note: if you want to follow along you can download the Excel file here.

Consider an Excel workbook that contains two sheets.  The first sheet has the survey results, a snippet of which is shown here.

Figure 1 -- Some raw survey data

Figure 1 — Some raw survey data

Notice the format: one row for each survey respondent and a separate column for each question in the survey where each question is identified with a Question ID (e.g.,  Q0, Q1, Q2, Q134a, etc.).

Column A contains a unique ID for each survey taker, Columns B through D contain demographic information, and Column E contains a weight for each survey respondent.

The second sheet maps each Question ID to a human-readable version of the question and groups related questions into logical buckets.

Figure 2 -- Helper file that maps each Question ID to the wording of the question from the survey

Figure 2 — Helper file that maps each Question ID to the wording of the question from the survey

Note that when I first blogged about survey stuff I didn’t use a helper file but now I won’t take on a project without creating one as I don’t want to spend time aliasing hundreds of question IDs.  The Grouping column also makes is much easier to select related questions and visualize them together.

The data wants to be tall and thin

Anyone who has read up on the subject know that life with survey data and Tableau is a lot easier when the data is reshaped so let’s see how to do this with Tableau 9.0.

  1. In Tableau, connect to the data source and the sheet that contains the data you want to reshape and visualize. This is what it looks like on my screen.

    Figure 3 -- Survey data prior to pivoting (reshaping)

    Figure 3 — Survey data prior to pivoting (reshaping)

  2. Select the fields you want to merge / pivot / reshape, in this case everything except the Resp ID, demographic fields, and Weight field.
  3. Click in any of the highlighted fields and select Pivot. Tableau will combine the 20+ fields into two fields, as shown here.

    Figure 4 -- Data after it has been pivoted

    Figure 4 — Data after it has been pivoted

  4. Rename the first field Question ID and the second field Value.

    Figure 5 – Pivoted fields renamed

    Figure 5 – Pivoted fields renamed

  5. Indicate whether you want an extract (a good idea when Excel is the data source) and go to the Tableau worksheet.
  6. Drag Question ID onto the rows shelf. Your screen should look like this.

    Figure 6 -- Reshaped data in Tableau.  Instead of 20 measures for each question we have only one measure.

    Figure 6 — Reshaped data in Tableau. Instead of 20 measures for each question we have only one measure.

Creating the temporary blend

Now we need to connect and relate the Helper file to our pivoted survey data.  We will do this with a blend, but then use a very slick feature of data blending that will allow us to ditch the secondary data source. Here are the steps.

  1. Click the Add New Data source tool.8_dataSource
  2. Connect to the Helper File sheet from the same Excel workbook and indicate whether or not you want to create an Extract (of course you do!)

    Figure 7 -- The secondary data source

    Figure 7 — The secondary data source

  3. Return to the Tableau worksheet.
  4. Drag the Grouping field to the left of Question ID on the rows shelf, and Wording to the right, as shown below.  Note that you don’t *have* to do this but it’s always useful to see if the hierarchy is working correctly.

    Figure 8 -- Blended Data

    Figure 8 — Blended Data

  5. Right-click the Grouping pill on the Rows shelf and select Create Primary Group.
  6. Rename the group Grouping as shown below.

    Figure 9 -- Leveraging the blend to create an ad-hoc group based on Question ID fields.

    Figure 9 — Leveraging the blend to create an ad-hoc group based on Question ID fields.

  7. Click OK.
  8. Right-click the Wording pill on the Rows shelf and select Create Primary Group.
  9. Rename the group Wording and click OK.
  10. Click the primary data source (the one from which we initially selected Question ID). Notice the two groups that Tableau generated for us.

    Figure 10 -- Tableau-generated groups

    Figure 10 — Tableau-generated groups

At this point we no longer need the secondary data source as the primary source now has groups that map and alias the Question IDs.  Very slick.

Seeing this in action

Now that we have the groups it’s easy for us to do some very quick analysis.  For example, let’s suppose we want to see the average Likert scale score for the collection of Likert scale questions.

  1. Create a new worksheet.
  2. Drag Grouping into the Filters shelf and select the collection of questions you want to view, in this case Likert Set 1.

    Figure 11 -- The Grouping group makes is easy to indicate which sets of related questions you want to examine.

    Figure 11 — The Grouping group makes is easy to indicate which sets of related questions you want to examine.

  3. Drag Wording to Rows.
  4. Right-Drag Value to Columns and select AVG(Value).
  5. Sort in descending order.

Isn’t this great?  We didn’t have to go groping around for the right Question IDs and we didn’t have to alias anything.

So, are there any shortcomings?  Is this blend approach as good as being able to join the pivoted data with the helper file?

Yes, there are shortcomings

There are several things that a join will give us that we can’t get with a blend.

You cannot refer to the group in a calculated field

You can’t refer to a group in a calculated field, so something like this won’t be available:

IF [Grouping] =”Things you Measure” then [Value] END

You need to update the group members if you add new questions

Tableau’s generation of the primary group is much like populating the members of a parameter with the members of a field.  Tableau will do it when you click a button, as it were, but it won’t update the list automatically.

If you end up adding new questions to your survey or reorganizing how questions are categorized in your helper file will either need to regenerate the primary data source groups or manually edit them.

You cannot combine text results with numeric results

This is one of the major “gotchas” for me, at least for larger surveys.  With most commercial survey systems you can download the data in a label format or a numeric format.  For example, when downloaded as labels survey responses might look like this:

Strongly disagree
Disagree
Neutral
Agree
Strongly agree

When downloaded as numbers the same responses would look like this:

1
2
3
4
5

I find I like to have both label and numeric responses, so I pivot / reshape both sets of data and then join them together using Question ID and Response ID.  Using Alteryx I can perform the join but I cannot do it with Tableau 9 and pivoted data.

Conclusion

For complex surveys where I need to do a fair amount of data cleanup and need both next and numeric values I’ll continue to use Alteryx.  For shorter surveys where I don’t need to do a lot of prep work and where either labels or numeric values will suffice, Tableau 9.0’s new pivot feature suits me just fine.  It’s a great addition to a great product.

 Posted by on March 30, 2015 2) Visualizing Survey Data, Blog Tagged with: , , ,  28 Responses »
Mar 112015
 

Overview

Note: I based my Tableau Conference 2015 presentation on this blog post. You can download a PDF of the presentation and the Tableau packaged workbook.  Click here to see a video of the presentation.

Earlier this year one of my clients was updating a collection of survey data dashboards and they wanted to revisit the way they presented demographic data.  They thought that the collection of bar charts comprising the demographics dashboard was boring and wanted to replace them with something that was a bit more visually arresting.  In particular they wanted to take something that looked like this this…

Figure 1 -- a "boring" collection of bar charts.

Figure 1 — a “boring” collection of bar charts.

… and replace it with something that looks like this:

Figure 2 -- A "flashy" demographics dashboard

Figure 2 — A “flashy” demographics dashboard

When asked why they wanted something “flashier” they indicated a desire to draw the viewer into the dashboard and they thought a dashboard with more than just bar charts would do the trick.

I wondered “why stop there?”  Why not add pictures of kittens and puppies?

Figure 2a -- the Too Cute dashboard.

Figure 2a — the Too Cute dashboard.

The real issue here is that the underlying data just isn’t interesting and adding sexy visual elements will do nothing to make the data more interesting.  There’s only one way I know to make this kind of data “interesting”.

Make it personal.

Tapestry and Chad Skelton

I recently attended the 2015 Tapestry Conference where Chad Skelton of the Vancouver Sun presented a great session making the case that people are ravenous for data about themselves.

I was particularly taken with an interactive dashboard Chad created that allows Canadians to see how much older / younger they were than other Canadians.

I decided I would look at United States census data and build a similar dashboard.

US Census Data without Personalization

Here’s a histogram showing the relationship between age and US population.

Figure 3 -- A histogram showing the relationship between age and US population.

Figure 3 — A histogram showing the relationship between age and US population.

I have to admit this doesn’t do much for me although I do find the long downward slope from around the age of 50 somewhat interesting (but I am a bit of a data geek).

Contrast this general purpose graphic with the personalized dashboard shown below.

Did you try it?  Are you over 38 years old?  If “yes,” were you a bit depressed?

I certainly was.

While I don’t mean to depress anyone I do want to underscore how much more interesting the data is when the data is about YOU.

Make the Demographics Dashboard Interesting – Make it Personal

With the goal of personalization in mind let’s see how we can make the dashboard in Figure 1 more interesting.

Let’s start by gathering some information about the person viewing the dashboard; that is, let’s present some parameters from which the viewer can apply personalized settings:

Figure 4 -- Get your user to tell you something about himself / herself.

Figure 4 — Get your user to tell you something about himself / herself.

Now we can take these parameter settings and highlight them in the dashboard.

Figure 5 -- A "personalized" demographic dashboard.

Figure 5 — A “personalized” demographic dashboard.

We can then go one step further and invite the viewer to select the colored bars to see exactly how many people that took the survey have the same demographic background as the person interacting with the dashboard.

6_boring

Figure 6 — There are 65 people who fall into the same demographic pool as the person viewing the dashboard.

Conclusion

I’ve become a big advocate for adding personalization to dashboards and a number of my clients have started to adopt the approach.  I’ve seen some very good results at Bersin by Deloitte where Bersin is leveraging their proprietary survey data by allowing individual organizations to benchmark their numbers against similar organizations.

Note: A few months ago Joe Mako sent me a link to a Stephen Few blog post.  In researching this topic I revisited the post and see that Chad Skelton was in fact featured in Few’s essay . It seems that Skelton did not just “happen” upon the idea of personalization but was grappling, like so many of us, with ways to entice people to engage with visualizations.

For the record, I think personalized bar charts beat packed bubbles any day of the week.