Archive for Data Visualization – Page 2

Storytelling with Data

I recently had the opportunity to attend a workshop called Storytelling with Data by Cole Nussbaumer Knaflic. Instead of focusing on data analytics tools, the workshop focused on using data as part of the storytelling process. We reviewed the fundamentals of data visualization and how to communicate effectively with data.
Here are five steps you can take to tell a story with your data.

Step 1. Give Your Data Context

After you have created your exploratory analysis (identifying interesting things to learn from your data), you can move to explanatory analysis (communicating these findings to someone else).
Just like in other forms of communicating, you need to think about your audience and the message you want to convey. You will need to understand the current situation.
Low tech storyboarding can be used to plan out your communication plan. The risk and impact are lower at the beginning if you need to modify your plan to ensure that it is clear.

Step 2. Choose the Right Visual Display

Choosing the most effective chart type for your data is important. There are no hard and fast rules but there are definitely guidelines.
We’ve discussed choosing the correct chart type before, so this part of the workshop served as a reminder to be deliberate in choosing the correct chart type for your data. Here’re some blogs we’ve written on this topic before: Powerful Visualization Choices (Part 1), Part 2, Part 3, Part 4.

Step 3. Declutter Your Viz

This is a step that it may be easy to skip, but it is one that will take your chart from drab to fab!
Take a hard look at the visual you created. You want to ensure that each element on the chart has a value. Think about chart borders, gridlines, data markers, axis labels and legends. Each element on your chart adds to the cognitive load for your audience. We want to make it as easy as possible for them to concentrate on the data story you are trying to tell, not spending energy trying to decipher all the visual cues you are throwing at them.
Here, Cole explained that we should consider the Gestalt Principles of Visual Perception which explains how people interact with and create order of out visual stimuli.
It was very interesting to see how the principles aligned with my experiences. Cole’s book explains this better than anything I could find elsewhere, so I guess this is my plug to buy the book!

Step 4. Focus Attention on the Key Parts of the Story

All good stories have the main characters and a central plot.
Once you have chosen the correct chart type and reduced clutter, keep thinking of the audience and the story you are trying to tell.
Here we learned about preattentive attributes (size, color, and position) and how to use them to focus attention. Our brain is super quick at picking up on preattentive attributes and we want to use that to our advantage.
These attributes should be used sparingly and purposefully. For example, don’t use color just to make it colorful. Use color to direct attention to what you want the audience to focus on, such as lower than acceptable KPIs or better than expected renewals in a member segment.
One simple exercise you can do is to ask a coworker or friend to look at your chart ask them where their eyes go first and where it is drawn. This can help you confirm that the chart you’ve designed is drawing attention in the way you expected.

Step 5. Tell Your Story

Once you have your beautiful charts, you need to think about how you will convey this information to your audience. The advantage of a story is that it sticks in our memory and can be retold. This is important as we seek to persuade others to see our point of view. You also want to use texts, labels, and action titles within your presentation. Just like your favorite movie or children’s book, think about how conflict and tension can be used when telling your story with data.
One technique I really liked was building the visual as you talked. For example, during an in-person presentation, have a slide that only shows the title and axis, then builds in the legend or categories. This allows you to explain your chart without having people focusing on the data yet (tension and suspense!) and then hit them with the data. You can even build the data in by showing past history first (context) and then showing current information.
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What Would Your Members Write in a Postcard to You?

Dear Data

Have you ever heard of a book titled Dear Data? I first learned about it from an article in Wired magazine.  The book is not a story, but rather a collection of postcards written between two information designers who communicated in data.
Giorgia Lupi, a resident of Brooklyn whose native tongue is Italian, and Stefanie Posavec, a resident of London who speaks English, first met at a design conference in 2014. At that time, they wondered if they could get to know each other through data without speaking the same language. They proceeded to find out by mailing each other postcards for 52 straight weeks expressed as visualizations.
Each week was a new theme extracted from daily life, such as sleep habits, spending habits, checks of yourself in the mirror, and number of times saying thank you. The book compiles all 104 postcards and positions them side by side with an explanation of each.
You can preview several pages of the book here, and it is absolutely fascinating to see how the same topic can be communicated differently, the way everyday life turns into a visualization, and the detailed artwork on these postcards.
The complexity and mundanity of everyday life jumps off the pages and the legends for interpreting each could definitely get you thinking creatively about new ways to visualize your own data – or life.

Counting Something Means It Matters

The best summary of the book is a quote from Lupi, “counting something means it matters.” Think of all the things you could count about your members. What could you discover about how your members engage with your association?
Let’s take it one step further. If your members sent you a postcard every week for one year with a data visualization about their lives, what would the postcards reveal about them, their interests, and their relationship to your organization?
Would your association play a prominent role in the narrative? Would you discover untapped areas of their lives where your organization could add value?

Data Analytics

It is unrealistic to ask your members to send weekly postcards, but you can use data analytics to visualize and understand your members and their journeys.
Pulling from data sources like your website, your AMS, and social media, you can paint a complete picture of your members. When we understand our members and customers, we can guide them along a more personalized journey.
Whether through postcards or data analytics, you can get to know somebody through data and that insight can help you better serve your members.

A Detailed Explanation of Level of Detail Calculations

Level of detail expressions (which are sometimes also referred to as “LOD Expressions” or “LOD Calculations”) are an advanced analytics feature in Tableau. LOD Expressions are useful for cohort analysis or looking at averages or totals across segements.
When Tableau first released Level of Detail calculations, we provided an intro into Fixed Level of Detail calculations. Now that Tableau has upgraded to version 10, I wanted to spend time going into the other elements of LOD calculations and some updates that occurred with the upgrade.

Structure of a LOD Calculation

There are three types of LOD calculations:

  1. Fixed;
  2. Include;
  3. Exclude.

Regardless of which type you do; the syntax stays the same. Also, new in Tableau 10, you can use expressions on dimensions within the calculation. For example, previously if you wanted to use the year from a date field, you would have to create a separate calculated field in order to determine the year and then reference that calculated field within your LOD expression.
{(Fixed/Include/Exclude) <Dimension 1>, <Dimension 2>: <aggregation of measure>}
{FIXED [State], YEAR([Event Date]): COUNTD([Registration Key])}


With a Fixed LOD, you specify the dimensions and the aggregation computes for those specific dimensions, regardless if these dimensions are on your visualization. If we use the formula above as an example, the distinct count of registrations will always happen per state, per year, even if you do not have state or event date on the visualization.


With an Include LOD, you specify dimensions, in addition to the dimensions in the view, to aggregate your measures against.
{INCLUDE year([Event Date]),[State Province Code]: AVG([Amount])}
If you do an average of amount, it will calculate the average amount per topic name. The LOD calculation does an average per year, state, and topic name.


With an Exclude LOD, you specify what dimensions including in the view, to exclude when you aggregate your measures.
{EXCLUDE [Topic Name]: AVG([Amount])}
In this example, the LOD calculation, will average per state and year and ignore the topic name.
Level of Detail calculations are very powerful tools to use within Tableau. It is easy to get stuck in one way of doing your analysis and visualizations. I hope this helps you think of new ways to attack your analysis.

How to Determine What Data to Combine

There is a lot of value in combining data from one business area with data from another business area. Similar to a jigsaw puzzle, when we combine data sets and put the pieces together, we get a complete picture of customers, events, and activities. But how do you know what data to combine?

Take Inventory of What You Have

To get started, take inventory of the data you already have available in your business area. Let’s take members for example. Membership teams often require a high level of granularity. They also have years of membership data that can be leveraged. The data they have may be stored in their Association Management System, Customer Relationship Management system, and their Financial Management System.
After identifying the data sources, consider what data is stored in each data source. Identify the file type and how you extract or integrate the data with other systems.

Consider What’s Missing

To determine what data could augment your existing data source, think about the aspects of the customer or activity that you care about.
What information could help answer your business question? If you don’t have a business question, what information would provide additional insight on customer behavior?
The membership team typically has data on when a member joined, membership type, length of membership, contact information, and dues payments. What other information would help them understand members? It may be helpful to combine membership data with components from other areas such as the number of events attended in the past two years, the last meeting attended, age, member status, tenure in the industry, and total spending in the past year.

Combine and Analyze

Combine the data and analyze it. Look for trends ad relationships. Distill down the information so that each component of activity that is of interest is presented as attributes of that person.
The table below shows some combined information as it relates to the Top 10 and Bottom 10 thread topics from an association’s online community. Using the information, we can see what a correlation may exist between a person’s attributes and the most active threads. From the data below, it looks like younger individuals with less membership tenure and professional development are replying and posting to threads generated by younger authors than the bottom threads. Perhaps action can be taken to target the younger members with messaging encouraging them and providing the benefits of authoring and responding to community posts.
Once you combine data, you can determine if there is actually a relationship between two data sets. You can also see if you need additional data to augment your analysis. Using business intelligence tools, like Tableau, allows you to easily connect data sets and experiment.

Everything You Need to Know About Tableau 10

This week, Tableau released Tableau 10 – the latest version of their business intelligence software. Here’s everything you need to know about Tableau 10.

Web Authoring in Tableau 10Our Favorite New Features

There are a lot of new features with Tableau 10, but here are a few of our favorites. Read our blog post on our top 5 favorite features.

  • Workbook Formatting – you can now set this on the workbook level. Now each new worksheet and dashboard will have the styling you want.
  • New Color Palettes – More modern colors that are more friendly for those with color vision deficiencies.
  • Clustering – Automatically groups together similar data points
  • Cross-database joins – Join data from different data sources, like SQL Server and Oracle. Publish the integrated data source to Tableau Online or Tableau Server to collaborate with others.
  • Mobile – Tableau 10 is mobile friendly and it’s easy to set default layouts for different devices
  • Revision History – The new revision history feature allows you to restore or download previous versions of workbooks.
  • Web Authoring – Tableau 10 includes a new feature that lets you ask additional questions of your data right on the web

Tableau Upgrade Tips

Upgrade Tableau Server

If you are using Tableau Server, here are a few tips to make sure your upgrade to Tableau 10 goes smoothly.

  1. Always do a backup first
  2. Always check the software product key first. It should not be expired. The key is not required to re-install. Verify license expiration in Dynamics CRM.
  3. Start downloading the file(s) while you are running the clean up and back up. Server is over 1GB and takes a while.
  4. It is required to uninstall the prior version. Configuration settings and content are preserved.
  5. Plan 2-3 hours for a major update.
  6. Using a maintenance script to do a clean and backup in advance will make the process go faster. It is advised to do an additional tsbak before starting.
  7. When upgrading an Active Directory integrated server, the service account password is not required.
  8. Update shortcuts and scripts after the upgrade to point to the new location of tabadmin.

Upgrade Tableau Desktop

  1. Do not uninstall the prior version first. A minor revision (9 to 9.1) will replace a major revision (8 to 9) will install side by side.
  2. Plan 30 minutes for the upgrade.

The Power of Combining Data from Multiple Sources

Super Charge Your Data

Combining or blending data happens when you connect two or more different data sources. Combining sources from multiple data sources reminds me of one of my son’s favorite shows, Power Rangers. While each one is committed to fighting evil, each Ranger has a unique skill and weapon. When their enemy is too great to handle individually, they combine their unique powers to create a Megazord. A well designed data mart is the ultimate Megazord that can battle the evilness of fragmented information.

Message Activity Analysis

Let me tell you what I mean. Information from your marketing system can be measured and analyzed. You are probably familiar with some common marketing key performance indicators (KPIs) such as number of sent emails, delivery rate, bounce rate, open rate, etc. This is interesting in itself to analyze which messages have higher opens and clicks and which ones are below average.
You might get something that looks like this:
standard message stats

Combine Powers

What makes this information really pop is combining it with your other data sources. Combining the messaging activity data with demographics from your AMS helps you evaluate the influence that things like job level, member type, age and/or gender have on your key messaging metrics.
open rate by generation
In this example, when we look at Open Rates by generation, we can see that those in “The Greatest Generation” have dramatically lower Open Rates than the other generations. The “Baby Boomers” have the highest open rate. If we were to only look at the average open rates, we might miss this distinction. What actions could you take knowing this information? Perhaps sending an extra mailing to your older members for important communication?

How to Blend Data

Watch this advanced Tableau tutorial to learn how to blend data. For deep analysis and improved performance, we recommend investing in a data warehouses and data marts using a dimensional data model. Learn more about our approach to data blending.

Don’t Be Afraid to Ask ‘What If?’

The oft-cited Gartner image depicting an analytics maturity model shows different forms of analytics that associations can use to understand customers and make decisions with confidence. We’ve previously discussed how Predictive Analytics can provide valuable insight into your association business, but how can you move towards Prescriptive Analytics to answer ‘how can we make it happen?’ One way to get there is through “What-if” Analysis.

What is “What-if” Analysis?

“What-if” Analysis is the process of changing the scenarios or variables to see how those changes will affect the outcome. Associations might use this when they have limited data for making a decision or they’re considering launching a major new program. This type of analysis can help you make decisions with confidence.
With “What-if” Analysis, you begin with the end in mind while exploring a world of possibilities in your association’s data. It is a great way for your association to apply models developed for Predictive Analytics to move towards prescriptive analytics. “What-if Analysis” incorporates predictive and other models demonstrating data relationships and allows you to measure the potential impact of different strategies. Here are potential questions that What-if Analysis can help answer:

  • How will different levels of membership dues impact overall revenue?
  • Will changing the location of a conference increase attendance?
  • What marketing channel allocation will maximize conversion rates?

Potential Challenges of “What-if” Analysis

Implementing multiple models and making data assumptions present certain challenges, such as:

  • Data relationships might not be linear – customers eventually encounter diminishing returns as their activity increases
  • Other data relationships may emerge – increasing meeting attendance could decrease training course attendance
  • Price elasticity is not uniform at untested levels – the impact of price on customer decisions may not be easily estimated

Another key consideration is understanding when fundamental changes over time change previously discovered data relationships. Although the past is often the best predictor of the future, this is not always the case.  You can identify instances of data changing over time by consistently monitoring and exploring Descriptive Analytics based on historical data.
These challenges demonstrate why you need analysis beyond basic spreadsheet features.

Getting Started

You can perform basic “What-if” Analysis in Microsoft Excel. However, you can take your “What-If” Analysis even farther, with these tips:

  • Get a Data Visualization Tool. You will want the power of interactive data visualization using tools, such as Tableau, to rapidly adjust data inputs and understand resulting changes.
  • Validate Data. You need to continuously re-validate models and measure the effectiveness of models to ensure the ongoing effectiveness of your models. Be sure to include this when you are considering resources. Also, not all data is created equal. You can use sensitivity analysis to identify the impact of individual variables on different outcomes.
  • Encourage “What-if” Questions. “What-if” Analysis works best in an innovative culture where intellectual curiosity is encouraged. Reward staff for experimenting and questioning long-held beliefs.

You can move your association towards Prescriptive Analytics to truly have conversations with data and create the future. So, now think about your own “what-if” questions!

Data Visualizations: Super Highway from the Eye to the Brain

Did you know that approximately 70% of the body’s sense receptors reside in the eye? Of all 5 senses, vision stands out dramatically as our primary and most powerful channel of input from the world around us! Not only that, but apparently, “the eye and visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers.”* I envision a super highway from the eye to the brain. This helps explain why data visualization is so powerful.
In order to make good decisions from data, you’ll need to not only be able to see the data, but also successfully analyze it. There are experts who specialize in this area, but you may have the aptitude to successfully analyze data as well. Just as a sonographer may have years of experience and training in reviewing ultrasounds, with just a little direction, first time parents can see their baby’s image and delight in her features, especially when the sonographer helps out by positioning the wand to get the classic profile picture! What is needed is the skill to see meaningful patterns in data. This can be learned and developed with practice. It is definitely something I enjoy!
Take a look at the examples below which show the same data in a table format and in a colored bar chart. Looking at the table, does anything stand out to you? I’ll give you a minute … Maybe largest number of registrants in 2014? Anything else? What additional questions do you have?
Now, let’s take a look at the bar chart. Even without looking at labels or legends, our eye is drawn to the highest bar and the darkest color. What are those about? An analyst will be immediately curious about why the highest number of registrants and the most events are not happening in the same year. (It was as a result of a concerted effort to target market based on analytics data.) It is amazing how quickly we were able to go from looking at the data in a visual way to asking questions on the path to making informed decisions.
*Information Visualization: Perception for Design, Second Edition, Colin Ware, Morgan Kaufmann Publishers, 2004 

Tableau 9.3: Smarter Version Control and Better Use of Color

Tableau 9.3 was released last week and has several exciting new features. These are a few of my favorites.

  1. Versioning.  In previous editions, when you published a workbook or data source to Tableau Server with the same name as something that already existed, you got a message asking if you’d like to overwrite the older file. “Of course I would,” I’d always say. But there are certainly times when seeing the previous version would have been helpful. Perhaps it would have also avoided some internal strife when a co-worker saved over my amazing workbook.versioning
  2. Publish data source flow. In this version, there is a newly designed dialog box to aid in publishing your data source more quickly. The most frequently used settings and options are available on the same screen. While seemingly small, what I am excited about is the option to “Update workbook to use the published data source.” This means that you can explore your data in Tableau Desktop while connected to the data source you just published. Previously, you had to publish the data source, connect to the data source from Tableau Server, then go through a replace data source process to connect to the published data source.
  3. Excluding totals from coloring. Totals, subtotals and grand totals can be excluded from color-encoding. What does that mean? Previously, if you wanted to do shading based on a count or sum and also wanted to show totals, the darkest color was always the total. This made it easy to overlook the items with the highest totals. Leaving the totals blank, the eye is drawn to 2014 female registrants instead of the grand total.totals coloring - oldtotals coloring - new
  4. Sheet colors. I love colors and I love organization. I’ve been using colors on sheet tabs for some time in Tableau Desktop to organize sheets based on data sources, the audiences or dashboards. Now, coloring is available on Tableau Server as well.sheet colors

How Analyzing Social Media is Like Walking Across Bridges

How do your customers connect with one another? Social media mixed with a historic mathematical theory can help you find those patterns and bridge gaps.
Combining social media with other external data can help your association use a range of personal interaction and engagement to move toward a more customer-focused approach. Analyzing social networks is often done through the mathematical concepts of graph theory and network theory, showing relationships between individuals. Richard Brath and David Jonker described using these concepts for business in their book.
Using technical analysis helps you identify people who are “connectors” and link to several groups, “influencers” who help groups form, and cliques that would otherwise be difficult to detect. Setting people up as a simple shape and line graph can be understood by:

  • Counting incoming and outgoing links between people.
  • Looking at the density of direct connections.
  • Examining the shortest and longest paths between people.
  • Considering how far the shapes are from one another.
  • Seeing how people tend to cluster together.

Graph analysis comes just from activities and does not make use of other attributes, like demographics. You can supplement this analysis with other data that you have. You can also use this kind of analysis to identify customers similar to the ones you found through social network analysis. It may be interesting to study things like how members’ interaction levels differ from those of non-members.
To bring social media data together with your other customer information, it should all come together in a data mart. In addition, you can also introduce text analytics to provide additional context. Different social media platforms make data available through application programming interfaces (APIs), which all have their own technical integration options and data scopes.
While social media analysis has been getting more popular recently, the graph theory that is used to break it down is centuries old. Mathematician Leonhard Euler famously used this theory in the 16th century to solve the “Seven Bridges of Königsberg” problem, devising a way to walk through a city while crossing each of its seven bridges only once.
While crossing physical bridges may not be on your list of priorities, social network and graph analysis can help cross several metaphoric bridges in your association, including:

  • Segmenting customers: Coming up with similar people, based on links and attributes.
  • Analyzing influence: Finding people with large numbers of connections and activity.
  • Analyzing the market basket: Figuring out items that are commonly purchased.
  • Finding general correlations: Seeing relationships between people, products, events and other things.
  • Website visit analysis: Determine which webpages are the most popular.

Visualization techniques can communicate the message in social network data through node size, node color, link weight, link colors, and labels. You can use a combination of visualization choices in Tableau to tell social network stories.