It has been a while since I have discussed some of the latest creative thoughts on data visualization from Stephen Few. I have read all of Steve’s books, attended several classes from him, and religiously follow his blog and newsletter on his website, Perceptual Edge.
For those of you who don’t know, Stephen Few is the Founder & Principal of Perceptual Edge. Perceptual Edge, founded in 2003, is a consultancy that was established to help organizations learn to design simple information displays for effective analysis and communication.
Steve has stated that his company will probably always be a company of one or two people, which is the perfect size for him. With 25 years of experience as an innovator, consultant, and educator in the fields of business intelligence and information design, he is now considered the leading expert in data visualization for data sense-making and communication.
Steve writes a quarterly Visual Business Intelligence Newsletter, speaks and teaches internationally, and provides design consulting. In 2004, he wrote the first comprehensive and practical guide to business graphics entitled Show Me the Numbers, now in its second edition. In 2006, he wrote the first and only guide to the visual design of dashboards, entitled Information Dashboard Design, also now in its second edition. In 2009, he wrote the first introduction for non-statisticians to visual data analysis, entitled Now You See It.
Here is his latest thoughts from his newsletter.
Why Do We Visualize Quantitative Data?
Per Stephen Few, we visualize quantitative data to perform three fundamental tasks in an effort to achieve three essential goals:
These three tasks are so fundamental to data visualization, Steve used them to define the term, as follows:
Data visualization is the use of visual representations to explore, make sense of, and communicate data.
Steve poses the question of why is it that we must sometimes use graphical displays to perform these tasks rather than other forms of representation? Why not always express values as numbers in tables? Why express them visually rather than audibly?
Essentially, there is only one good reason to express quantitative data visually: some features of quantitative data can be best perceived and understood, and some quantitative tasks can be best performed, when values are displayed graphically. This is so because of the ways our brains work. Vision is by far our dominant sense. We have evolved to perform many data sensing and processing tasks visually. This has been so since the days of our earliest ancestors who survived and learned to thrive on the African savannah. What visual perception evolved to do especially well, it can do faster and better than the conscious thinking parts of our brains. Data exploration, sensemaking, and communication should always involve an intimate collaboration between seeing and thinking (i.e., visual thinking).
Despite this essential reason for visualizing data, people often do it for reasons that are misguided. Steve dispels a few common myths about data visualization.
Myth #1: We visualize data because some people are visual learners.
While it is true that some people have greater visual thinking abilities than others and that some people have a greater interest in images than others, all people with normal perceptual abilities are predominantly visual. Everyone benefits from data visualization, whether they consider themselves visual learners or not, including those who prefer numbers.
Myth #2: We visualize data for people who have difficulty understanding numbers.
While it is true that some people are more comfortable with quantitative concepts and mathematics than others, even the brightest mathematicians benefit from seeing quantitative information displayed visually. Data visualization is not a dumbed-down expression of quantitative concepts.
Myth #3: We visualize data to grab people’s attention with eye-catching but inevitably less informative displays.
Visualizations don’t need to be dumbed down to be engaging. It isn’t necessary to sacrifice content in lieu of appearance. Data can always be displayed in ways that are optimally informative, pleasing to the eye, and engaging. To engage with a data display without being well-informed of something useful is a waste.
Myth #4: The best data visualizers are those who have been trained in graphic arts.
While training in graphic arts can be useful, it is much more important to understand the data and be trained in visual thinking and communication. Graphic arts training that focuses on marketing (i.e., persuading people to buy or do something through manipulation) and artistry rather than communication can actually get in the way of effective data visualization.
Myth #5: Graphics provide the best means of telling stories contained in data.
While it is true that graphics are often useful and sometimes even essential for data-based storytelling, it isn’t storytelling itself that demands graphics. Much of storytelling is best expressed in words and numbers rather than images. Graphics are useful for storytelling because some features of data are best understood by our brains when they’re presented visually.
We visualize data because the human brain can perceive particular quantitative features and perform particular quantitative tasks most effectively when the data is expressed graphically. Visual data processing provides optimal support for the following:
1. Seeing the big picture
Graphs reveal the big picture: an overview of a data set. An overview summarizes the data’s essential characteristics, from which we can discern what’s routine vs. exceptional.
The series of three bar graphs below provides an overview of the opinions that 15 countries had about America in 2004, not long after the events of 9/11 and the military campaigns that followed.
Steve first discovered this information in the following form on the website of PBS:
Based on this table of numbers, he had to read each value one at a time and, because working memory is limited to three or four simultaneous chunks of information at a time, he couldn’t use this display to construct and hold an overview of these countries’ opinions in his head. To solve this problem, he redisplayed this information as the three bar graphs shown above, which provided the overview that he wanted. Steve was able to use it to quickly get a sense of these countries’ opinions overall and in comparison to one another.
Bonus: Here is a link to where Steve discusses the example above on his website.
2. Easily and rapidly comparing values
Try to quickly compare the magnitudes of values using a table of numbers, such as the one shown above. You can’t, because numbers must be read one at a time and only two numbers can be compared at a time. Graphs, however, such as the bar graphs above, make it possible to see all of the values at once and to easily and rapidly compare them.
3. Seeing patterns among values
Many quantitative messages are revealed in patterns formed by sets of values. These patterns describe the nature of change through time, how values are distributed, and correlations, to name a few.
Try to construct the pattern of monthly change in either domestic or international sales for the entire year using the table below.
Difficult, isn’t it? The line graph below, however, presents the patterns of change in a way that can be perceived immediately, without conscious effort.
You can thank processes that take place in your visual cortex for this. The visual cortex perceives patterns and then the conscious thinking parts of our brains make sense of them.
4. Comparing patterns
Visual representations of patterns are easy to compare. Not only can the independent patterns of domestic and international sales be easily perceived by viewing the graph above, but they can also be compared to one another to determine how they are similar and different.
These four quantitative features and activities require visual displays. This is why we visualize quantitative data.
Data Blending: Why are Some Metric Values Blank in Documents Using Multiple Datasets in MicroStrategy Analytics Enterprise 9.4.1 (Part 8)
Starting with MicroStrategy Analytics Enterprise 9.4.1, Report Services documents can contain grids with objects coming from more than one dataset.
Multiple Datasets in a Single Grid/Graph/Widget Object in MicroStrategy Web 9.4 
Users now have the ability to add attributes and/or metrics from multiple datasets to a single grid, graph, or widget. For example, if Dataset #1 contains Category and Revenue and Dataset #2 contains Category and Profit, a grid can be created which contains Category, Revenue, and Profit.
Administrators can control the use of multiple datasets in a single grid, graph, or widget through the Analytical Engine VLDB properties window at the project level.
- Right mouse click (RMC) on the project name.
- Select Project Configuration.
- Click on Project Definition.
- Select ‘Advanced’.
- Click “Configure” under the Analytical engine VLDB.
The default value is set to: “Objects in document grids must come from the grid’s source dataset only”.
Users can set the set the source of the grid to a particular dataset or choose no dataset (in which case, the MicroStrategy engine will determine the best suited dataset). 
The MicroStrategy Analytical Engine displays no data for metrics in ambiguous cases or when there is a conflict. Ambiguous cases can arise in cases where multiple datasets contain the same objects. Examples based on the MicroStrategy Tutorial project have been provided to explain this information.
Note: When the MicroStrategy Analytical Engine cannot resolve the correct datatset as explained in the cases below, the data displayed for these will correspond to the value chosen for the missing object display under Project Configuration > Report definition > Null values > Missing Object Display. The default value for this blank.
Multiple datasets have the same metric. Only one dataset does not contain this metric and this dataset is set as the source of the grid.
This case is explained with an example based on the MicroStrategy Tutorial project.
1. Create the following objects:
a. Dataset DS1 with the attribute ‘Year’ and metric ‘Profit’.
b. Dataset DS2 with the attribute ‘Year’ and metrics ‘Profit’, ‘Revenue’.
c. Dataset DS3 with the attribute ‘Quarter’ and metric ‘Cost’.
2. Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Quarter’, ‘Profit’. Set the source of this grid to be the dataset ‘DS3’.
3. In the executed document, no data is displayed for the metric ‘Profit’ as shown below.
In the above example, the metric ‘Profit’ does not exist in the source dataset ‘DS3’ and exists in more than dataset which are in the document i.e., it exists in both ‘DS1’ and ‘DS2’. Since the engine cannot just randomly pick one of the two available datasets, it chooses not to display any data for this metric. If users do not want such blank columns to be displayed, set the source dataset so that such ambiguity does not arise.
The same metric exists multiple times on the grid. For example, users can have a smart compound metric and a component metric of this compound smart metric on the grid in the document. The smart metric and the component metric are from different datasets.
This case is also explained with an example based on the MicroStrategy Tutorial project.
1. Create the following objects:
a. Dataset DS1 with attribute ‘Year’ and metric ‘Profit’.
b. Dataset DS2 with attribute ‘Year’ and metrics ‘Revenue’, ‘Profit’, ‘Profit Margin’ (this is a compound smart metric calculated from metrics Revenue and Profit).
2. Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Revenue’, ‘Profit’ and ‘Profit Margin’. The source of this grid object is set to DS1.
3. In the executed document, no data is displayed for the metric ‘Profit Margin’, as shown below.
In the above example, since the source of the dataset is set to ‘DS1’, the ‘Profit’ metric is sourced from this dataset and the metric ‘Revenue’ is sourced from the dataset ‘DS2’ (as this is the ONLY datatset with this metric). However, for the metric ‘Profit Margin’, the component metric ‘Profit’ exists on dataset ‘DS1’, so this becomes a conflict metric and is not displayed. If the source of the grid is changed to ‘DS2’, the data is displayed correctly as shown below.
 MicroStrategy Knowledgebase, Why are some metric values blank in documents using multiple datasets in MicroStrategy Analytics Enterprise 9.4.1, TN Key: 44517, 12/16/2013, https://resource.microstrategy.com/support/mainsearch.aspx.
 MicroStrategy Knowledgebase, Multiple datasets in a single grid/graph/widget object in MicroStrategy Web 9.4, TN Key: 44944, 09/30/2013, https://resource.microstrategy.com/support/mainsearch.aspx.
NOTE: You may need to register to view MicroStrategy’s Knowledgebase.
Dorie Clark is a marketing strategist and professional speaker who teaches at Duke University’s Fuqua School of Business. Learn more about her new book Reinventing You: Define Your Brand, Imagine Your Future (Harvard Business Review Press) and follow her on Twitter.
I hope you find this helpful in your data visualization endeavors.
Data Visualization Is The Future – Here’s Why
“The Visual Organization is fundamentally about how progressive organizations today are using a wide array of data visualization (dataviz) tools to ask better questions of their data – and make better business decisions,” says Simon, citing the example of companies such as Amazon, Apple , Facebook, Google, Twitter, and Netflix, among others.
Two recent factors have conspired to make this the moment for data visualization. First, says Simon, is the rise of Big Data and the growing public awareness of its power. “Today more than ever, professionals are being asked to argue their cases and make their decisions based on data,” he says. “A new, data-oriented mind-set is permeating the business world.”
But that push outside IT circles means that many non-technical professionals must now produce and comprehend insights from Big Data. Visualization can help, and a raft of new tools makes that possible. “IBM, Cognos, SAS, and other enterprise BI (business intelligence) stalwarts are still around, but they are no longer the only game in town,” he says. “Today, an organization need not spend hundreds of thousands or millions of dollars to get going with dataviz. These new tools have become progressively more powerful and democratic over the last decade. Long gone are the days in which IT needed to generate reports for non-technical employees. They have made it easier than ever to for employees to quickly discover new things in increasingly large datasets. Examples include Visual.ly, Tableau, Vizify, D3.js, R, and myriad others.”