I can put 3,356 horizontal bars on the screen at once, but they will appear as thin, indistinguishable lines. Also, there wont be any room for the customer names, but those names dont appear in the bubble display either, so thats not a problem. Lets see how it looks. This isnt ideal by any means, but it provides a more informative overview than the bubbles. For example, we can easily see that approximately 10 of the customers made purchases totaling more than 35,000, approximately 70 of them made purchases totaling less than 5,000, and approximately half of them made purchases totaling more than 2,500. We could continue citing similar observations. If we wanted to see and compare individual customers, a sorted, scrolling version of a normal bar graph like the one below would often work when comparing similar values, and we could filter the data to compare specific customers that dont simultaneously appear on the.
Understanding level of Detail (LOD) Expressions with
We can now see that sales in khan the. Were approximately nine times greater than sales in Argentina. What if there are too many values to display in a bar graph without having to scroll? Lets look at larger set of data—sales to 3,356 customers—first using bubbles. Assuming theyre all there, thats a lot of bubbles. What do these bubbles tell us? Some customers buy a lot and some buy little and a bunch buy amounts in between. No, thats pretty much. And why is only the one bubble that represents Jim Hunt labeled? As well write see in a moment, it isnt the largest.
Yes, its there in paperwork the list; it just isnt labeled. We can solve this omission by forcing all of the labels to appear, as follows: Now, can you find south Africa? Given enough time, you can spot it at the top. What is the value of sales in south Africa? Nothing about the bubble reveals this, but we can hover over the bubble to access its value. We could do this for all the bubbles if we dont mind taking forever to see what a bar graph would reveal to an approximate degree automatically. How much greater are sales in the United States than Argentina? Come on, give it a try. Try it now using the bar graph below.
Did they add this feature to essay satisfy one of their prominent uk customers, the guardian? Whatever the reason, with the addition of word clouds, how many of Tableaus customers will waste their time trying to analyze data using this ineffective form of display? Packed Bubbles, bubbles have their place in the lexicon of visual language, but only when encoding values using the sizes of circles is the best choice available because the most effective means—2-D position (e.g., data points along a line in a line graph) and length. This is the case when we display quantitative values on a map, because 2-D position is already being used to represent geographical location and bars cannot be aligned for easy comparison because they cannot share a common baseline when theyre geographically positioned. Bubbles are also useful when, in a scatter plot, which uses horizontal position to represent one variable friendship and vertical position to represent another, we also want to make rough comparisons among the values of a third variable in the form of a bubble plot. Using bubbles by themselves, however, is never the best way to display values, but this is what youll soon be able to do with Tableau. Heres a simple example that displays sales per country: How are sales in south Africa?
Bars would provide what the word cloud cannot, a relative representation of the values in a way that our brains can perceive. Words differ in length, so in a word cloud a long word that was spoken 100 times would appear much more salient than a short word that appeared the same number of times. You might wonder, What if there are too many words for a horizontal bar graph? In that case, another one of Tableaus new visualizations—a treemap—could handle the job more effectively. More about treemaps later. Word clouds are fun, but they lack analytical merit. When did Tableau, which was originally developed for visual analysis, become a tool for creating impoverished infographics?
Self-service bi review : Tableau
Tag clouds also make it difficult to see which topics appear in a set of tags. For example, in the image below, its hard to see which operating systems are talked about versus which ones are omitted. Intuitively, to me, it seemed that an ordinary word list would be better for getting the gist of a set of tags because it would be more readable. Since marti wrote this article, what was once reserved for html tags has become a popular way to display words from many contexts, such as books and speeches. Heres a word cloud that Tableau is currently featuring on its website to showcase this new addition to tableau 8: What this tells me is that the candidates said the following words quite a bit: people, going, governor, president, government, weve, make, more, along with.
These and individual words without context are not very enlightening. Filters have been added to this word cloud for selecting words spoken by Obama, romney, or both candidates and for removing words that were spoken outside a specified number of instances. Combining a word cloud with filters gives it an appearance of analytical usefulness, but the appearance is deceiving. A word cloud is as useful for data analysis and presentation as a cheap umbrella is for staying dry in a hurricane. Assuming that an analysis of these words in isolation from their context is useful, a horizontal bar graph would have displayed them far better.
To express my concern, Ill focus primarily on three new visualizations that are being added in Tableau 8 and why, in two cases, they should have never been added and, in one case, how its design fails in a fundamental way. Word Clouds, back in 2008 my friend Marti hearst, who teaches information visualization and search technologies. Berkeley, wrote a guest article for my newsletter about word clouds. In the article, marti described some of the fundamental flaws of word clouds, which she referred to in the article as tag clouds, because these visualizations were always based on html tags at the time. I was confused about tag clouds in part because they are clearly problematic from a perceptual cognition point of view. For one thing, there is no visual flow to the layout.
Graphic designers, as well as painters of landscapes, know that a good visual design guides the eye through the work, providing an intuitive starting point and visual cues that gently suggest a visual path. By contrast, with tag clouds, the eye zigs and zags across the view, coming to rest on a large tag, flitting away again in an erratic direction until it finds another large tag, with perhaps a quick glance at a medium-sized tag along the way. Small tags are little more than annoying speed bumps along the path. In most visualizations, physical proximity is an important visual cue to indicate meaningful relationships. But in a tag cloud, tags that are semantically similar do not necessarily occur near one another, because the tags are organized in alphabetical order. Furthermore, if the paragraph is resized, then the locations of tags re-arrange. If tag A was above b initially, after resizing, they might end up on the same line but far apart.
Review : Tableau takes self-service bi to new heights
I recently received an email promoting the merits of Tableau. It included a link to more information, and when I clicked on it, this is what I read: Crave more Bling? I couldnt believe my eyes. Could I have clicked on a link to sap business Objects by mistake? This is not the tableau that i know and respect. As it turns out, someone in the tableaus Marketing Department thought twice about the term bling and removed it before my screams reached seattle, but in truth, whoever called this bling was just being honest; some healthy of the items in this list of new visualizations. I wont write a full review of Tableau 8 here. Despite the problems that Im focusing on, this version of the software includes many worthwhile and well-designed features. For the time being, it will remain one of the best visual data exploration and analysis tools on the market, but Im concerned that its current direction does not bode well for Tableaus future.
Not long after starting Perceptual Edge, i discovered Tableau in its original release and business wrote the first independent review of Tableau. I was thrilled, for in Tableau i found a bi software company that shared my vision of visual data exploration and analysis done well. Since then ive used Tableau, along with Spotfire, panopticon, and sas jmp, to illustrate good data visualization functionality in my courses and lectures. Until recently, i assumed that Tableau, of all these vendors, would be the one mostly likely to continue its tenacious commitment to best practices. However, what ive seen in Tableau 8, due to be released soon, has broken my heart. Tableau is now introducing visualizations that are analytically impoverished. Tableaus vision has become blurred.
inspiring vision, but it is difficult for that vision to remain pure when the organization grows to 50, 100, 1,000, or more people. The demands of payroll and release schedules make it easier and easier to justify compromises and to chase near-sighted wins. Add to these challenges the demands of taking a company public and the alchemy seldom produces gold. What does this have to do with Tableau? I believe that this wonderful company, which I have uniquely appreciated and respected, is losing the clear vision of its youth. Even though Tableau distinguished itself by a courageous commitment to best practices, which I believe is why it has done so well, it now seems to be competing with the big guys by joining in their folly. Tableau seems to have forsaken the road less travelled of elegance through simplicity for the well-trodden super-highway of more and sexier is better. Tableau has a special place in my heart.
Watch the full course on LinkedIn learning 100 of Lynda content is now on LinkedIn learning. Get unlimited access to more than 6,000 courses, including personalized recommendations. Start my free month. You are now leaving m and will be automatically redirected to linkedIn learning to start your free trial. Ive seen it happen many times, but it never ceases to sadden. An organization starts off with a clear vision and an impervious commitment to excellence, but as it grows, the vision blurs and excellence gets diluted through a series of compromises. Software companies are often founded hotel by a few people with a great idea, and their beginnings are magical. They shine as beacons, lighting the way, but as they grow, what was once clear becomes clouded, what was once firm becomes flaccid, and what was once promising becomes just one more example of business as usual. The prominent business intelligence (BI) software companies of today have become too big to easily change course in necessary ways and too focused on quick wins to ever make the sacrifices that would be needed to.
Tableau the leader: Data visualization
Library, tableau, learn how to use tableau to see and understand your business's data better. Tableau is a key player in the business intelligence field. These tutorials will help you use this program to analyze and visualize your organization's data. Start my free month, short now, all m courses are on LinkedIn learning. Start your free month on LinkedIn learning. Unlimited Access, choose exactly what you'd like to learn from our extensive library. Expert teachers, learn from industry experts who are passionate about teaching. Learn Anywhere, switch between devices without losing your place.