Visual Analytics - An Interaction of Sight and Thought - JMP

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More dimensions through multiple visual encodings and small multiples You can increase the number of dimensions (variables) that can be viewed simultaneously in two ways: 1) by using multiple visual encodings, and 2) by breaking the data into a series of small graphs that share common scales so they can be compared to one another. In the bubble plot on the right, we can view four variables at once: the rate of property crime based in position along the X-axis, the rate of violent crime based on position along the Y-axis, the population of states based on the size of the bubbles, and the region in which each state resides based on color. The series of small multiples on the left allows us to revenue, costs of goods sold, and gross profit along the Y-axis, quarters along the X-axis, and different product lines per graph. If we tried to display all of this information in a single graph, it would be far too cluttered and difficult to perceive.
More perspectives through coordinated views There’s an old folktale that you’ve probably all heard about three blind men who encounte elephant one day for the first time and do their best to learn about it by touch alone. The experience of each is unique because each touches a different part of the elephant. This ancient story, originally from China, can teach us something important today about busine intelligence (BI). According to the original Chinese tale, the first man touches the elephant ear, the second his legs, and the third his tail. From this point, here’s how the story goes: The three blind men then went their way. Each one was secretly excited over the experience and had a lot to say, yet all walked rapidly without saying a word. "Let's sit down and have a discussion about this queer animal," the second blind man sa breaking the silence. "A very good idea. Very good." the other two agreed for they also had this in mind. With waiting for anyone to be properly seated, the second one blurted out, "This queer anima like our straw fans swinging back and forth to give us a breeze. However, it's not so big well made. The main portion is rather wispy." "No, no!" the first blind man shouted in disagreement. "This queer animal resembles two big trees without any branches." "You're both wrong." the third man replied. "This queer animal is similar to a snake; it's l and round, and very strong." How they argued! Each one insisted that he alone was correct. Of course, there was no conclusion for not one had thoroughly examined the whole elephant. How can anyone describe the whole until he has learned the total of the parts. If I retold this story today to teach a lesson about BI, I might call it “Three blind analysts an data warehouse.” Business people struggle every day to make sense of data, stumbling blindly, touching only small parts of the information, and coming away with a narrow and fragmented understanding of what it means.
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Visual Analytics An Interaction of
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Hurray data! Hurray technology! We
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Upon this gifted age, in its dark h
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We must tap into that steady stream
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Data analysts JMP are now sexy. Dat
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To do this we need the right skills
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Two of the right skills • Statist
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What is visual thinking? Temple Gra
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We need greater balance. Modes of r
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Leonardo da Vinci For some four hun
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Complex problems require visual thi
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Tools extend our reach when they’
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Can computers replace us? Our brain
Page 28 and 29: Visualization empowers statistics.
Page 30 and 31: “I see!” There is an intimate c
Page 32 and 33: Vision is our dominant sense. 30% 7
Page 34 and 35: Trends, patterns, and exceptions co
Page 36 and 37: This visualization fails to do this
Page 38 and 39: The right balance 38 Information vi
Page 40 and 41: Many visualization tools feature de
Page 42 and 43: Bling your graph. Many software ven
Page 44 and 45: 3-D graphs are rarely useful. One o
Page 46 and 47: Even when there’s a 3 rd variable
Page 48 and 49: We don’t want to confuse people w
Page 50 and 51: Good visualizations and good visual
Page 52 and 53: This arrangement is even better yet
Page 54 and 55: Visual perception works according t
Page 56 and 57: Visual perception is not just camer
Page 58 and 59: Two essential activities • Multi-
Page 60 and 61: Data analysis requires rapid intera
Page 62 and 63: Key interactions of visual analysis
Page 64 and 65: Shneiderman’s Mantra “Overview
Page 66 and 67: Overview first, zoom and filter, th
Page 72 and 73: Attention is limited. Ever saw a ma
Page 74 and 75: Capacity = 3 or 4 chunks at a time
Page 76 and 77: Good analysis tools augment memory
Page 80 and 81: Coordinated views expand what we ca
Page 82 and 83: Analytical challenges demand statis
Page 84 and 85: Predictions rely on statistical mod
Page 86 and 87: Finding and understanding the stori
Page 88 and 89: Wisdom Knowledge Information Inform
Page 90 and 91: O perpetual revolution of configure
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Visual Analytics - An Interaction of Sight and Thought - JMP

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