Visual Analytics - An Interaction of Sight and Thought - JMP

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Two essential activities • Multi-faceted views • Comparisons Data sensemaking revolves primarily around two essential activities: viewing data from multiple perspectives and comparing things. No one view of the data will suffice. To understand your data thoroughly, you must view it from every possible perspective. With each new view, more than anything else, you make compare things.
Multiple views of change through time Have you ever noticed that time-series data can look quite different if you change the interval of time that you’ve aggregated it to in a graph? For example, if you are examining a year’s worth of visits to your website with one value per quarter (aggregated to quarterly intervals), and then switch to a monthly aggregation of the same data, and then switch to a daily aggregation, the patterns of change might look quite different. All three versions of the graph are useful and correct, but the daily version reveals details that aren’t visible when viewing the same data by month or quarter. On the other hand, the overall trend is difficult to discern from the daily view. One view isn’t better than the other in general, but one is definitely better than the other when you have a specific analytical purpose in mind. For this reason, don’t restrict your view of timeseries data to a single interval of time, especially when looking for anything that seems interesting. Switch the level of aggregation from year to quarter, quarter to month, month to week, week to day, and so on—back and forth—to tease out the insights that can only arise when this is done. Software products that allow you to quickly and easily switch between various intervals of time while viewing data graphically are what’s needed to encourage this practice. The ability to switch time intervals with a mouse click or two, or by using something as simple as an interval slider control, will set you free to explore without distraction from onerous operations.
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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
Page 8 and 9: We must tap into that steady stream
Page 10 and 11: Data analysts JMP are now sexy. Dat
Page 12 and 13: To do this we need the right skills
Page 14 and 15: Two of the right skills • Statist
Page 16 and 17: What is visual thinking? Temple Gra
Page 18 and 19: We need greater balance. Modes of r
Page 20 and 21: Leonardo da Vinci For some four hun
Page 22 and 23: Complex problems require visual thi
Page 24 and 25: Tools extend our reach when they’
Page 26 and 27: 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 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 78 and 79: More dimensions through multiple vi
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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