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Innovation Center
Interdisciplinary Product Development
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Data visualization
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Innovation Center
Interdisciplinary Product Development
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Data visualization Interdisciplinary Product 1 Caterpillar - - PowerPoint PPT Presentation
Data visualization Interdisciplinary Product 1 Caterpillar - - PowerPoint PPT Presentation
Data visualization Interdisciplinary Product 1 Caterpillar Innovation Center Development Data visualization: Whats the value in visualizing data? Why is data visualization such a thing these days? Mining data: Different approaches
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Data visualization is another form of story telling. Visualization makes complicated things understandable. Comparisons are easier when made visual. Visualization makes the “invisible” something you can see. Visualizing data helps people make better decisions. Data is a commodity that can be leveraged. The value in data visualization
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Innovation Center
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I want to say just one word to you. Just one word: Plastics.
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Innovation Center
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In June of 1812, Napoleon led an army consisting of 422,000 men in a campaign against
- Russia. His army began at the Polish/Russian border by crossing the Neiman river. In late July,
22,000 men pulled out of ranks and and waited just inside the Russian border. In August, 35,000 men detached and waited in Polotsk. As the majority of the troops marched on to Moscow, Russian troops retreated, destroying everything of value behind them. Fighting, fatigue, and illness diminished French numbers to 175,000 by the time they reached Witebsk. Upon approaching their objective, there was a loss of 27,000 men crossing the river Muskva. Napoleon’s troops finally reach Moscow in October with only 100,000 nearly-starving men
- remaining. He retreats in the face of a bitterly cold winter. By 9 November, the army was
reduced to 37,000 men as the temperature dipped to -8 degrees, and was further reduced to 24,000 men by 14 November, with a temperature of -21 degrees. Around 20 November, 35,000 troops from Polotsk rejoined the retreating army, but met with disaster at the crossing of the Berezina river, where they lost half of the remaining army, in temperatures of -20 degrees. Napoleon struggled back to Poland with only 10,000 men remaining.
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Data visualization as story-telling: Napoleon’s march on Moscow (The data)
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Charles Joseph Minard, 1869
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Making the invisible visible / using technology to provide answers
Étienne-Jules Marey, 1901 Eadweard Muybridge, 1886 Marey’s chronophotographic gun
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Everyday, we create data through our actions: What we purchase, how we browse, how long our phone calls last, where we’re driving, what we tweet. . . and most of it is collected. This is part of Big Data. Why is data visualization such a thing these days?
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Human and machine activity today is not all that different that it was 10 years ago, but the ability to track it, connect it, and analyze it has. We live in an informationally-rich environment. Rick Robinson calls this the “thickened-present.”
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The Internet of Things: Objects collecting and reporting actions, work-cycles, temperatures, vibrations, motions, locations, speed, repetition, quantities consumed, weights transferred, etc.
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In this class one of the problems we have been tasked with is this: CAT has big data—but how do they realize the value of it for their customers?” CAT calls it the Data Lake. The promise of its value can only be realized if we can make sense
- f it, and make it useful. How to begin?
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Start with existing data, and see if it tells us interesting things. Perturb a situation, monitor it, and see what happens. Start with a hypothesis, and find data to answer your questions. Or some combination of these things. . . Mining data: Where to start?
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Data-mining is one way to approach it as a commodity. But looking at “the data” and hoping to find insights is a monumental task. On the other hand, seeing data that has never been visualized before provides you with a slap in the face that’s quite powerful. Starting with existing data
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1635 1665 1695 1725 1755 1785 1815 1845 1860 1875 1890 1905 1920 1935 1949 1952 1955
Wood/biomass Coal
518 in 1804 1804 16,561 16,561 in 1845 1845 2,294 in 1876
Hydroelectric
Source: Whale oil production data from Alexander Starbuck, History of the American Whale Fishery, Waltham, Mass: 1878, p. 661; data for primary
Whaling on a large scale extended into the 1850s, then declined. The arc of its use as a fuel source is often used to illustrate M. King Hubbert’s bell-shaped production curve of a nonrecycleable mineral resource. Hubbert applied his theory in 1956 to correctly predict 1970 as the peak year for production of crude oil in the US mainland.
Whale oil production, thousands of barrels
Lights out for whale oil
103 1958 1961 1964 1967 1970 1973 1978 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2007 Petroleum Natural gas Nuclear Wind Geothermal Solar
Histogram: Our use of fuel types through the ages
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Tracking and collecting data to support a hunch you have is a reasonable approach. ”I bet there is something interesting here, and if I had proof, it would be powerful.” Monitor something, and see what happens
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Visualizing the development of language and it’s relationship to location
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Innovation Center
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Monitor something: Planning how to gather real numbers
Bottle movement instrument: Motion-sensitive accelerometer attached to a bottle to measure movement, direction of movement and duration of movement. Bottle camera instrument: A motion-activated, micro-camera attached to a bottle to capture in-the-moment video and audio of spray cleaner use in addition to movement data. Instrumented Storage Unit: A pressure-sensitive storage caddy that records "in-and-out" of multiple products from storage.
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Evidence: Minute-by-minute use over 30 days
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Evidence-based insight
Storage Bathroom Kitchen
Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Device ID: 348
Habitual Spot Planned
Living Room
Other
Blue AB
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Kitchen Bathroom Bedroom Living room Dining room
Story-telling: A comparison of use by room
46 11 10 44 26 25 12 5
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Ho Hori rizontal s surf rface Ve Vertic ical l surface
Additional value from the data: An “aha” moment
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Connecting motion to something you can see and feel
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It’s hard to collect original data in a “directed” way. It’s less hard (but hard) to analyze the right data to find insights into what you’re interested in. (We are asking you to do this). Start with a hypothesis, and find data to answer your questions.
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One of the things retailers want to know is how and when to push the right messages to customers. We know that people have dynamic attention, and toggle between mental activities constantly. Knowing when someone is in the right physical space and the right mental space to connect with them would be a breakthrough. . . One last example / collecting data to build on an idea
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Mobile Usage R e f e r e n c e & L e a r n i n g Search B r- w
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- Sprint Zone - Android
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RescueTime deployed onto the smartphones and other mobile devices a community of “opted-in” participants to track long-term data about everyday life.
A week of on-device activity from one participant
Visualization as a tool to plan complex research problems
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R e f e r e n c e & L e a r n i n g Search Browser Search A n d r
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SLIDE 36
Innovation Center
Interdisciplinary Product Development
Caterpillar
36
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+ RescueTime D Scout
Visualization as a tool to explain complex research plans
SLIDE 37
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Visualization as a tool to explain complex research plans
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Our insights are based on some data-point. Our presentations (our stories) are based on data. What Professor Moon is asking for in her Marketing Research is going to be based on data. Your challenge is to find a compelling way to present it, that supports your insights in a powerful way. Data, and the visualization of that data, is central to this class.
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http://www.edwardtufte.com/tufte/ http://feltron.com http://quantifiedself.com Range of visualization examples https://github.com/mbostock/d3/wiki/Gallery Art Olsen video http://tommytoy.typepad.com/tommy-toy-pbt-consultin/2011/10/pathbreaking-work-at-arthur-olsons-molecular-graphics-lab-drug-design-is-a-scientific-problem-so- important-that-researchers.html Machine Learning Intro http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
Sources
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Edward Tufte http://www.edwardtufte.com/tufte/index The Quantified Self http://quantifiedself.com Nicolaus Feltron http://feltron.com Arthur Olson http://tommytoy.typepad.com/tommy-toy-pbt-consultin/2011/10/pathbreaking-work-at-arthur-olsons-molecular-graphics-lab-drug-design-is-a- scientific-problem-so-important-that-researchers.html Machine Learning http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ Data Visualization Examples https://github.com/mbostock/d3/wiki/Gallery Deb Roy; The Birth of a Word https://www.ted.com/talks/deb_roy_the_birth_of_a_word?language=en