Yap Xiong's List: Visual Analytics (Articles)

• Data visualization means different things to many people
• In my experience
• strong programming background with experience in Processing, Actionscript, or some other similar language and probably have worked with large databases at one point or another
• analyzers want to know the relationships between variables, find positive and negative trends, and are most likely to tell you that you should have used a different type of graph or chart for that dataset
• obsessed with the final product - what the visualization will finally look like
• designers
• field experts who want to visualize their data but might not have the know-how to follow through
• usually handle a large data visualization project on their own

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• Data Scientist
• able to extract information from large datasets and then present something of use to non-data experts.
• Visualization - graphs, tools, etc.
• skills and talents from often disjoint areas of expertise:
• We're seeing data scientists - people who can do it all - emerge from the rest of the pack.
• Oftentimes, visualization projects are disjoint processes and involve a lot of waiting
• Terabytes of data are coming in whether it be from websites or from sales across the country, but in an area where Excel is the tool of choice (or force), there are limitations, hence all the tools, applications, and consultancies to help out.
• the more you learn, the more you can do, and the higher in demand you will be as the amount of data grows and the more people want to make use of it.

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• There also were very few graphics dedicated to the differential impact of incarceration practices on poor and minority communities.
• easy but illogical way to summarize data points
• a good information graphic should begin to tell the story at first glance, before you have to read anything.
• but the overall theme should be clear without resorting to text

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• creating charts and bas
• Most people have created a basic bar or pie chart before, but few realize how much you can do with even the few simple tools in Excel.
• very little data is freely available
• Visualization also needs to break out of the academic world and do something for real people with real data and real questions
• in a few years will hopefully be part of mainstream technology the way other electronic media are today.
• whose makers seem to be completely unaware of the work that is being done in information visualization.
• Many Eyes exists in a much more academic setting than Swivel
• One important question for Many Eyes is therefore its longer-term sustainability, especially if it gets more popular and requires more resources to run.
• It does explain the description of Swivel as "YouTube for data" though, a slogan that was used for a short time when it was launched. But it also means that Swivel needs to be very careful if it wants to be taken seriously, since outrageous claims do not exactly build trust.
• he question of course is whether these users will trust Swivel with their valuable proprietary data, and see enough value in Swivel's own tools to take that risk (cost probably won't be an issue, at least not in comparison).
• download the data that exists on the sites. This is very useful, as it provides badly needed access to a huge variety of data.
• Existing visualizations can be changed easily in Swivel
• Many Eyes is missing such a function
• Swivel also has tags and graphs can be rated by users
• ags provide easy and democratic means of navigation, and are basically a must for a highly interactive website.
• ease of access for the user and development effort for the developers.
• But of course Java has one great advantage: interaction
• vertical and horizontal bar charts, scatterplots, and line charts.
• Many Eyes offers more than a dozen visualization types, and that's not even counting bar charts twice
• All of Many Eyes' visualizations are interactive
• Both Swivel and Many Eyes handle the comparison of numbers surprisingly badly
• missing from both sites is the ability to mix different graph types
• simply chart junk, and while Swivel can't keep their users from producing meaningless charts, they are very unwise to promote this kind of nonsense on their front page.
• inflated numbers.
• will need to appeal to more sophisticated customers that are actually willing to spend money.
• One of their challenges will be to provide cutting-edge visualization tools in a way that people can actually use

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• The current view of course is that visualization systems should be designed in a way that is useful to domain experts with little to no knowledge of visualization
• The current view of course is that visualization systems should be designed in a way that is useful to domain experts with little to no knowledge of visualization
• The current view of course is that visualization systems should be designed in a way that is useful to domain experts with little to no knowledge of visualization.
• current view of course is that visualization systems should be designed in a way that is useful to domain experts with little to no knowledge of visualization
• the casual (but interested) non- or semi-expert.
• can make things that are far more advanced and complex than we would expect somebody without experience in visualization to understand.
• These users need general (i.e., not data-specific) tools that will be much simpler, and that will need to follow known interaction paradigms as much as possible.

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• a hypothesis about the data that the choice of a particular formal expression aims to address
• At this point, visualization becomes a rhetorical device—it is biased towards conveying (but not necessarily validating) a hypothesis, effecting not only its visual expression, but also the use and description of data.
• While the piece in its essence is making a single statement, the fact that it is supported by data lends it both credibility and relevance.

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• Visualization as metaphor
• Metaphor simulates: it seeks to explain one subject through another.
• we (the viewers) expect to find rationale behind every decision
• In data visualization, metaphor is used to translate raw data into visual form
• the choice of any visualization is always a matter of interpretation.

  • Summary of a very heavy paragraph:
    
    1. Visualizations are subjective translations
    2. Visualizations are metaphorical (indirect meanings) when viewed with its context
    3. Metaphors translate actual data into a visual concept
    4. Therefore, visualization itself is always up to the nature of the translation/ metaohor.
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• source of a data point, the quality of the data, statistical significance, or why two numbers don't align?

  • Accuracy matters in the meaning of data - needs to be accepted regardless of presentation
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• People who have experience and comfort with numbers have the ability to abstract meaning from analysis for themselves—even when numbers don't line up, the data is unclear, or the analysis has minor flaws.

  • Does this mean the accuracy doesn't matter? Sounds like letting the data speak for itself, in spite of how it is presented
• gather their own meaning,

  • "Own meaning" - can be/ seems very subjective - suggests a high level of expertise needed (see analogy in previous sentences)
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• recognize that the "truth" won't tell an actionable story
• directionally-correct results

  • What is "directionally-correct"? Does that mean the results of the analytics will be dependent on thr situatuion/ context?
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• tangible and incontrovertible to hang onto
• personal credibility

  • Not just the data itself, or the accuracy, or the presentation, or the context it is used in - also the status & personality of the presenter. Subjective factor.
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• set up a time to discuss data quality and analysis methodology that is separate from discussion of results and implications.

  • Quality of data/ methodolgy is SEPARATE from results/ implications
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• belief that visual analysis software is needed by a broad audience of people, not just those who have the term “analyst” in their titles

  • Need for visual analytics software to:
    (A) Reach a broader market OR (B) have a more active role in daily life.
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• direct more of their efforts toward the practical needs of a broad audience of potential users.

  • Visual analytics still a closed maeket? Still a high expertise specialty?
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• desperate need for tools that will help them make sense of it
• litany of useless and ineffective pseudo-analytical features
• oftware companies such as Tableau that are thoughtful, agile, design-oriented, and well-informed, have differentiated themselves from the pack and are now reaping the rewards of their commitment to give people analysis tools that really work.

  • Analytics companies view themselves as different from "traditional" business analytical means. NOTE: interesting use of adjectives for description.
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• five core principles

  • Turned out to all be assumptions
• see and understand complex data.
• see and understand massive data
• new visual paradigms
• understand hidden insights
• help analysts save time.
• Their assumptions about what it would take to get people to adopt visual analytics made perfect sense, given their perspective at the time.
• solve much more widespread problems that are just as important by making it easier for people to do the simple stuff that they must do over and over again each day

  • Visual analytics can be applied to the more "normal" problems to make it more approachble & less elite.
    
    Tackle the repetitive/ necessary tasks first before proceeding onto more complex data.
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• start small,

  • Advice to tackle the smaller/ more basic sets (Excel) of data, instead of the larger ones.
    
    Does that mean the larger the data sets, the more specialized the task becomes.
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• start proven

  • No need for extensive, new visualizations. Can be solved by existing diagrams, midels. Does the meaning change when new visualizations are used?
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• save time

  • Visual analytics provide insight, but provide time-saving opportunities more
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• can no longer serve the needs of small groups with specialized needs, but should invite everyone to the table.

  • A call to decentralize the specialty of visual analytics - decentralization to the market allows analysts to develop better products & do more complex problems
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• Great Data Visualization Tells a Great Story
• They probably all told a great story
• Data can be boring if you don't know what you're looking for or don't know that there's something to look for in the first place
• There's meaning, truth, and beauty.
• Sometimes the stories will be simple and other times complex. Some will belong in a textbook; others will come in novel form. It's up to the statistician, computer scientist, designer, or analyst to make that decision.
• makes it easy for readers to see the story in the data.
• They present the data, give you the facts, and (usually) leave it to you to make your own conclusions.
• S/he who can tell a great story possesses great power, because s/he has the power to move people. Data visualization can show that story for you
• The Lesson: Find the Story

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• Enter the new "visual scientists" who are turning bits and bytes of data -- once purely the domain of mathematicians and coders -- into stories for our digital age.
• fundamentally changing the way we create and consume narratives about events, products and services.
• advances in technology at the presentation layer and the new found ability to tap into once hidden data sources that is enabling these new visual scientists to chart a new narrative course.
• What data do you have that is truly valuable?
• How can you create a meaningful experience or narrative out of it?
• Traditional charts and spreadsheets fail to evoke urgency, meaning, and impact, ultimately failing to reach real people and do the one thing we need: elevate us from abstract, large-scale data dumps to another plane of understanding that makes a real connection and prompts action and emotion.
• But just because I'm engaged with your data doesn't mean you've rewritten the rules of narrative.

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