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Advanced Business Analytics > 8 - Data Visualization > Flashcards

8 - Data Visualization Flashcards

1
Q

Why visualize?

Explorative Analysis

A
  • visualisation supports analyst intuition, e.g. finding outliers
  • may help you look for connections you didn’t think of before
  • manual data mining
  • > requires both good visualization and flexible adaptation
  • > you visualize for yourself
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2
Q

Why visualize?

Decision support & management information

A
  • visualization can provide a quick overview of relevant trends and patterns, e.g. data from month to month
  • > requires both good visualization and simple adaptation
  • > you build visualization tools
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3
Q

Why visualize?

Presentation and argumentation

A
  • Visualization can help you to underline your arguments with quantitative data in a way that is easy to communicate
  • careful: ethics of visualization, e.g. What do I decide ti show? How do I show it? Manipulation without lying
  • > requires polished visualization, no adaptation
  • > you visualize for others
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4
Q

Good visualization …

A
  • shows all relevant data
  • makes the audience think about the content rather than the representations
  • does not distort the data
  • makes large data sets understandable
  • enables comparisons
  • layers details from overview to finer points
  • has a clear purpose
  • is integrated with the context of the representation

= substance + data analysis + design

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5
Q

Three steps for effective visualization

A
  1. formulate the question
  2. gather (and analyze) the data
  3. apply a visual representation
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6
Q

Question, concepts and visuals

What … is the best and the worst?

A

Concept:
- maximums and minimums

Visuals:
- bar graph

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7
Q

Questions, concepts and visuals

How has … changed over time?

A

Concept:
- temporal patterns (trend, seasonality)

Visuals:
- line graph

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8
Q

Questions, concepts and visuals

What … stand out from the rest?

A

Concept:
- outliers

Visuals:
?

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9
Q

Questions, concepts and visuals

What makes … different from …?

A

Concept:
- Clustering

Visuals:
?

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10
Q

Questions, concepts and visuals

How are … and … related?

A

Concept:
- correlation

Visuals:
- scatter plot

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11
Q

Questions, concepts and visuals

What is the breakdown for …?

A

Concept:
- distribution

Visuals:
- stacked bar graph

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12
Q

Warning: Visualizations can distort data

Data can be distorted by …

A
  • changing the scale of the y-axis between diagrams
  • modifying the base line
  • switching the aggregation level
  • using areas to show one-dimensional data
  • using advantageous visual effects (shadows, highlights, …)
  • > distortion can be the consequence of errors, mislead decoration, or intentional deception
  • > data visualization is also a matter of ethics
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13
Q

Types of representation

A
  • size: represent by area
  • color: e.g. coloring values differently
  • location: e.g. on a map
  • network: e.g. identifying different groups
  • time: e.g. line graph across different years
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14
Q

Different ways of visualizing distributions

A

Sorted
- you can show the median

Unsorted

  • distribution according to the time of sample
  • no median possible
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15
Q

Histograms: Beware the power of bins

A
  • small bins show variations at higher granularity
  • the larger the bins the less variation is visible
  • the more we aggregate the the more the median becomes obvious (however further aggregation leads to loss of information)
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16
Q

How can proportions be shown?

A

Pie chart:
- need to add up to 100%

(Stacked) Bar graphs

17
Q

Maximize Information Content

A

Pay attention to:

  • adding labels providing information on direct, size
  • scaling time by providing overall duration
  • summarizing development in static overview
  • choosing colors that are good for visualization
18
Q

Use the smallest effective Difference

A

For elegant visualization that do not distract, make visual distinctions as subtle as possible but as clear as necessary

  • mark only those lines strongly, that display the most important data
  • make color differences visible
  • avoid “chart junk”
19
Q

Three approaches to using color

A

Sequential:
- same or similar hues are used and saturation varies for a single metric

Diverging:
- two hues are used to indicate a division, such as positive and negative values

Qualitative:
- when data is non-numeric, contrasting colors are used for each category

20
Q

Scaling

Scaling one graph vs. scaling several graphs

A

One Graph:

  • deliberately set the axes’ minimum and maximum value
  • chose the best aspect ratio to make information visible

Several Graphs:

  • consider scaling all compared graphs similarly
  • makes comparisons easier
  • can make graphs hard to read
21
Q

Aggregation

A
  • can make data stand out

- can gloss over data

22
Q

Multiples and parallelism

A
  • can be used for comparisons and for transformations
  • makes both similarities and differences easier to follow and understand
  • reduces effort that can then be spend on interpreting relevant details - “Don’t make me think”
23
Q

Infographics vs. Data Visualization

A

Data visualisation creates a picture from a data set that efficiently communicates the main insights

Infographics tell a story through

  • data visualization
  • and text
  • and images
  • > frequently used in the context of PR, marketing and journalism
  • > can also visualize a process
24
Q

The art of (visual) storytelling

A

Introduction/Foundation:
- What is this about and why should you care?

Ah ha! The main event
- Some new, previously unknown piece of information - generate insight. This is the infographics entire reason of existence

Conclusion/Call to action
- What is the follow-up we want? Addresses?

25
Q

Tips for designing good infographics

A

Be accurate
- tell what you did and what you found out

Focus on a key message
- why should we care?

Visualize when possible

  • show trends, ratios, patterns, but also
  • processes, organizational hierarchies, examples …

Minimise text
- presentations should not be good at transferring knowledge without verbal explanation

Be data transparent

  • where did the data come from
  • what did you do to it?
26
Q

Animated and Interactive Visualizations

A
  • Interactive visualizations let you adapt diagrams to filter or aggregate data on the fly
  • these are particularly relevant for web based presentations of data sets
  • a range of tools, e.g. based in Python, is available however: the aforementioned design principles still apply

Advanced Business Analytics (11 decks)

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