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Data Analytics process in Learning and Academic Analytics projects Day 4: Data visualization Alex Rayón Jerez [email protected] DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es

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Data Analytics process in Learning and Academic Analytics projects. Day 4: Data visualization

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Page 1: Data Analaytics.04. Data visualization

Data Analytics process in Learning and Academic

Analytics projects

Day 4: Data visualization

Alex Rayón [email protected]

DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain

www.deusto.es

Page 2: Data Analaytics.04. Data visualization

“Perfection is achieved not when there is nothing more to add, but when there is nothing

left to take away”

Antoine de Saint-Exupery

Page 3: Data Analaytics.04. Data visualization

Narrative+

Design+

Statistics

Page 4: Data Analaytics.04. Data visualization

“[...] people almost universally use story narratives to represent, reason about, and make

sense of contexts involving multiple interacting agents, using motivations and goals to explain

both observed and possible future actions. With regard to learning analytics, I’m seeing this as how

it can contribute to the retrospective understanding and sharing of what transpired

within the operational contexts”

[Zachary2013]

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Objectives

● Know the foundations○ Learn the principles of information visualization

● Learn about existing techniques and systems○ Effectiveness

○ Develop the knowledge to select appropriate visualization techniques for particular tasks

● Build○ Build your own visualizations○ Apply theoretical foundations

Page 6: Data Analaytics.04. Data visualization

Table of contents

● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 7: Data Analaytics.04. Data visualization

Table of contents

● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 8: Data Analaytics.04. Data visualization

Introduction

● Danger of getting lost in data, which may be:○ Irrelevant to the current task in hand○ Processed in an inappropriate way○ Presented in an inappropriate way

Source: http://www.planetminecraft.com/server/padlens-maze/

Page 9: Data Analaytics.04. Data visualization

Introduction (II)

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Introduction (III)

● Good graphics….○ Point relationships, trends or patterns○ Explore data to infer new things○ To make something easy to understand○ To observe a reality from different viewpoints○ To achieve an idea to be memorized

Page 11: Data Analaytics.04. Data visualization

Introduction (IV)

● It is a way of expressing○ Like maths, music, drawing or writing

● So, it has some rules to respect

Source: http://powerlisting.wikia.com/wiki/Mathematics_Manipulation

Page 12: Data Analaytics.04. Data visualization

Table of contents

● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 13: Data Analaytics.04. Data visualization

HistoryDefinition and characteristics

18th Century 19th Century 20th Century

Joseph PriestleyWilliam Playfair

John SnowCharles J. Minard

F. Nightingale

Jacques BertinJohn Tukey

Edward TufteLeland Wilkinson

Page 14: Data Analaytics.04. Data visualization

History18th Century: Joseph Priestley

Source: http://en.wikipedia.org/wiki/A_New_Chart_of_History#mediaviewer/File:A_New_Chart_of_History_color.jpg

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History18th Century: Joseph Priestley (II)

● Lectures on History and General Policy (1788)○ A Chart of Biography (1765)○ A New Chart of History (1769)

● Beautiful metaphors of an inaccurate and abstract dimension (time) translated to a concrete one (space)○ Time thinking consumes cognitive

resources

Page 16: Data Analaytics.04. Data visualization

History18th Century: William Playfair

Source: http://en.wikipedia.org/wiki/William_Playfair

Page 17: Data Analaytics.04. Data visualization

History19th Century: John Snow

Source: http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

Page 18: Data Analaytics.04. Data visualization

History19th Century: Charles J. Minard

Source: http://en.wikipedia.org/wiki/Charles_Joseph_Minard

Page 19: Data Analaytics.04. Data visualization

History19th Century: Florence Nightingale

Source: http://en.wikipedia.org/wiki/Florence_Nightingale

Page 20: Data Analaytics.04. Data visualization

History20th Century: Jacques Bertin

Source: http://www.amazon.com/Semiology-Graphics-Diagrams-Networks-Maps/dp/1589482611

Page 21: Data Analaytics.04. Data visualization

History20th Century: John W. Tukey

Source: http://books.google.es/books/about/Exploratory_Data_Analysis.html?id=UT9dAAAAIAAJ&redir_esc=y

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History20th Century: Edward R. Tufte

Source: http://www.edwardtufte.com/tufte/books_vdqi

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History20th Century: Leland Wilkinson

Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448

Page 24: Data Analaytics.04. Data visualization

History20th Century: Leland Wilkinson

Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448

Page 25: Data Analaytics.04. Data visualization

Table of contents

● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 26: Data Analaytics.04. Data visualization

ConceptsIntroduction

● Data Visualization● Information visualization● GeoVisualization● Visual Analytics● Information Design● Infographic

Page 27: Data Analaytics.04. Data visualization

ConceptsIntroduction (II)

● Cognitive tools: extending human perception and learning○ Were invented and developed by our ancestors for

making sense of the world and acting more effectively within it

■ Stories that helped people to remember things by making knowledge more engaging

■ Metaphors that enabled people to understand one thing by seeing it in terms of another

■ Binary oppositions like good/bad that helped people to organize and categorize knowledge

Page 28: Data Analaytics.04. Data visualization

ConceptsIntroduction (III)

Source: http://ierg.net/about/briefguide.html#cogtools

Page 29: Data Analaytics.04. Data visualization

ConceptsIntroduction (IV)

Source:http://en.wikipedia.org/wiki/Cognitive_ergonomics

Page 30: Data Analaytics.04. Data visualization

ConceptsData visualization

The use of computer-supported, interactive, visual

representations of abstract elements to amplify cognition

[Card1999]

Page 31: Data Analaytics.04. Data visualization

ConceptsInformation visualization

● Also known as InfoVis● Focuses on visualizing non-physical, abstract

data such as financial data, business information, document collections and abstract conceptions

● However, inadequately supported decision making [AmarStasko2004]○ Limited affordances○ Predetermined representations○ Decline of determinism in decision-making

Page 32: Data Analaytics.04. Data visualization

ConceptsGeovisualization

● Geo-spatial data is special since it describes objects or phenomena that are related to a specific location in the real world

Source: http://www.boostlabs.com/why-geovisualization-geographic-visualization-works/

Page 33: Data Analaytics.04. Data visualization

ConceptsVisual Analytics

The science of analytical reasoning facilitated by

interactive visual interfaces

[ThomasCook2005]

Page 34: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (II)

[Keim2006]

Page 35: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (III)

[Keim2006]

“Visual analytics is more than just visualization and can rather be seen as an integrated approach

combining visualization, human factors and data analysis. [...]integrates methodology from information analytics, geospatial analytics, and scientific analytics. Especially human factors (e.g., interaction, cognition,

perception, collaboration, presentation, and dissemination) play a key role in the communication

between human and computer, as well as in the decisionmaking process.”

Page 36: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (IV)

● [Shneiderman2002] suggests combining computational analysis approaches such as data mining with information visualization

● People use visual analytics tools and techniques to○ Synthesize information and derive insight from

massive, dynamic, ambiguous and often conflicting data

○ Detect the expected and discover the unexpected

○ Provide timely, defensible, and understandable assessments

○ Communicate assessment effectively for action

Page 37: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (V)

Interactivevisualization

Computational analysis

Analyticalreasoning

Page 38: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (VI)

● Combine strengths of both human and electronic data processing [Keim2008]○ Gives a semi-automated analytical process○ Use strengths from each

Page 39: Data Analaytics.04. Data visualization

ConceptsVisual Analytics (VII)

[Verbert2014]

Page 40: Data Analaytics.04. Data visualization

ConceptsInformation design

The practice of presenting information

in a way that fosters efficient and effective

understanding of it

Page 41: Data Analaytics.04. Data visualization

ConceptsInformation design (II)

Source: http://www.nytimes.com/imagepages/2007/03/17/nyregion/nyregionspecial2/20070318_TRAIN_GRAPHIC.html

Page 42: Data Analaytics.04. Data visualization

ConceptsInfographics

The graphic visual representations of data,

information or knowledge intended to present complex

information quickly and clearly

Page 43: Data Analaytics.04. Data visualization

ConceptsInfographics (II)

Source: http://blog.crazyegg.com/2012/02/22/infographics-how-to-strike-the-elusive-balance-between-data-and-visualization/

Page 44: Data Analaytics.04. Data visualization

ConceptsInfographics (III)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016760ebbbcd970b-550wi

Page 45: Data Analaytics.04. Data visualization

ConceptsComparison

Source: http://www.slideshare.net/SookyoungSong/hci-tutorial0212

Page 46: Data Analaytics.04. Data visualization

Table of contents

● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 47: Data Analaytics.04. Data visualization

ProcessIntroduction

The purpose of analytical displays of evidence is to assist thinking. Consequently, in constructing displays of evidence, the first question

is, “What are the thinking tasks that these displays are supposed to serve?” The central claim of the book is that effective analytic

designs entail turning thinking principles into seeing principles. So, if the thinking task is to understand causality, the task calls for a design principle: “Show causality.” If a thinking task is to answer a question

and compare it with alternatives, the design principle is: “Show comparisons.” The point is that analytical designs are not to be

decided on their convenience to the user or necessarily their readability or what psychologists or decorators think about them;

rather, design architectures should be decided on how the architecture assists analytical thinking about evidence.

Edward T. Tufte in an interview

Page 48: Data Analaytics.04. Data visualization

ProcessData Visualization Reference Model

[Chi2000]

Page 49: Data Analaytics.04. Data visualization

Process1) Data transformation

● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data

● Encoding of relation○ Lines○ Maps and diagrams

Page 50: Data Analaytics.04. Data visualization

Process1) Data transformation (II)

● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data

● Encoding of relation○ Lines○ Maps and diagrams

Page 51: Data Analaytics.04. Data visualization

Process1) Data transformation (III)

[Shneiderman1996]

Page 52: Data Analaytics.04. Data visualization

Process1) Data transformation (IV)

Data Visualization [Jarvainen2013]

Univariate data

Page 53: Data Analaytics.04. Data visualization

Process1) Data transformation (V)

Data Visualization [Jarvainen2013]

Bivariate data

Page 54: Data Analaytics.04. Data visualization

Process1) Data transformation (VI)

Anscombe's quartetSource: http://en.wikipedia.org/wiki/Anscombe's_quartet

Page 55: Data Analaytics.04. Data visualization

Process1) Data transformation (VII)

Data Visualization [Jarvainen2013]

Multivariate data

Page 56: Data Analaytics.04. Data visualization

Process1) Data transformation (VIII)

● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data

● Encoding of relation○ Lines○ Maps and diagrams

Page 57: Data Analaytics.04. Data visualization

Process1) Data transformation (IX)

● Relation○ A logical or natural association between two or more

things○ Relevance of one to another○ Connection

Page 58: Data Analaytics.04. Data visualization

Process1) Data transformation (X)

Source: http://www.digitaltrainingacademy.com/socialmedia/2009/06/social_networking_map.php

Social network

Lines indicate relationship

Page 60: Data Analaytics.04. Data visualization

Process1) Data transformation (XII)

Source: http://www.d3noob.org/2013/02/formatting-data-for-sankey-diagrams-in.html

Sankey Diagram

Page 61: Data Analaytics.04. Data visualization

Process1) Data transformation (XIII)

Source: http://en.wikipedia.org/wiki/Harry_Beck

Page 62: Data Analaytics.04. Data visualization

Process1) Data transformation (XIV)

A Tour Through the Visualization Zoo Source: http://homes.cs.washington.edu/~jheer//files/zoo/

Page 63: Data Analaytics.04. Data visualization

Process1) Data transformation (XV)

Page 64: Data Analaytics.04. Data visualization

Process2) Visual mapping

Ranking of elementary perceptual tasks [ClevelandMcGill1985]

Page 65: Data Analaytics.04. Data visualization

Process2) Visual mapping (II)

● Two researchers of the AT&T Bell Labs, William S. Cleveland y Robert McGill, published a core article in the Journal of the American Statistical Association

● The title was: “Graphical perception: theory, experimentation, and application to the development of graphical methods”

● It proposes a guide the most suitable visual representation depending on the objective of each graph

Page 66: Data Analaytics.04. Data visualization

Process2) Visual mapping (III)

“A graphical form that involves elementary perceptual tasks that lead to more accurate judgements than another

graphical form (with the same quantitative information) will result in a

better organization and increase the chances of a correct perception of

patterns and behavior.”

Page 67: Data Analaytics.04. Data visualization

Process2) Visual mapping (IV)

Source: http://www.businessinsider.com/pie-charts-are-the-worst-2013-6

“Save the pies for dessert”

(Stephen Few)

Page 68: Data Analaytics.04. Data visualization

Process2) Visual mapping (V)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0167631df6f7970b-550wi

Page 69: Data Analaytics.04. Data visualization

Process2) Visual mapping (VI)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016302299aa9970d-550wi

In some representations, the accuracy is not the

objective, but the perception of general

patterns, concentrations, aggregations, trends, etc.

The shapes in the low part of the list could be

quite useful

Page 70: Data Analaytics.04. Data visualization

Process2) Visual mapping (VII)

Page 71: Data Analaytics.04. Data visualization

Process2) Visual mapping (VIII)

Depictive graphics Symbolic graphics

Source: http://www.dnr.mo.gov/regions/regions.htm

Source: http://trevorcairney.blogspot.com.es/2010_04_01_archive.html

Source: http://pubs.usgs.gov/of/2005/1231/sumstat.htm

Page 72: Data Analaytics.04. Data visualization

Process2) Visual mapping (IX)

● Maria Kozhevnikov, states that not everybody understands statistical graphs easily○ It depends on some activation patterns within the

brain

● In one of her studies, she exposed how artists, architects and scientifics interpret graphs in different ways○ The same happens with regular readers

Page 73: Data Analaytics.04. Data visualization

Process2) Visual mapping (X)

Ranking of perceptual tasks [ClevelandMcGill1985]

Page 74: Data Analaytics.04. Data visualization

Process2) Visual mapping (XI)

Remembering what Tufte said: “What are the thinking tasks

that these displays are supposed to serve?”

Page 75: Data Analaytics.04. Data visualization

Process2) Visual mapping (XII)

Compare numbers?

A bar chart (Source: http://en.wikipedia.org/wiki/Bar_chart)

Page 76: Data Analaytics.04. Data visualization

Process2) Visual mapping (XIII)

Compare numbers?

Source: http://www.improving-visualisation.org/img_uploads/2009-03-09_Mon/200939171254.jpg

?

Page 77: Data Analaytics.04. Data visualization

Process2) Visual mapping (XIV)

Temporal variance of a magnitude?

A line chart (Source: http://en.wikipedia.org/wiki/Line_graph)

Page 78: Data Analaytics.04. Data visualization

Process2) Visual mapping (XV)

Correlation among two variables?

A scatter plot(Source: http://en.wikipedia.org/wiki/Scatter_plot)

Page 79: Data Analaytics.04. Data visualization

Process2) Visual mapping (XVI)

Difference between two variables?

As Cleveland and McGill states, our brain has problems comparing angles, curves and directions → if we want to show the difference, we must represent

directly the difference

or

Page 80: Data Analaytics.04. Data visualization

Process2) Visual mapping (XVII)

Source: http://www.excelcharts.com/blog/uncommon-knowledge-about-pie-charts/#prettyPhoto[gallery]/0/

Page 81: Data Analaytics.04. Data visualization

Process2) Visual mapping (XVIII)

The best strategy?

Represent the same data in different ways

Page 82: Data Analaytics.04. Data visualization

Process2) Visual mapping (XIX)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi

A map

Graphics

Numeric table

Page 83: Data Analaytics.04. Data visualization

Process2) Visual mapping (XX)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi

Different visualization

configurations

Filters (zoom, search tool, select data by continent and size)

Depth search (click in the bubbles and show

more data, etc.)

Page 84: Data Analaytics.04. Data visualization

Process2) Visual mapping (XXI)

Source: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf

Page 85: Data Analaytics.04. Data visualization

Process2) Visual mapping (XXII)

Source: http://apandre.wordpress.com/dataviews/choiceofchart/

Page 86: Data Analaytics.04. Data visualization

Process2) Visual mapping (XXIII)

Source: http://apandre.wordpress.com/dataviews/choiceofchart/

Page 87: Data Analaytics.04. Data visualization

Process2) Visual mapping (XXIV)

Source: http://www.visual-literacy.org/periodic_table/periodic_table.html

Page 88: Data Analaytics.04. Data visualization

Process3) View Transformations

Classification of Visual Data Exploration Techniques [Keim2002]

Page 89: Data Analaytics.04. Data visualization

ProcessPrinciples

● Summary of Tufte’s principles○ Tell the truth

■ Graphical integrity○ Do it effectively with clarity, precision, etc.

■ Design aesthetics

“The success of a visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content”

[Tufte1983]

Page 90: Data Analaytics.04. Data visualization

ProcessPrinciples (II)

● Design aesthetics: five principles○ Above all else show the data○ Maximize the data-ink ratio, within reason○ Erase non-data ink, within reason○ Erase redundant data-ink○ Revise and edit

Page 91: Data Analaytics.04. Data visualization

ProcessPrinciples (III)

● Preattentive attributes○ Color○ Size○ Orientation○ Placement on page

or

Source: http://www.storytellingwithdata.com/2011/10/google-example-preattentive-attributes.html

Page 92: Data Analaytics.04. Data visualization

Table of contents

● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 94: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes

Problems?

Page 95: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (II)

● Multidimensionality● Lack of context and

understanding○ Are the numbers

relevant?○ What do they mean?○ How do they affect

to me?

An onion with just one layer

Page 96: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (III)

Problems?

Try to identify:

1) The biggest donor in 20082) The smallest donor in 2009

3) The variation between 2008 and 2009

4) Which region received the biggest amount of moneySource: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi

Page 97: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (IV)

● A map is not the best way to represent that data

● If I want to answer previously stated questions I must search for the relevant figures, memorize them and then compare

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi

Page 98: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (V)

Problems?

The graph tries to reveal the size of UK’s deficit (the black

box in the right side)

Does the graph helps in the contextualization?

Can we analyze data deeper?How can we compare?Know the differences?

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a96894970b-550wi

Page 99: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (VI)

Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a98d8a970b-550wi

Solution

Page 100: Data Analaytics.04. Data visualization

Mistakes in visualizationSome mistakes (VII)

Problems?

Bar values should start at zero

Source: http://www.qualitydigest.com/inside/quality-insider-article/asci-customer-satisfaction-airlines-remains-low.html

Page 101: Data Analaytics.04. Data visualization

Table of contents

● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 102: Data Analaytics.04. Data visualization

ToolsPentaho Reporting

Page 104: Data Analaytics.04. Data visualization

ToolsTableau Public

Page 105: Data Analaytics.04. Data visualization

ToolsTableau Public (II)

● Free to use● 1 GB of storage● Easy to embed in webpage● Tableau Public Premium

○ Price based on page views

Page 107: Data Analaytics.04. Data visualization

ToolsHighcharts

Page 108: Data Analaytics.04. Data visualization

ToolsR Studio

Page 109: Data Analaytics.04. Data visualization

Toolsggplot2 in R

An implementation of the Grammar of Graphics by Leland Wilkinson

“In brief, the grammar tells us that a statistical graphic is a mapping from data to aesthetic

attributes (color, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is

drawn on a specific coordinate system”

Page 110: Data Analaytics.04. Data visualization

Toolsggplot2 in R (II)

Page 112: Data Analaytics.04. Data visualization

ToolsGoogle Charts (II)

Page 114: Data Analaytics.04. Data visualization

ToolsGoogle Fusion Tables (II)

Page 115: Data Analaytics.04. Data visualization

ToolsSimile Widgets

Page 116: Data Analaytics.04. Data visualization

ToolsProcessing.js

Page 117: Data Analaytics.04. Data visualization

ToolsNodeXL

Page 118: Data Analaytics.04. Data visualization

ToolsSpotfire

Page 119: Data Analaytics.04. Data visualization

ToolsAdvizor Analyst

Page 120: Data Analaytics.04. Data visualization

ToolsDatawatch

Page 121: Data Analaytics.04. Data visualization

ToolsQlikView

Page 122: Data Analaytics.04. Data visualization

ToolsPrefuse

Page 124: Data Analaytics.04. Data visualization

Table of contents

● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard

Page 125: Data Analaytics.04. Data visualization

DashboardIntroduction

Fundamentals

PerceptionVisionColor

Principles

Techniques

RepresentationPresentationInteraction

Applications

DashboardsVisual

Analytics

Page 126: Data Analaytics.04. Data visualization

DashboardIntroduction (II)

“Most information dashboards that are used in business today fall far short of their potential”

Stephen Few

Page 127: Data Analaytics.04. Data visualization

DashboardDefinition

“A dashboard is a visual display of the most important information needed to

achieve one or more objectives; consolidated and arranged on a single

screen so the information can be monitored at a glance”

[Few2007]

Page 128: Data Analaytics.04. Data visualization

DashboardCharacteristics

● Visual displays● Display information needed to achieve specific

objectives● Fits on a single computer screen● Are used to monitor information at a glance● Have small, concise, clear, intuitive display

mechanisms● Are customized

Page 129: Data Analaytics.04. Data visualization

DashboardCategories

Role Strategic, Operational, Analytical

Type of data Quantitative, Non-quantitative

Data domain Sales, Finance, Marketing, Manufacturing, Human Resources, Learning, etc.

Type of measures Balanced Scored Cards, Six Sigma, Non-performance

Span of data Enterprise wide, Departmental, Individual

Update frequency Monthly, Weekly, Daily, Hourly, Real-time

Interactivity Static display, Interactive display

Mechanisms of display

Primarily graphical, Primarily text, Integration of graphics and text

Portal functionality Conduit to additional data. No portal functionality

Page 130: Data Analaytics.04. Data visualization

DashboardCommon mistakes

1) Exceeding the boundaries of a single screen

● Information that appears on dashboards is often fragmented in one of two ways:○ Separated into discrete screens to which one must

navigate

○ Separated into different instances of a single screen that are accesses through same form of interaction

Page 131: Data Analaytics.04. Data visualization

DashboardCommon mistakes (II)

2) Supplying inadequate context for the data

● Fail to provide adequate context to make the measures meaningful

3) Displaying excessive detail or precision

● Show unnecessary detail

4) Choosing a deficient measure

● Use of measures that fail to directly express the intended message

Page 132: Data Analaytics.04. Data visualization

DashboardCommon mistakes (III)

5) Choosing inappropiate display media

● Common problem with pie charts ;-)

6) Introducing meaningless variety

● Exhibit unnecessary variety of display media

Page 133: Data Analaytics.04. Data visualization

DashboardCommon mistakes (IV)

7) Using poorly designed display media● A legend was used to label and assign values to the slices

of the pie. This forces our eyes to bounce back and forth between the graph and the legend to glean meaning, which is a waste of time and effort when the slices could have been labeled directly.

● The order of the slices and the corresponding labels appears random. Ordering them by size would have provided useful information that could have been assimilated instantly.

● The bright colors of the pie slices produce sensory overkill. Bright colors ought to be reserved for specific data that should stand out from the rest.

Page 134: Data Analaytics.04. Data visualization

DashboardCommon mistakes (V)

8) Encoding quantitative data inaccurately

9) Arranging the data poorly

● The most important data ought to be prominent

● Data that require immediate attention ought to stand out

● Data that should be compared ought to be arranged and visually designed to encourage comparisons

Page 135: Data Analaytics.04. Data visualization

DashboardCommon mistakes (VI)

10) Highlighting important data ineffectively or not at all

● Fail to differentiate data by its importance○ Giving relatively equal prominence to everything on

the screen

11) Cluttering the display with useless decoration

● Try to look something that is not● It results in useless and distracting decoration

Page 136: Data Analaytics.04. Data visualization

DashboardCommon mistakes (VII)

12) Misusing or overusing color

● Too much color undermines its power

13) Designing an unattractive visual display

● The fundamental challenge of dashboard design is to effectively display a great deal of often disparate data in a small amount of space

Page 137: Data Analaytics.04. Data visualization

DashboardBuzz words

● Dashboards○ Presents information in a way that is easy to read and

interpret

● Key Performance Indicator○ Success or steps leading to the success of a goal

Page 138: Data Analaytics.04. Data visualization

DashboardExploratory Analytics Requirements

● The tool ideally exhibits the following characteristics:○ Provides every analytical display, interaction, and

function that might be needed by those who use it for their analytical tasks

○ Grounds the entire analytical experience in a single,

central workspace, with all displays, interactions, and functions within easy reach from there

Page 139: Data Analaytics.04. Data visualization

DashboardExploratory Analytics Requirements (II)

● The tool ideally exhibits the following characteristics:○ Supports efficient, seamless transitions from one step

to the next of the analytical process, even though the

sequence and nature of those steps cannot be anticipated

○ Doesn’t require a lot of fiddling with things to whip

them into shape to support your analytical needs

(such as having to take time to carefully position and size graphs on the screen)

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DashboardExploratory Analytics Requirements (III)

Source: http://www.perceptualedge.com/articles/visual_business_intelligence/differences_in_analytical_tools.pdf

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DashboardPresentation

● Present: to offer to view; display● Space limitations

○ Scrolling○ Overview + detail○ Distortion○ Supression○ Zoom and pan

● Time limitations○ Rapid serial visual presentation○ Eye-gaze

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DashboardInteractive data visualizations

Graphic design

Staticvisualization

Data analysis

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DashboardInteractive data visualizations (II)

Graphic design

Data analysis

Interactive design

ExploratoryData analysis

Interactivevisualization

Userinterface

design

Static visualization

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DashboardInteractive data visualizations (III)

● When is static representation not enough?○ Scale

■ Too many data points■ Too many different dimensions

○ Storytelling○ Exploration○ Learning

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DashboardInteractive data visualizations (IV)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (V)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (VI)

Pick a detail from a larger dataset to keep track of it

Source: http://en.wikipedia.org/wiki/Closest_pair_of_points_problem

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DashboardInteractive data visualizations (VII)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (VIII)

● Overcome limitations of display size● Most common technique: panning

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DashboardInteractive data visualizations (IX)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (XI)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (XII)

● Change visual variables: colors, sizes, orientation, font, shape

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DashboardInteractive data visualizations (XIII)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (XIV)

Show more or less detail: focus + context

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DashboardInteractive data visualizations (XV)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (XVI)

Filter: Show something conditionally

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DashboardInteractive data visualizations (XVII)

● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect

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DashboardInteractive data visualizations (XVIII)

Show related items: brushing and linking

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DashboardInteraction framework

● Continuous interaction● Stopped interaction● Passive interaction● Composite interaction

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DashboardInteraction framework (II)

Continuous interaction

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DashboardInteraction framework (III)

Stopped interaction

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DashboardInteraction framework (IV)

Passive interaction

Two important aspects of passive interaction:

1)  During typical use of a visualization tool, most of the user’s time is spent on passive interaction

– often involving eye movement

2)  Passive interaction does not imply a static representation

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DashboardInteraction framework (V)

Passive interaction

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DashboardInteraction framework (VI)

Composite interaction

Source: http://vis.berkeley.edu/papers/generalized_selection/

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DashboardSteps

Source: http://www.tableausoftware.com/es-es/trial/tableau-software

1. Choose metrics that matter

2. Keep it visual3. Make it interactive4. Keep it current or

don’t bother5. Make it simple to

access and use

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References[AmarStasko2005] Amar, R. A., & Stasko, J. T. (2005). Knowledge precepts for design and evaluation of information visualizations. Visualization and Computer Graphics, IEEE Transactions on, 11(4), 432-442.

[Cairo] Alberto Cairo [Online]. URL: https://twitter.com/albertocairo

[Chi2000] Chi, Ed H. "A taxonomy of visualization techniques using the data state reference model." Information Visualization, 2000. InfoVis 2000. IEEE Symposium on. IEEE, 2000.

[ClevelandMcGill1985] Cleveland, William S., and Robert McGill. "Graphical perception and graphical methods for analyzing scientific data." Science 229.4716 (1985): 828-833.

[Few2004] Few, Stephen. "Show me the numbers." Analytics Pres (2004).

[Few2007] Few, Stephen. "Dashboard confusion revisited." Perceptual Edge (2007).

[Fry] Ben Fry [Online]. URL: http://benfry.com/

[Jarvinen2013] Data visualization [Online]. URL: http://lib.tkk.fi/Lic/2013/urn100763.pdf

[Keim2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.

[Kosslyn] Kosslyn Laboratory [Online]. URL: http://isites.harvard.edu/icb/icb.do?keyword=kosslynlab&pageid=icb.page250946

[Malamed] Visual Language for Designers: Principles for Creating Graphics that People Understand [Online]. URL: http://www.amazon.com/Visual-Language-Designers-Principles-Understand/dp/1592535151

[Shneiderman1996] Shneiderman, Ben. "The eyes have it: A task by data type taxonomy for information visualizations." Visual Languages, 1996. Proceedings., IEEE Symposium on. IEEE, 1996.

[Shneiderman2002] Shneiderman, B. (2002) Inventing discovery tools: combining information visualization with data mining1. Information visualization, 1(1), 5-12.

[ThomasCook2005] J.J. Thomas and K.A. Cook, "A Visual Analytics Agenda," IEEE Computer Graphics & Applications, vol. 26, pp. 10-13, 2006.

[Verbert2014a] Visual Analytics [Online]. URL: http://www.slideshare.net/kverbert/in-34471961

[Yau] Nathan Yau [Online]. URL: http://flowingdata.com/about-nathan/

[Zachary2013] Zachary, W., Rosoff, A., Miller, L. C., & Read, S. J. (2013). Context as a Cognitive Process: An Integrative Framework for Supporting Decision Making. Paper presented at the STIDS.

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CoursesKU Leuven [Online]. URL: http://ariadne.cs.kuleuven.be/wiki/index.php/MM-Course1314

Berkeley [Online]. URL: http://blogs.ischool.berkeley.edu/i247s13/

Columbia university [Online]. URL: http://columbiadataviz.wordpress.com/student-work/

Information Visualization MOOC [Online]. URL: http://ivmooc.cns.iu.edu/

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Additional resourceshttp://infosthetics.com/

http://visualizing.org

http://www.visualcomplexity.com/vc/

http://visual.ly/

http://flowingdata.com

http://www.infovis-wiki.net

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Data Analytics process in Learning and Academic

Analytics projects

Day 4: Data visualization

Alex Rayón [email protected]

DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain

www.deusto.es