This document discusses the process of data visualization. It begins with data transformation, which involves encoding values and relationships in data through visual mappings like charts, graphs, and diagrams. Next is visual mapping, where the appropriate visualization technique is selected based on the data and thinking task. Rankings of perceptual tasks can guide technique selection. Finally, view transformations allow interactive exploration of data through filtering, zooming, etc. The goal throughout is effective visual communication to support analytical thinking about evidence.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
The Future Of Data Visualization
with Gert Franke
OVERVIEW
Data visualization has become increasingly popular over the last few years. Many tools nowadays include some kind of data visualization which gives you insight in usage, the best possible way to travel, the best product offering, etc. Data visualization seems to be a powerful solution for summarising information in a world where the amount of information targeted towards us is increasing every day. But is this the holy grail for processing information? What new possibilities does visualising data provide us? What is the best possible way to present and interact with these data visualizations?
In this talk Gert Franke will briefly show where data visualization comes from, how it now influences our daily life, what the potential of data visualization is and what the future of data visualization might look like.
OBJECTIVE
Show the history, potential and future of data visualization.
TARGET AUDIENCE
People that want to understand the possibilities of interactive data visualizations.
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
The history of data visualization
The reasons why data visualization became so hot the last few years
The potential of data visualization
The things we have to be aware of when creating (interactive) data visualizations
What might the future look like with the use of data visualization
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
A deep dive in data visualization covering some handful tools like Advance excel, Tableau, Qliksense etc.
You can add more content like discussing Google API, Perception and cognition theory,some more readable formats for data visualization and its framework.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
This presentation have the concept of Big data.
Why Big data is important to the present world.
How to visualize big data.
Steps for perfect visualization.
Visualization and design principle.
Also It had a number of visualization method for big data and traditional data.
Advantage of Visualization in Big Data
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Data visualization is a complex set of processes which is like an umbrella that covers both information and scientific visualization simultaneously. We can’t ignore the benefits of data visualization for its accurate quantities, as it is easily comparable. It also lends valuable suggestion pertaining to the usage of its technique and tools. Scientifically its effectiveness lies in our brain's ability to maintain a proper balance between perception and cognition through visualization.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
The Future Of Data Visualization
with Gert Franke
OVERVIEW
Data visualization has become increasingly popular over the last few years. Many tools nowadays include some kind of data visualization which gives you insight in usage, the best possible way to travel, the best product offering, etc. Data visualization seems to be a powerful solution for summarising information in a world where the amount of information targeted towards us is increasing every day. But is this the holy grail for processing information? What new possibilities does visualising data provide us? What is the best possible way to present and interact with these data visualizations?
In this talk Gert Franke will briefly show where data visualization comes from, how it now influences our daily life, what the potential of data visualization is and what the future of data visualization might look like.
OBJECTIVE
Show the history, potential and future of data visualization.
TARGET AUDIENCE
People that want to understand the possibilities of interactive data visualizations.
FIVE THINGS AUDIENCE MEMBERS WILL LEARN
The history of data visualization
The reasons why data visualization became so hot the last few years
The potential of data visualization
The things we have to be aware of when creating (interactive) data visualizations
What might the future look like with the use of data visualization
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
A deep dive in data visualization covering some handful tools like Advance excel, Tableau, Qliksense etc.
You can add more content like discussing Google API, Perception and cognition theory,some more readable formats for data visualization and its framework.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
This presentation have the concept of Big data.
Why Big data is important to the present world.
How to visualize big data.
Steps for perfect visualization.
Visualization and design principle.
Also It had a number of visualization method for big data and traditional data.
Advantage of Visualization in Big Data
Creating a Data-Driven Organization, Crunchconf, October 2015Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Data visualization is a complex set of processes which is like an umbrella that covers both information and scientific visualization simultaneously. We can’t ignore the benefits of data visualization for its accurate quantities, as it is easily comparable. It also lends valuable suggestion pertaining to the usage of its technique and tools. Scientifically its effectiveness lies in our brain's ability to maintain a proper balance between perception and cognition through visualization.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Data Scopes - Towards transparent data research in digital humanities (Digita...Marijn Koolen
Data scopes describe the process of data gathering, cleaning and combining in digital humanities research, which is too often considered as mere preparation that is not part of research, and is mostly not described in scholarly communications. We argue that scholars need to be more aware of the intellectual effort of this process and make it more transparent
“World creation” How might we educate the citizens of the future to be thoug...Stine Ejsing-Duun
Education that focus on facts and grades does not nurture creativity and problem-solving skills. If the new generations are expected to tackle real-world problems, we need to be able to learn from practice and use theory, but also to produce new insights in the realm of the unknown. When venturing into untrodden ground, tackling emerging problems abductive reasoning as a type of reasoning that is behind introducing new ideas. However, while inductive and deductive reasoning is highly appreciated, abductive reasoning is a way of thinking often not supported in (higher) education.
Through an investigation of abductive reasoning, design as inquiry, and design thinking as approaches to pedagogy and learning, this presentation shows possibilities for nurturing creativity and critical thinking. In my talk, I will use examples from different parts of the educational system. It relates to game-based learning, design thinking and design practice.
Visualizing Healthcare Data: Information Design Best Practices (eHealth 2012 ...Stefan Popowycz
This is my eHealth 2012 presentation will focuse on the principles behind information design and how visualization best practices can be leveraged within context of healthcare data. It illustrates theory in action, by drawing specific attention to the successful public facing solution, the 2012 Canadian Hospital Reporting Project (CHRP). The CHRP tool is a pan-Canadian external facing solution with an audience of over 3000+ users; it received over 25,000 impressions in the first 24 hours, and was called by the Toronto Star as “an innovative online tool that is being heralded as the most advanced of its kind in the world.”
Explain the term "digital humanities," and how it is understood across humanities disciplines.
Describe the research journey as a partnership between researcher and library collections and staff.
List examples of the limits of classification.
Describe the implicit and explicit hierarchies that are created when gathering and analyzing data.
Distinguish between what counts as data and what does not.
Identify different data formats and how they fit into a research workflow.
INDIAN STATISTICAL INSTITUTE
Documentation Research & Training Centre
8th Mile, Mysore Road, RVCE Post
Bangalore-560 059
DRTC Seminar- 5
2014
Data Literacy
ABSTRACT
In our increasingly data-driven society, data literacy is an important civic skill which we should be developing in our society. Data is slowly but steadily forcing their way into the societies. Data literacy may seem less technical than either Computer Science or any other fields. Still we need to envisage a wide variety of tools for accessing, converting and manipulating data. These require to understand relational databases (like MS Access), data manipulation techniques, statistical software tools (like Minitab, SPSS, STATA and MS Excel) and data representation software tools (like MS PowerPoint and MS Excel). This seminar includes an introduction on data literacy, its inter-relationship with information literacy and statistical literacy. It also includes various steps for working with data followed by short demonstration of data analysis techniques by using the software STATA11.
Speaker: Jayanta Kr. Nayek
Date:29 .10.2014. Time: 2 p.m.
Venue: DRTC, ISI Bangalore.
All are cordially invited.
Seminar Coordinator
Biswanath Dutta
Ranking Buildings and Mining the Web for Popular Architectural PatternsUjwal Gadiraju
Knowledge about the reception of architectural structures is crucial for architects and urban planners. Yet obtaining such information has been a challenging and costly activity. However, with the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information
about the building’s features and characteristics (for instance, through public Linked Data). Hence, first mining (i) the popularity of buildings from the social Web and (ii) then correlating such rankings with certain features of
buildings, can provide an efficient method to identify successful architectural patterns. In this paper we propose an approach to rank buildings through the automated mining of Flickr metadata. By further correlating such rankings with
building properties described in Linked Data we are able to identify popular patterns for particular building types (airports, bridges, churches, halls, and skyscrapers). Our approach combines crowdsourcing with Web mining techniques
to establish influential factors, as well as ground truth to evaluate our rankings. Our extensive experimental results depict that methods tailored to specific structure types allow an accurate measurement of their public perception.
This is the presentation of the Juan Cruz-Benito’s PhD “On data-driven systems analyzing, supporting and enhancing users’ interaction and experience” that was defended on September 3rd, 2018 in the Faculty of Sciences at University of Salamanca Spain. This PhD was graded with the maximum qualification “Sobresaliente Cum Laude”.
A Socio-Technical Design Approach to Build Crowdsourced and Volunteered Geogr...José Pablo Gómez Barrón S.
Ph.D. dissertation defence at Technical University of Madrid (UPM).
A Socio-Technical Design Approach to Build Crowdsourced and Volunteered Geographic Information Systems (VGIS) Leveraging the Crowds and Participatory Communities for Geoinformation Management.
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1. Data Analytics process in
Learning and Academic
Analytics projects
Day 4: Data visualization
Alex Rayón Jerez
alex.rayon@deusto.es
DeustoTech Learning – Deusto Institute of Technology – University of Deusto
Avda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es
2. “Perfection is achieved not
when there is nothing more to
add, but when there is nothing
left to take away”
Antoine de Saint-Exupery
4. “[...] 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]
5. 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
6. Table of contents
● Introduction
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
7. Table of contents
● Introduction
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
8. 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/
10. 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
11. 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
12. Table of contents
● Introduction
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
13. History
Definition and characteristics
18th Century 19th Century 20th Century
Joseph Priestley
William Playfair
John Snow
Charles J. Minard
F. Nightingale
Jacques Bertin
John Tukey
Edward Tufte
Leland Wilkinson
14. History
18th Century: Joseph Priestley
Source: http://en.wikipedia.org/wiki/A_New_Chart_of_History#mediaviewer/File:A_New_Chart_of_History_color.jpg
15. History
18th 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
27. Concepts
Introduction (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
30. Concepts
Data visualization
The use of computer-supported,
interactive, visual
representations of abstract
elements to amplify cognition
[Card1999]
31. Concepts
Information 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
32. Concepts
Geovisualization
● 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/
35. Concepts
Visual 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.”
36. Concepts
Visual 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
38. Concepts
Visual Analytics (VI)
● Combine strengths of both human and
electronic data processing [Keim2008]
○ Gives a semi-automated analytical process
○ Use strengths from each
46. Table of contents
● Introduction
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
47. Process
Introduction
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
49. Process
1) Data transformation
● Encoding of value
○ Univariate data
○ Bivariate data
○ Multivariate data
● Encoding of relation
○ Lines
○ Maps and diagrams
50. Process
1) Data transformation (II)
● Encoding of value
○ Univariate data
○ Bivariate data
○ Multivariate data
● Encoding of relation
○ Lines
○ Maps and diagrams
56. Process
1) Data transformation (VIII)
● Encoding of value
○ Univariate data
○ Bivariate data
○ Multivariate data
● Encoding of relation
○ Lines
○ Maps and diagrams
57. Process
1) Data transformation (IX)
● Relation
○ A logical or natural association between two or more
things
○ Relevance of one to another
○ Connection
58. Process
1) Data transformation (X)
Source: http://www.digitaltrainingacademy.com/socialmedia/2009/06/social_networking_map.php
Social network
Lines indicate
relationship
65. Process
2) 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
66. Process
2) 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.”
67. Process
2) Visual mapping (IV)
Source: http://www.businessinsider.com/pie-charts-are-the-worst-2013-6
“Save the pies for
dessert”
(Stephen Few)
68. Process
2) Visual mapping (V)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0167631df6f7970b-550wi
69. Process
2) 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
72. Process
2) 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
77. Process
2) Visual mapping (XIV)
Temporal variance of a magnitude?
A line chart
(Source: http://en.wikipedia.org/wiki/Line_graph)
78. Process
2) Visual mapping (XV)
Correlation among two variables?
A scatter plot
(Source: http://en.wikipedia.org/wiki/Scatter_plot)
79. Process
2) 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
80. Process
2) Visual mapping (XVII)
Source: http://www.excelcharts.com/blog/uncommon-knowledge-about-pie-charts/#prettyPhoto[gallery]/0/
82. Process
2) Visual mapping (XIX)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi
A map
Graphics
Numeric
table
83. Process
2) 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.)
84. Process
2) Visual mapping (XXI)
Source: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf
89. Process
Principles
● 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]
90. Process
Principles (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
91. Process
Principles (III)
● Preattentive attributes
○ Color
○ Size
○ Orientation
○ Placement on page
or
Source: http://www.storytellingwithdata.com/2011/10/google-example-preattentive-attributes.html
92. Table of contents
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
95. Mistakes in visualization
Some 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
96. Mistakes in visualization
Some mistakes (III)
Problems?
Try to identify:
1) The biggest donor in 2008
2) 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
97. Mistakes in visualization
Some 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
98. Mistakes in visualization
Some 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
99. Mistakes in visualization
Some mistakes (VI)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a98d8a970b-550wi
Solution
100. Mistakes in visualization
Some 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
101. Table of contents
● History
● Concept
● Process
● Mistakes in visualization
● Tools
● Designing a Dashboard
109. Tools
ggplot2 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”
127. Dashboard
Definition
“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]
128. Dashboard
Characteristics
● 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
129. Dashboard
Categories
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
130. Dashboard
Common 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
131. Dashboard
Common 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
132. Dashboard
Common mistakes (III)
5) Choosing inappropiate display media
● Common problem with pie charts ;-)
6) Introducing meaningless variety
● Exhibit unnecessary variety of display media
133. Dashboard
Common 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.
134. Dashboard
Common 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
135. Dashboard
Common 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
136. Dashboard
Common 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
137. Dashboard
Buzz 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
138. Dashboard
Exploratory 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
139. Dashboard
Exploratory 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)
143. Dashboard
Interactive data visualizations (II)
Graphic
design
Data analysis
Interactive
design
Exploratory
Data analysis
Interactive
visualization
User
interface
design
Static
visualization
144. Dashboard
Interactive data visualizations (III)
● When is static representation not enough?
○ Scale
■ Too many data points
■ Too many different dimensions
○ Storytelling
○ Exploration
○ Learning
147. Dashboard
Interactive 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
163. Dashboard
Interaction 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
167. References
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[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/
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[Kosslyn] Kosslyn Laboratory [Online]. URL: http://isites.harvard.edu/icb/icb.do?keyword=kosslynlab&pageid=icb.page250946
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[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.
168. Courses
KU 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/
170. Data Analytics process in
Learning and Academic
Analytics projects
Day 4: Data visualization
Alex Rayón Jerez
alex.rayon@deusto.es
DeustoTech Learning – Deusto Institute of Technology – University of Deusto
Avda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es