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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“Perfection is achieved not when there is nothing more to add, but when there is nothing
left to take away”
Antoine de Saint-Exupery
Narrative+
Design+
Statistics
“[...] 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]
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
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
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
● 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
Introduction (IV)
● It is a way of expressing○ Like maths, music, drawing or writing
The use of computer-supported, interactive, visual
representations of abstract elements to amplify cognition
[Card1999]
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
ConceptsGeovisualization
● Geo-spatial data is special since it describes objects or phenomena that are related to a specific location in the real world
The science of analytical reasoning facilitated by
interactive visual interfaces
[ThomasCook2005]
ConceptsVisual Analytics (II)
[Keim2006]
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.”
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
ConceptsVisual Analytics (V)
Interactivevisualization
Computational analysis
Analyticalreasoning
ConceptsVisual Analytics (VI)
● Combine strengths of both human and electronic data processing [Keim2008]○ Gives a semi-automated analytical process○ Use strengths from each
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
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.
Ranking of elementary perceptual tasks [ClevelandMcGill1985]
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
Classification of Visual Data Exploration Techniques [Keim2002]
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]
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
ProcessPrinciples (III)
● Preattentive attributes○ Color○ Size○ Orientation○ Placement on page
● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
DashboardIntroduction
Fundamentals
PerceptionVisionColor
Principles
Techniques
RepresentationPresentationInteraction
Applications
DashboardsVisual
Analytics
DashboardIntroduction (II)
“Most information dashboards that are used in business today fall far short of their potential”
Stephen Few
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]
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
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
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
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
DashboardCommon mistakes (III)
5) Choosing inappropiate display media
● Common problem with pie charts ;-)
6) Introducing meaningless variety
● Exhibit unnecessary variety of display media
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.
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
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
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
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
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
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)
2. Keep it visual3. Make it interactive4. Keep it current or
don’t bother5. Make it simple to
access and use
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).
[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.
[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.
[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.