Data Experience Critique Framework
ELEVATING DATA EXPERIENCES
HEURISTICS FOR
DATA EXPERIENCES
The Data Experience (DX) critique framework was developed to provide a more structured and consistent methodology for critiquing data visualization design with a human-centered focus. The DX critique framework leverages existing heuristic frameworks and human-centered design techniques, along with principles from cognitive science and accessibility. These were then extended to address unique aspects of Data Experience and Data Visualization design.
The DX critique framework is divided into separate parts covering six design pillars, which are based on stages of the DX design process.
Each assessment consists of two main steps.
Study
A set of critique-al questions to guide you in considering the strengths and weaknesses in the current DX design, through the lens of human-centered Data Experience heuristics.
DATA EXPERIENCE
DESIGN PILLARS
Activities and questions that help you to understand the current DX design, and to identify the visual elements and design choices in the current product.
Evaluate
These DX design pillars build on each other, working as a useful gauge of a product’s DX design maturity.
Products that are earlier in their DX design maturity journey will likely have weaknesses or opportunities related to the Information Architecture pillar, or even the Purpose pillar. Addressing opportunities related to those initial pillars will yield more impactful improvements to the Data Experience.
Conversely, starting out by skipping ahead to a later pillar, such as Data Representation or Visual Hierarchy, will likely lead to less impact on the DX if there are significant weaknesses in the product’s Information Architecture, or if the purpose of the product is still ill-defined.
| PILLAR 1 PURPOSE | What key business questions can be answered with this data product? | Audience
Key business question
Analytical tasks
Information needs |
| --- | --- | --- |
| PILLAR 2 INFORMATION ARCHITECTURE | How are information elements structured and organized to support user flow(s) for completing the analytical tasks? | User task flow
Content structure
Navigation
Orienting information |
| PILLAR 3 DATA REPRESENTATION | How are numbers translated into abstractions (aggregations), and visual forms?
How do these data representation choices support or hinder the purpose and analytical tasks? | Chart forms
Visual encoding attributes
Axis scale
Aggregation & level of detail
Chart-level visual elements, including gridlines, axis lines & labels, mark labels, reference lines |
| PILLAR 4 VISUAL HIERARCHY | How are content elements (data + non-data elements) visually organized and styled to communicate the flow and architecture of this product? | Typography hierarchy
Visual priority of page elements with layout position, size, color
Visual grouping of page elements with proximity, white space, alignment, enclosing containers |
| PILLAR 5 INTERACTIVITY | What can users do with the default display?
How do these interactions support or hinder the purpose and analytical tasks of this product? | Interactive functionality for exploring data, including data-driven interactions, filters, search, or other customization options
Visual cues and feedback to communicate changed state(s) |
| PILLAR 6 CONTEXT | How does additional content, visual formatting, or functionality provide supplemental information?
How do these supplemental elements support or hinder the purpose and analytical tasks for this product? | Text elements, including annotations & explanatory text
Contextual information to add meaning
Animation
Visual metaphor |
ELEVATING DATA EXPERIENCES
DATA EXPERIENCE
HEURISTICS
The DX critique framework includes 16 human-centered Data Experience heuristics.
The heuristics are grouped into 5 broader categories of DX design outcomes.
These DX design outcomes and heuristics are focused on aspects of the Data Experience from the users’ perspective, and allow us to more consistently measure strengths and weaknesses in the design of our data products.
1 The 16 heuristics were selected after reviewing heuristics and related concepts from other well-established frameworks. Most of the heuristics in our final list appear in multiple frameworks. However, a few are unique or have a slightly different definition or scope, so that they could fit better to some of the unique aspects of data products that go beyond the more general aspects of user experience.
For a detailed mapping of the DX heuristics and their sources, see appendix A.
` 10 Usability Heuristics for User Interface Design.
` Usability 101: Introduction to Usability
` The Development of Heuristics for Evaluation of Dashboard Visualizations
` Web Accessibility Quick Checklist for Designers.
` Information Architecture Heuristics: A Checklist for Critique
` User Experience Design
` Stanford Guidelines for Web Credibility
` First Principles of Interaction Design
• FINDABLE Users can easily find their way, as well as locate and discover information and functionality
| NAVIGATE | Fi1 |
|---|---|
| PAGE STRUCTURE | Fi2 |
| LOCATE | Fi3 |
• LEARNABLE
Users can easily learn how to use the system and how to interpret the information
| CONSISTENCY | Le1 |
|---|---|
| MATCH MENTAL MODELS | Le2 |
| RECOGNITION OVER RECALL | Le3 |
| FEEDBACK | Le4 |
| HELP | Le5 |
• FOCUSED & CLEAR
Users can focus on what is most important and most meaningful to their analysis
| FOCUSED & CLEAR | Users can focus on what is most important and most meaningful to their analysis |
|---|---|
| LIMIT DISTRACTION | Fc1 |
| DIRECT ATTENTION | Fc2 |
| FLEXIBILITY | Fc3 |
| PRODUCTIVITY | Fc4 |
• TRUSTED
Users trust the data and their interactions with the system
| PREDICTABILITY | Tr¹ |
|---|---|
| ERROR PREVENTION & RECOVERY | Tr² |
| CREDIBILITY | Tr³ |
• VALUABLE
Enables users to answer key business question(s) or complete analytical task
| VALUABLE | Va1 |
|---|
PILLAR 1 PURPOSE
A well-defined purpose is the foundation and starting point of Data Experience design.
All other elements of the DX design are judged against the product’s purpose, so a well-defined purpose is necessary in order to evaluate all remaining elements.
For that reason, critiquing this pillar is a little different than all of our other DX design pillars.
Critiquing this pillar requires stepping back from looking at the data product, and instead looking at how key information about the data product has been defined.
A well-defined purpose for a data product should include these elements:
` Audience
` Key business question (KBQ) or analytical task
` Follow-up questions or related tasks
` Information audience will need in order to answer the KBQ or complete the analytical task(s)
THE VIZ CANVAS
All these elements of a data product’s purpose can be documented with the Viz Canvas.
A completed Viz Canvas can then be used to critique the purpose pillar: whether the definition of the product’s purpose is specific and focused enough to proceed with evaluating the remaining Data Experience design pillars.
Note that your data product may have several target audiences. Complete a separate Viz Canvas for each audience.
| 1 AUDIENCE | |
|---|---|
| Who will use this viz? What do they already know about this topic? Are they experts or generalists? What is their data literacy level? | |
| How much time will they spend viewing or interacting with this viz? | |
| 2 PURPOSE | Why will they want to use this viz? What key business question(s) do they need to be able to answer? Are there any follow-up questions? |
| 3 OUTCOME | Why do they want to answer these questions? What decisions, activities, actions, or other changes will this viz support? Is there a threshold that determines what action should be taken?(budgets, levels of risk) |
| 4 MEDIUM | Where will they use this viz? Online vs print? Phone vs desktop vs projector? Will it be static or interactive? Will it be a self-service dashboard or a presentation? What size of screen or page will users consume this on? |
| 5 DATA | What information can help your audience answer their key question(s)? How will it help them to answer their question(s)? What data elements & source(s) could provide this information? What is the quality of the available data(completeness, life expectancy, and freshness)? |
STUDY
EVALUATE
To critique the Purpose pillar, you will need one item.
Start by reviewing the current Viz Canvas to understand the purpose of the data product as currently defined.
Review required items:
` Completed Viz Canvas
Based on the Purpose and Outcome sections of the Viz Canvas, answer these questions:
Evaluate the product’s Purpose pillar with these critique-al questions.
` What is the key business question, problem or job task?
` What follow-up questions would enable users to answer the key business question?
` Why do they want to answer these questions? What will they do with the answers?
• 1. Is the purpose focused and specific to a key business question, problem or job task?
• 2. Are the outcomes actionable or related to a specific decision or job task?
PILLAR 2 INFORMATION ARCHITECTURE
STUDY
To critique the Information Architecture pillar you will need these items.
To understand strengths, weaknesses, and opportunities in the current design, begin by reviewing the Information Architecture.
EVALUATE
Review required items
`Completed Viz Canvas with a well-defined key business question
Define the product-level structure with an Information Architecture map
Evaluate the product’s Information Architecture pillar with these critique-al questions.
` List each page in the product
Note: You may not need to consider every question for every data product. These questions are simply meant as guides to help you consider key aspects of Information Architecture from a human-centered perspective.
Describe the purpose, key business question, or main tasks for each page in 1-2 sentences For each page, are there any likely follow-up questions to the main key business question?
` Are there any navigation relationships between pages? What would trigger or cause users to navigate to the next page?
Define the page-level structure with a wireframe of each page
Each wireframe should identify:
What are the main content sections, or groups of content? What information does each section provide?
` What is the relative importance of each section? Note: some (or all) sections may have equal visual importance
Define the key task flows with a wireflow diagram
Using the page-level structure (page wireframes) and product-level structure (Information Architecture map) of the current design, describe the most common task flow(s): what steps do users need to take to answer the key question or complete the analytical task with the current design?
• 1. Flow: what steps do users need to take to answer the key question or complete analytical tasks with the current design?
• 1.1 What are the strengths and weaknesses in how this flow supports the task(s)?
• 1.2 How might it be streamlined or enhanced to better support the task and reduce cognitive friction?
• 1.3 How might it be streamlined or enhanced to help users focus on what’s most important and not be distracted by less relevant information?
• 2. Does navigation provide flexibility to move between different levels of overview, and zoom, in a way that fits well with the users’ analytical flow?
• 3. On each page: can users easily understand where they are, and what information is and isn’t available?
• 3.1 Are there clear visible cues to communicate users’ path to the current view?
• 3.2 Do similar elements look and behave consistently across screens and states?
(Orienting information, navigation menus)
• 4. Can users easily scan the page and locate information elements?
• 5. Do language and symbols (icons) match users’ language and mental models?
• 9. Is information about the data source, recency, and scope communicated clearly, accurately, and consistently?
• 8. How might elements that communicate supporting information or available functionality be integrated with users’ flow? (To minimize memory load without cluttering the view)
• 7. Are instructions or help available, in close proximity to where it is most applicable?
- Are language and symbols (icons) used consistently?
PILLAR 3 DATA REPRESENTATION
STUDY
To critique the Data Representation pillar you will need these items.
To understand strengths, weaknesses, and opportunities in the current design, begin by reviewing each chart and describing what is being visualized.
EVALUATE
Evaluate the product’s Data Representation pillar with these critique-al questions.
Note: You may not need to consider every question for every data product. These questions are simply meant as guides to help you consider key aspects of data representation from a human-centered perspective.
Visual search tasks
Use categories from the typology developed by Brehmer & Munzner:
` Lookup
` Locate
` Browse
` Explore
` Identify
` Compare
` Summarize
From: A Multi-Level Typology of Abstract Visualization Tasks
Review required items
` Completed Viz Canvas with a well-defined key business question
` Information Architecture documentation: Information Architecture map, wireframes for each page, and wireflow (page wireframes combined with task flows)
Unit of analysis
What entities or things are being measured (such as sales transactions, cardholders, widgets)?
What is being measured?
A quantitative value, such as sales amount or count of transactions. There may be a primary measure as well as supplementary measures.
Unit of aggregation
What entities or dimensions are used to group the unit of analysis (such as transaction status, region)?
Facets
Are any additional dimensions used to show more detailed facets of the data?
| 1. How does the information in each chart relate to the key questions or tasks? | Va1 |
|---|---|
| 2. What are the strengths and weaknesses of the data abstraction choices (unit of aggregation, aggregation methods, supplemental facets) | |
| 2.1 Do the data abstraction choices provide valuable insight? | Va1 |
| 2.2 Is there any unnecessary detail included that adds noise or distracts from the signal? | Fc1 |
| 2.3 How could data abstraction choices potentially lead to inaccurate interpretation? | Tr2 |
| 2.4 How might the data be abstracted to better emphasize the most important pattern or attribute of the data? | Fc2 |
| 3. What are the strengths and weaknesses of the visual encoding in answering the key questions? | |
| 3.1 Are there alternative encodings that would be more effective for answering the key questions? | Va1 |
| 3.2 Is any unnecessary visual detail included that competes for attention without adding significant value? (On each data mark or on chart-level visual elements like gridlines) | Fc1 |
| 3.3 How could visual encoding choices potentially lead to inaccurate interpretation? | Tr2 |
| 3.4 How might the data be presented to more effectively prevent error or inaccurate interpretation (Visual encoding choices, axis scales, labeling) | Tr2 |
| 4. How might additional visual encodings be used to present additional information and context? | |
| 4.1 To help users gain a more nuanced or complete picture | Fc4 Tr2 |
| 4.2 To help users focus on important data points, patterns, relationships, or other insights | Fi3 Fc2 |
| 4.3 To help users make accurate comparisons, and understand scale & magnitude of numbers | Fc4 Tr2 Tr3 |
| 4.4 For charts that use more than one visual encoding attribute, does it add valuable information? Does it hinder the user's ability to perform their task in any way? | Va1 Fc4 |
| 5. What are the cognitive steps required to decode the meaning of each data point and to complete the visual search task? How might these steps be optimized for the visual search task? | Fi3 Fc4 |
| 6. How are new users supported in understanding how to decode the data representation? | |
| 6.1 How might instructions, hints, or visual cues be integrated with users' decoding or visual analysis flow? | Le3 Le2 |
| 6.2 Is visual language used in encoding and language used in text elements both internally and externally logical? (Consistently used to represent the same thing, and matching users' mental models) | Le1 Le2 |
| 7. How is excluded (filtered) and unknown data communicated to users? How is the scope of the data, specific to a page or page state, communicated? | Le4 Tr3 |
PILLAR 4 VISUAL HIERARCHY
STUDY
To critique the Visual Hierarchy pillar you will need these items.
Review required items
`Completed Viz Canvas with a well-defined key business question
To understand strengths, weaknesses, and opportunities in the current design, begin by reviewing the visual hierarchy of each page.
`Information Architecture documentation: Information Architecture map, wireframes for each page, and wireflow (page wireframes combined with task flows)
Look at each page in the current design
Do any sections, charts, or other elements of the page draw your attention first?
Look at the wireframe of each page
Rank the relative visual importance of each section in terms of the current visual hierarchy of the design (which sections draw more or less attention)?
Evaluate the product’s Visual Hierarchy pillar with these critique-al questions.
Note: Some sections may have equal visual importance
EVALUATE
Note: You may not need to consider every question for every data product. These questions are simply meant as guides to help you consider key aspects of Visual Hierarchy from a human-centered perspective.
• 1. On each page: is the relative importance of each text element visually clear?
| 1.1 Are more important text elements formatted for higher visual priority? (Page title, section headers) | Fi2 Fc2 |
|---|---|
| 1.2 Are less important text elements de-emphasized? (Axis titles, labels) | Fi2 Fc1 |
| 1.3 Are text elements with a similar function or similar level of importance formatted consistently | Le1 |
| 1.4 Is there enough contrast between different levels of the typography hierarchy for users to quickly and easily be able to discern the difference? | Fc2 |
| 1.5 How might text formatting be used to more clearly communicate visual priority? (Size, weight, color) | Fi2 Fc2 |
| 2. On each page: which sections, charts, or other elements of the viz draw attention most and least? | |
| 2.1 Do these correspond to the elements of the viz that should draw the most or least attention? (Based on the key business question and users' analytical task flow) | Fi2 Fc2 |
| 2.2 Are there any elements that could be de-emphasized to reduce visual clutter? | Fc1 |
| 2.3 Are elements with a similar level of importance formatted with consistent design treatment? | Le1 |
| 2.4 Is there enough contrast between items with a different level of importance, for users to quickly and easily be able to discern the difference? | Fc2 |
| 2.5 How might design treatments be used to more clearly communicate visual priority? (Layout position, size, color) | Fi2 Fc2 |
| 3. Which charts or elements of the viz belong to a related grouping? Are these visual relationships communicated clearly? | Fi2 Fc2 |
| 4. How might design elements be used to more clearly communicate visual relationships? (Proximity & white space, alignment, enclosing containers) | Fi2 Fc2 |
PILLAR 5 INTERACTIVITY
STUDY
To critique the Interactivity pillar you will need these items.
To understand strengths, weaknesses, and opportunities in the current design, begin by reviewing the available interactive functionality in each page, in relation to the key business question and user task flows
`Completed Viz Canvas with a well-defined key business question.
Review required items
`Information Architecture documentation: Information Architecture map, wireframes for each page, and wireflow (page wireframes combined with task flow
How does available interactive functionality fit in with users' task flow(s)
How is available interactive functionality communicated?
How is available interactive functionality triggered by users?
EVALUATE
Evaluate the product’s Interactivity pillar with these critique-al questions.
Note: You may not need to consider every question for every data product. These questions are simply meant as guides to help you consider key aspects of interactivity from a human-centered perspective.
| 1. Are the available interactive features a good fit for the audience and type of Data Experience(DX)?(Is it primarily an explanatory or exploratory DX?) | Va1 |
|---|---|
| 2. What are the strengths and weaknesses of the available interactive features? | |
| 2.1 In supporting users to answer their key question or tasks | Va1 |
| 2.2 In providing additional context to help the user correctly interpret the display | Le2Fc1 |
| 2.3 In preventing or reducing errors in insights drawn from the display | Tr2 |
| 2.4 In helping users to learn how to read, interpret, or interact with the display | Le4Le5 |
| 3. Can users easily discover and understand what interactive functionality and options are available? | Fi3Le3Tr1 |
| 4. Is similar or recurring interactive functionality presented consistently? | Le1 |
| 5. Does similar interactive functionality behave consistently?(Click vs. hover) | Le1 |
| 6. Are selections or other actions easy to reverse? Can users clear all actions to revert back to the original view? | Fc3Tr2 |
| 7. Is the scope of interactive functionality clearly communicated, both before and after it is triggered? (Does it impact one chart vs. all charts on the page?) | Le3Le4Tr1 |
| 8. How might interactive features provide users the ability to customize the display for more meaningful or advanced analysis? | Va1Fc3 |
| 9. How might interactive features be used to provide flexible and data-driven ways to zoom in and out to different levels of detail? (Within pages and between pages) | Fi1Fc1Fc3 |
| 10. How might interactive features be used to enable searching for specific data points, patterns, or relationships? | Fi3Fc2Fc4 |
| 11. If interaction requires data input, can users clearly understand how this data will be used, stored, and protected? | Tr3 |
PILLAR 6 CONTEXT
STUDY
To critique the Context pillar, you will need these items.
To understand strengths, weaknesses, and opportunities in the current design, begin by reviewing how supplemental information and visual encodings are used on each page, in relation to the key business question and user task flows.
EVALUATE
Evaluate the product’s Context pillar with these critique-al questions.
Review required items
Review required items these items.
`Completed Viz Canvas with a well-defined key business question
`Information Architecture documentation: Information Architecture map, wireframes for each page, and wireflow (page wireframes combined with task flows)
Note: You may not need to consider every question for every data product. These questions are simply meant as guides to help you consider key aspects of the contextual layer from a human-centered perspective.
How do supplemental or context-related elements fit in with the users’ task flows?
Supplemental or context-related elements could include:
` Annotations, titles, and explanatory text
` Animation
` Color
• 1. How might we spark users’ curiosity?
(With additional data elements, visual encodings, or interactions)
• 2. Could any other layers of meaning be added to improve the data experience?
(Using data elements, visual encodings, interactions)
• 3. Metaphor
• 3.1 How could metaphor be used to make the numbers more relatable or understandable?
• 4.1 Do titles and explanatory text help users to understand the available content & functionality, and how to use it?
• 3.2 Are there any visual metaphors being used that could clash with the audience’s existing mental models?
• 4. Text elements
• 4.2 How might annotations be used to help users focus on important data points, patterns, relationships, or other insights?
• 4.3 How might annotations be used to provide users more details on demand?
• 4.4 If annotations are being used, how do they help users in answering their key business question? Do they distract, or add unnecessary cognitive load?
• 5. Animation
• 5.1 If animation is used, how does it help users in understanding or interpreting the data, or changes in the data? Does animation distract, or add unnecessary cognitive load?
• 5.2 Can animation be easily controlled by users? (Started, stopped, speed control)
• 6. Supplementary color usage
• 5.3 How might sequencing or gradual build-up of narrative be used to help users focus on important data points, patterns, relationships, or other insights?
• 6.1 How is color used to help users understand how to interpret color encoding? (Labels, legends, text elements)
• 7. Is there meaningful context provided for accurate comparisons?
For understanding scale & magnitude?
For understanding complex KPIs or underlying business logic?
• 6.2 How might color be used beyond data encoding to direct attention or support users in their analytical tasks?
APPENDIX A
HEURISTIC FRAMEWORK The matrix table below shows how the 16 heuristics and 5 categories from Visa’s Data Experience Critique Framework map to heuristics from
other heuristic frameworks. This matrix is meant to show general overlap of concepts. All frameworks are listed in the references section (on MAPPING
the next page), which can be consulted for more detailed definitions of each heuristic.
| VISA DATA EXPERIENCE CRITIQUE FRAMEWORK | USABILITY HEURISTICS¹ | QUALITIES OF LUX AND USABILITY² | DASHBOARD HEURISTICS³ | WEB ACCESSIBILITY CHECKLIST⁴ | INFORMATION ARCHITECTURE HEURISTICS⁵ | USER EXPERIENCE HONEYCOMB⁶ | GUIDELINES FOR WEB CREDIBILITY⁷ | INTERACTION DESIGN PRINCIPLES⁸ |
|---|---|---|---|---|---|---|---|---|
| FINDABLE | Findable Communicative | Findable | Discoverability | |||||
| NAVIGATE | Navigation and wayfinding | Communicative | Explorable interfaces Visible navigation | |||||
| PAGE STRUCTURE | Spatial organization | Structure and semantics | ||||||
| LOCATE | Orientation | Navigation and wayfinding | Discoverability | |||||
| LEARNABLE | Learnability | Clear Useful Learnable | Usable | Learnability | ||||
| CONSISTENCY | Consistency and standards | Consistency and standards | Predictability and consistency | Learnable | Consistency | |||
| MATCH MENTAL MODELS | Match between system and the real world | Match between system and the real world | Language and readability | Consistency (with user expectation) Use of metaphors | ||||
| RECOGNITION OVER RECALL | Recognition rather than recall | Recognition rather than recall | Communicative | Anticipation Discoverability Visible Navigation | ||||
| FEEDBACK | Visibility of system status | Visibility of system status | Error prevention and states | Communicative | Autonomy (keep state information up to date and within easy view) Latency reduction | |||
| HELP | Help and documentation | Recognition rather than recall | Error prevention and states | Credible | ||||
| FOCUSED & CLEAR | ||||||||
| LIMIT DISTRACTIONS | Aesthetic and minimalist design | Aesthetic and minimalist design/remove the extraneous ink | Clear | Simplify | ||||
| DIRECT ATTENTION | ||||||||
| FLEXIBILITY | Flexibility and efficiency of use User control and freedom | Flexibility and efficiency of use User control and freedom | Useful Controlable | Autonomy Explorable interfaces | ||||
| PRODUCTIVITY | Flexibility and efficiency of use Efficiency | Flexibility and efficiency of use | Useful | Usable | Anticipation Efficiency of the user Fitter's law |
APPENDIX A
| VISA DATA EXPERIENCE CRITIQUE FRAMEWORK | USABILITY HEURISTICS1 | QUALITIES OF UX AND USABILITY2 | DASHBOARD HEURISTICS3 | WEB ACCESSIBILITY CHECKLIST4 | INFORMATION ARCHITECTURE HEURISTICS5 | USER EXPERIENCE HONEYCOMB6 | GUIDELINES FOR WEB CREDIBILITY7 | INTERACTION DESIGN PRINCIPLES8 |
|---|---|---|---|---|---|---|---|---|
| TRUSTED | ||||||||
| PREDICTABILITY | Predictability and consistency | Learnable | Consistency(with user expectation) | |||||
| ERROR PREVENTION AND RECOVERY | Error preventionHelp users recognize, diagnose, and recover from errors | Errors | Error prevention and states | Controllable | Explorable Interfaces(make actions reversible)Fitts's law | |||
| CREDIBILITY | Credible | Credible | Make it easy to verify the accuracy of the information on your site Update your site's content often | State(make clear what you will store & protect the user’s information) | ||||
| VALUABLE | Utility Satisfaction | Valuable | Useful Valuable |
REFERENCES
Nielsen, J. (2020, November 15). 10 Usability Heuristics for User Interface Design. nngroup. https://www.nngroup.com/articles/ten-usability-heuristics/.
Nielsen, J. (2012, January 3). Usability 101: Introduction to Usability. nngroup. https://www.nngroup.com/articles/usability-101-introduction-to-usability/.
Dowding D, Merrill JA. (2018 Jul). The Development of Heuristics for Evaluation of Dashboard Visualizations. Appl Clin Inform. 9(3):511-518. doi: 10.1055/s-0038-1666842. Epub 2018 Jul 11. PMID: 29998455; PMCID: PMC6041119.
Nandakumar, Laura. (2019, October 24). Web Accessibility Quick Checklist for Designers. Deque. https://www.deque.com/resources/quick-accessibility-checklist for-designers/.
NOTES
Covert, A. (2020, July 30). Information Architecture Heuristics: A Checklist for Critique. abbycovert. https://abbycovert.com/ia-tools/ia-heuristics/.
Morville, P. (2004, June 21). User Experience Design. Semantic Studios. https://semanticstudios.com/user_experience_design/.
Fogg, B.J. (May 2002). Stanford Guidelines for Web Credibility. A Research Summary from the Stanford Persuasive Technology Lab. Stanford University. www.webcredibility.org/guidelines.
Tognazzini, B. (2014, March 5). First Principles of Interaction Design (Revised & Expanded). Ask TOG. https://asktog.com/atc/principles-of-interaction-design/.
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Preferred citation:
Visa, Inc. (February, 2022). Data Experience Critique Framework. Visa Data Experience Team. https://developer.visa.com/images2/visa-chart-components/Data%20Experience%20Critique%20Framework.pdf