Author: vinaykjay

Design is not a science. But “not a science” isn’t the same as “completely subjective”. In fact, the critique process has brought discipline to design for centuries. For visualizations which are based on an underlying shared data set, there’s an opportunity for an additional level of rigor: to demonstrate the value of a critique through a redesign based on the same data.

Criticism through redesign may be one of the most powerful tools we have for moving the field of visualization forward. At the same time, it’s not easy, and there are many pitfalls, intellectual, practical, and social. How can we use the tool of criticism to best advantage, with awareness and respect for all involved? Here are some suggestions, which fall into three categories: maintain rigor, respect for designers, and respect for critics.

1. Maintain rigor

As with a scientific experiment, it’s important to know the reason for a redesign — what is being “measured”, in a sense. There are many possible goals for a visualization. A critic who creates a redesign should be explicit about the goal — and the fact that they may be interested in a different goal than the designer.

Second, critics must be honest about any simplifying assumptions. If a redesign shows less data than the original, that should be mentioned up front. Otherwise, there’s a danger that any perceived simplicity of a redesign is really just the result of a reduction in data.

Part of maintaining rigor is acknowledging situations where professional judgments don’t agree, and finding ways to come to an understanding. The first step is to have a conversation about the source of the disagreement. Very often it turns out that different professionals have different criteria for success for a visualization, or have different goals in mind; clarifying these is extremely useful to the field.

2. Respect the designer

All redesigns have the potential to seem adversarial, as if the critic is pointing out flaws in the designer personally, asserting their own superior skills, or even, as assigning some blame for a disaster. But it isn’t a pleasant experience. Therefore making the process more friendly for the designer is a good idea.

3. Respect the critic

Criticism is hard, as hard as design. Indeed, in established media (books, movies, music) good critics are recognized as experts in their own right. As a field, we should give the same respect to our visualization critics.

A point for designers is to keep in mind the goal of the critique process: ultimately, none of this is a personal evaluation, but instead a way for the field as a whole to improve.

Conclusion

Data visualization is still a new field. It’s already become an essential medium for journalists, scientists, and anyone else who needs to understand data. But the medium is far from understood. It’s early still, and there’s a lot of room for improvement. Therefore criticism, and redesign is an essential part of visualization criticism.

Source: https://medium.com/@hint_fm/design-and-redesign-4ab77206cf9#.7l57fdh70

Visual exploration is dealing with open-ended data-driven visualizations that needs experts like Data Scientists, and Business Intelligence analysts. Although new tools have begun to engage general managers in visual exploration. It’s exciting to try, because it often produces insights that can’t be gleaned any other way.

During this exploration we don’t know what we are looking for, these visuals tend to plot data more inclusively. In extreme cases, this kind of project may combine multiple data sets or load dynamic, real-time data into a system that updates automatically. Statistical modeling benefits from visual exploration.

Exploration also lends itself to interactivity: Managers can adjust parameters, inject new data sources, and continually revisualizes. Complex data sometimes also suits specialized and unusual visualization, such as force-directed diagrams that show how networks cluster, or topographical plots.

Skills like analytical, programming, data management, and business intelligence are more crucial than the ability to create more presentable charts .These skills are crucial for managers to help setup systems to wrangle data and create visualizations that fit their analytic goals, and therefore it mostly needs experts to create visualizations.

Source: https://hbr.org/2016/06/visualizations-that-really-work

Problems in visual confirmation arises when there is no clear and specific claim defined for ending, and having no claim to start with. One of the typical problems in taking this approach is with patternicities.

Patternicities are finding meaningful patterns in meaningless noise. Proximate cause of this is due to priming effect, in which our brain and senses are prepared to interpret stimuli according to expected model. If we fall into this trap, we land up in visual discovery. During this process, we proceed towards the claim by comparing it with our mental model. This is a random approach with a hope of finding a meaningful pattern in a meaningless noise.

Therefore, we find ourselves in investigating and exploring the problem, instead of investigating and exploiting the problem. Solution to this problem is that we have to proceed systematically by first clearly defining the claim-the visual confirmation, and the claim to start with. Then we approach this way in a continuum basis by finding the differences in between those claims.

If there is no differences then this leads to confounding situations that need to be addressed systematically as well. We have take the divergence approach by first listing out all the options that can possibly lead to our claim, We have to then test each of those claims by matching to our final claim. Now we prioritize all the options we have tested and converge to the final claim to give the final visual confirmation.

 

Source: https://www.scientificamerican.com/article/patternicity-finding-meaningful-patterns/

 

 

Data driven decisions at Walmart is more like a norm than a exception. WalmartLabs analyzes the data from the social network sites through their tweets, pins, shares, comments and so on to get retail related insights.

In an age where sharing of information has been made easy, social media is paying a vital role in creating better understanding of consumer likes through social buzz. Such social buzz typically precedes all important product launches. People are frequently expressing their views about the latest smartphone or the coolest video game to be hitting the shelf. WalmartLabs taps this social buzz and helps buyers plan their inventory and assortment.

Consider the following visualization of Sony’s Android phone Xperia Z showing a spike in social activity that helps its buyers to make smarter decision ahead of time.  Walmart’s buyers also get a sense of what they should stock online and in stores by checking out pins on Pinterest. Top pins feed into a social-media analytics dashboard for buyers. So do the reports from Twitter that engineers have created by visualizing and analyzing Twitter feeds. Buyers can see when the number of tweets on, say, gel nail polish peaked and see which colors were the most popular in which locations.

Such humongous amounts of social data are generated online, and it is crucial for retailers to transform it into meaningful information. These insights is what enables the buyers to understand the customer demands and plan their inventory accordingly.

 

Source: http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf

A good dashboard makes us think directly about metrics rather than aesthetics itself. Therefore, it should be designed to facilitate ease of use. Since the best dashboard designs work on the subconscious level, it can be hard to pinpoint exactly what makes them so effective. But if we look beyond specific techniques for creating a dashboard, we’ll see three common themes.

1. IT’S FUNCTIONAL.

A well-designed dashboard must first and foremost be functional. 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.”

Since the primary purpose of the dashboards is to clearly communicate our most important metric, it’s only logical for the design to enhance this functionality. Any design elements that hinder the objective should be discarded.

2. IT’S INTUITIVE.

As mentioned earlier, dashboards should be glanceable. In order for a dashboard to be understandable at a glance, it must be intuitive. This involves two aspects: 1) removing cognitive barriers (such as misleading pie charts, 3-D visualizations and unnecessary information) and 2) properly visualizing and labeling the metrics.

3. IT’S LIVE.

An effective, well-designed dashboard is always-on and refreshes automatically (i.e. the data doesn’t have to be manually updated). It’s easy to take this one for granted, but without live updates on the dashboard, your metrics might as well be buried in an email attachment or spreadsheet.

These common themes of KPI’s are a measurement of the result that is a consequence of a goal. As we discussed in the class call for action can also be influenced by the goal. Having this in mind and designing a dashboard with themed framework discussed above will make for a effective dashboards.

 

Source: https://www.geckoboard.com/blog/dashboard-design-what-makes-an-effective-kpi-dashboard/#.WKqIghIrLdc

1. 1. Tell the “why” and “how”

   Use a descriptive chart title and annotation that not only describe what is being measured rather also why the reader should care and how to read the chart. This will avoid misinterpretations and save time for the chart viewers.

   Example:

   Original title: MSIS degree

   Improved title with note: MSIS degree placement rate. Note: 86% of the MSIS graduates had job placements, which is the highest placement rate when compared to other programs.

   2. Highlight what’s important, tell one story

   Although it is possible to tell 100 of story using a single line chart, it makes much sense to keep the focus on just one story.

   Example: Consider this image, There are 5 products in the chart, and it is not clear what product is the story focusing on. Therefore we must highlight the line that we are focusing on to tell that particular story and keep the rest in context in the background.

   3. Do not use 3D charts

   Studies show that 3D effect reduce comprehension. The extra dimension can hide the visibility of the data, and therefore unable to understand the pattern in data.

   Example: consider this 3D effect image, as we see from the chart, most of the data are hidden, and hence are not easily understandable.

   reference: http://www.eea.europa.eu/data-and-maps/daviz/learn-more/chart-dos-and-donts#toc-0

    

Data visualization attracts lot of attention because they are a finished product, and look nice as well. However, for many companies engaged in data visualization, those final deliverables aren’t the most important benefit of data visualization. Instead, it’s the insights into the quality of their collected data that truly leads to success.

Data visualization provides 3 key insights into data:

Insight into Data #1: Is the data complete?

The most straightforward insight that visualization can give you about your data is its completeness. With a few quick charts, areas where data is missing show up as gaps or blanks on the report (called the “Swiss Cheese” effect).

In addition to learning which specific data elements are missing, visualizations can show trends of missing data. Those trends can tell a story about the data collection process and provide insight into changes necessary in the way data is gathered.

Insight into Data #2: Is the data valid?

Visualization plays a pivotal role in understanding data’s validity. By executing a quick, preliminary visualization on collected data, trends that indicate problems in the complete data can be found.

Insight into Data #3: Is the data well-organized?

Poorly organized data can be the bane of the final step of a data collection or business intelligence process. Using data organization tools from the start can help streamline later steps of the process.

During collection, the data is often organized in a way that optimizes the gathering process. However, that same organizational scheme can be a problem when the time comes to act. The data visualization process serves to highlight the organizational challenges of your data and provides insights into how it might be done better.

Source: http://www.boostlabs.com/benefit-of-data-visualization-3-crucial-insights-into-your-data/

General principals of designing effective visualization

• Affordances: In the field of design, experts speak of things having affordances – characteristics that reveal how they’re to be used. A teapot has a handle. A door that you push has a push plate. The design of an object should, in and of itself, suggest how the object should be used. The same is true of your graphs, tables, and slides. Lead your audience through your visual – make it easy on them! Provide a visual hierarchy of information, these are visual cues for your audience so they know where to direct their attention.
• Accessibility: Designs should be usable by people of diverse abilities. Example of good design by this measure are Apple products: my mother can barely send an email, but put her iPhone or iPad in her hand and it’s so intuitive that she doesn’t feel overwhelmed by the technology. Work to make your data visualizations similarly straightforward and easy to use. Don’t overcomplicate. Use text to label, introduce, explain, reinforce, highlight, recommend, and tell a story.
• Aesthetics: People perceive more aesthetic designs as easier to use than less aesthetic designs whether they are or not. Specifically, studies have shown that more aesthetic designs are perceived as easier to use, more readily accepted and used over time, promote creative thinking and problem solving, and foster positive relationships, making people more tolerant of problems with design (this is crazy, right? leverage it!). Use a pleasant color palette (personally, I tend to do everything in shades of grey with strategic, explicit use of bright blue to draw my audience’s eye). Bring a sense of visual organization to your design (preserve margins, align things visually), showing attention to detail and a general respect for your work and for your audience.

source: http://www.storytellingwithdata.com/blog/2011/07/what-makes-good-data-visualization

Artist Amy Radcliffe is exploring the relationship of emotion and smell with the Scent-ography device, an analog system designed to capture and reproduce odors. While this is a speculative device, the principles illustrated point to the power of capturing and replicating smells. Given how tightly our emotions are linked to our olfactory senses, capturing and replicating the scent of objects and places could be enormously potent in creating impactful data experiences.

As we move into the extra-visual era of data representation, it is important to remember that the goal is not simply to find the best alternative or complement to visualization. Rather, the ideal is to experience the data more richly. This means that anyone can take a data set and begin to map the parameters to different sensory modes, exploring the data and uncovering new insights. Experiencing data is what humans are evolved to do. Yet, in terms of our ability to understand and use data in meaningful ways, we have only scratched the surface. Moving past visual representation offers new opportunities to discover and communicate insights from data.

Source: https://designmind.frogdesign.com/2014/05/beyond-data-visualization-experiencing-data-senses/

The real challenge as a data scientist is to turn a beautiful visualization into something more meaningful. Every data has a compelling story behind it. Its simply a matter of presentation. To create compelling visualization, we focus less on the actual visualization and more on what’s behind it: a well crafted story.

Create a narrative: Whatever the dataset you’re visualizing, there’s a story that comes out of it. This can be as simple as the change over time- what is important to realize is that it’s not just numbers. it’s representing a point in a larger narrative. You just need to figure out exactly what that narrative is.

Every Story Needs Conflict: A compelling story hinges on conflict. There needs to be some sort of tension in the story. While that might not play out in terms of “character development” or a plot arc, there is still a way to convey this tension—that something is wrong, or broken, or being fixed. There is significance to the data beyond it simply presenting something new.

Identifying The Narrative Elements: The five main elements of a narrative are the character, setting, conflict, plot, and theme. We do not present a solution, that’s for the audience to conclude themselves

Build On Your Story: The challenge for most data storytellers, however is that they’re not working with “compelling” data. You could be working with cell phone customer data in China, or consumer behavior based on eCommerce search queries. So how do you make that into something persuasive and beautiful?

Keep It Simple, Keep It Safe: The key is in simplicity and patience. Arguably the greatest teacher of non-fiction writing, William Zinsser, had a lot to say about simplicity that apply to data visualization, notably: “writing improves in direct ratio to the number of things we keep out of it that shouldn’t be there.

Whatever data it is that you’re presenting, you have the ability to make it interesting. It’s a matter of discovering the conflict that’s within the numbers—taking the time in your analysis to decide not just what the conclusions are, but also the implications of the conflict for your audience.

Source: https://www.import.io/post/how-to-build-compelling-stories-from-your-data-sets/