4 Tips for Better Data Visualization on Mobile Devices

Demand for data visualization on mobile devices is on the rise, both in the consumer and business spaces. Whether it is stock price performance or sensor fluctuations in a production plant, data in enterprise helps in monitoring operations, optimizing processes and quickly making informed decisions. One of the challenges in providing uninterrupted access to visualized information is the drastic reduction in screen size when moving from a desktop or laptop to a tablet or a smartphone. Best practices for print or large screen graph presentations are unsuitable: chart titles, axis labels, and other graph elements on a small screen are clutter rather than useful information. Nonetheless, effective data visualization on mobile devices can be accomplished by following these recommendations.

1. Determine what your users need

Before starting graph design for a mobile screen, gather particulars about what kind of data and what kind of format should be presented so that the charts best meet your users’ needs. For example, while temperature data are typically presented on line graphs, a user who really needs to know only how today’s temperature compares to tomorrow’s forecast might be completely satisfied by a numerical representation of a few data points rather than an actual chart. Understanding how users utilize information rather than in what form they expect to see the data will give designers more freedom to explore functional solutions to the problem.

2. Reduce standard graphs to bare bones

Visualized data, when accessed on a mobile device, are typically part of an app that was built for a specific function. For example, various banking apps show user account balances, pending payments and even breakdowns of various spending categories. When a user engages with an app, she has a specific purpose which provides context to the data that will be accessed. For that reason, graph titles, axis labels and other supporting elements can usually be omitted.

3. Take advantage of mobile device capabilities

Mobile device as a data display platform is great for interactive graphs. First, screen orientation (portrait vs. landscape) affords different pros and cons for chart displays. While portrait mode may work very well for a bar chart with a few data points, landscape mode is superior for line graphs. Second, interactions with the graph offer numerous opportunities to provide the user with detailed information that he may need while avoiding clutter on the screen. For example, pinching and zooming on a line graph could show the user changes in data over different periods of time, affording a historical glimpse of the trend as well as a very close look at data over the last hour. Similarly, tapping on individual bar graphs could bring up a modal containing a precise data label for the value.

Moreover, isolating a data point from a graph could give user access to detailed information about the data, allow her to take specific actions such as forward data to a colleague, attach a note, print graph and so on. Alternatively, a drawer that contains detailed information and slides onto the screen from the side of the graph is a good way to avoid visual clutter while increasing data density and preserving more specific information. Here, the user would only need to tap on a point in the graph to access details.

4. Follow design best practices

When designing the chart for a mobile app, especially when it is meant to support business processes or tasks, it is critical that information is legible for users given their work conditions. For example, if app users work on day and night shifts, the graph design should accommodate different lighting conditions for this group. Graphs with higher contrast work best in varying light conditions. Charts that contain multiple data sets (more than one line, for instance) require even greater contrast as two lines of similar hue may be indistinguishable from one another on the device screen.

Additionally, typography should follow the information hierarchy using appropriate size and weight for the level of importance. Avoid compressed type used in print materials as it will be more difficult to read. Similarly, avoid creating interactive graph elements that are smaller than suggested by guidelines for tappable areas. Moreover, assess how modals and pop-ups that are used to supplement graphs affect the users’ ability to extract meaningful information. Conducting a usability test with the graph and measuring the Standard Usability Score (SUS) will help ensure that visualized data meets users needs effectively.

In conclusion

User research should inform design requirements for data visualization. By stripping down graphs that are typically made for print and large screen presentations to their bare bones, you will be able to reduce chart clutter and help your users to better comprehend the presented information. Taking full advantage of the interactions available on mobile devices and weighing the pros and cons of various graphing libraries available to the developers will help ensure that the graph will be interesting, engaging, and, most importantly, informative to your users.

This blog post was written with Stuart Conway and originally published here.

ROI of UX: Mozilla Support Site Redesign

Nielsen Norman Group recently shared a great case study showing return on investment of utilizing user-centered design and usability testing for a website redesign. Take-aways are:

  • Mozilla support website redesign took 560 hours (or 14 weeks)
  • Multiple UX research methods uncovered pain points and areas for improvement
  • Designs were tested as prototypes and improved based on user feedback; 7 versions were assessed during project lifecycle
  • As a result, there was a 70% decrease in support questions submitted, and
  • 80-90% of submitted questions were answered within 24 hrs, an increase from 40-60% rate before re-design

 

Visualized Data Helps Drive Decisions: A Primer on Chart Types

 

Information drives decision-making, especially in the business world. When multiple parties are engaged in collecting intelligence, synthesizing data and determining actionable items based on results, effective ways of communicating information are critical. First, it is imperative that data are collected and synthesized using appropriate procedures. Second, it is crucial that results are summarized and presented in an accessible manner. Visualized data is one of the most effective ways to communicate insights from information and spur decision-making.

The basics of graphing data as well as core design principles can help translate quantitative and qualitative data sets into vessels of brief take-away messages. The most common types of charts are bar and column graphs, as well as pie and line charts.

Bar charts allow viewers to visually compare values across multiple categories. Because these charts present data in horizontal bars, they are especially handy when the number of categories is large or the category labels are long.

  • Good use case: Revenue generated by each member of the sales team (assuming the sales team is reasonably small).
  • Bad use case: Regional temperature averages for every month of the year. A line graph would be more appropriate here. 

Column charts, like bar graphs, allow viewers to compare values across categories. Here, however, the bars are positioned vertically rather than horizontally. Column charts are particularly useful when negative and positive values need to be represented, a handful of data points are shown, and labels of categories are short.

  • Good use case: Company revenue across the four quarters of a year.
  • Bad use case: Population across every state in the US. Here, a bar graph or even a table would be more appropriate.

Pie charts should be used to represent proportional relation between several values in a single category. Here, quantitative information is converted to percent values that add up to no more and no less than 100%.

  • Good use case: Mobile device operating system breakdown in a city (e.g. 58% iOS, 36% Android, 4% Windows, 2% Other)
  • Bad use case: Average duration of calls to the Help Desk at a business. Here, a bar or column chart would be most appropriate. Here is another example of a poor pie chart use.

Line charts are best for showing changes in data over time. A line graph can contain several sets of data, allowing the reader to compare them across time, such as company profits and cost of operations. However, multiple series of data should be presented with caution; otherwise, a reader will infer a presence or absence of a relation between visualized data that does not actually exist (see excellent examples of this problem here). Research methods should inform if and when plotting multiple series of data in a line graph is appropriate.

  • Good use case: Coffee consumption rates per department at a company during 2014.
  • Bad use case: Scores on tests measuring different variables (e.g. aesthetic appeal, ease-of-use, and level of an innovation of a website) at a single point in time. Here, a column chart would be appropriate.

Although these are the four most commonly encountered charts, their variants, such as stacked column graphs, bubble charts and treemaps also exist. These alternative chart types have their own specific use cases and should be selected only after carefully considering what type of visualization is most appropriate for the research method used to gather the data, and what insights the chart must communicate. If the amount of information that must be displayed exceeds the capabilities of these standard graphs, often the best practice is to break the data down into simpler, more easily absorbed sections. In most instances, the simplicity of design and clarity of information conveyed by a carefully chosen standard graph cannot be surpassed by their more complex variants.

 

This blog post was originally published here. 

Why Software Dev Projects Fail: a Classic Reference

About 25% of software development projects fail before launch (source). For many businesses such failures can be the straw that broke the camel’s back. In his 2005 article Why Software Fails, Robert Charette discusses common factors that contribute to such poor outcomes. While some responsibility can be laid on the doorsteps of stakeholders and managers, lack of team-wide focus on user needs and user-focused requirements are also among the culprits. Although it’s been 10 years since the article was published, little has changed in how software development projects are executed. Hence, use this reference to help evangelize user-centered design and user experience practices internally and externally!

ROI of UX: Discussion Forum Redesign

I do not need to convince my colleagues of the value of user research, usability testing and overall focus on meeting user needs during product development cycle. They get it: satisfied users are good for business! Yet, stakeholders regularly demand case studies and hard numbers before opening purse strings to fund a project. As a result, I am collecting (and sharing) UX success stories to help evangelize on value of UX and to make future “battles” for funding shorter, less intense, and (fingers crossed!) extinct!


Redesign of BreastCancer.org website based on user testing lead to the following return on investment:

  • 117% increase in unique website visitors
  • 41% increase in new user registrations
  • 53% reduction in duration of registration process
  • 206% increase in number of daily posts
  • 80% decrease in number of Help Desk support cases
  • 69% decrease in Help Desk cost

These are pretty nice numbers! You can read the full report here.

 

Transferring Academic Skills to Industry

Academics are surrounded by smart, hard-working and highly skilled peers who can wax poetic on complex statistical procedures and cite authors of obscure theories. Thus, a typical graduate student or postdoc who has been submerged in such an environment occasionally wonders “When everyone else is so highly accomplished, what can I offer to an employer?” Yet, many skills that academics take for granted are highly sought after and admired in industry. Here are some examples:

Solving Problems

Researchers solve problems and through extensive training know how to look for and find answers to questions that have never been asked before. The toolkit of a researcher contains various methods for finding answers: collecting qualitative and quantitative data through experimental, observational, ethnographic, historical etc. research. For someone coming from a strong quantitative (or strong qualitative) research background I do recommend gaining at least passing familiarity with the less practiced data type collection and analysis methods.

Thinking Critically

Researchers think critically about their own process and can evaluate others’ attempts at finding answers. Researchers understand the value of and practice rigorous scientific method. Exposure to statistics has ingrained the concept of illusory correlations, that correlation does not equal causation, and other laws of looking at data.

Managing Projects

At some point in their careers researchers take a leading role in conducting experiments from start to finish. This covers experience in setting up the studies, recruiting participants, setting up the lab, materials and equipment, developing procedures, executing data collection, supervising research assistants, synthesizing and analyzing data, presenting findings to peers and so on. Just this experience of running a study alone covers a lot of the ground that a researcher in industry (e.g. User Experience or Usability Researcher) engages in on a daily basis.

Working with Data

Descriptive statistics (means, standard deviations, ranges etc.) crunches lots of numbers into a handful. Business stakeholders greatly value such summaries of data.

Inferential statistics for comparing sets of data, for assessing whether a rate of change is significant and so on are very impressive skills that can be used to inform business decisions based on data. Graduate level statistics skills are extremely impressive. Experience with neuroimaging data is an example of experience working with large and complex datasets.

Visualizing data, whether in charts or tables, is an impressive skill for telling stories with just a few numbers.

Excel knowledge is often quite sufficient; skills with SPSS, R, SAS or some other software are impressive but unnecessary unless “Data Scientist” or “Data Analyst” is the sought position.

Communicating in Writing

Academics write many papers which means they have, on average, more practice at communicating in writing than a typical job applicant who is fresh out of college. Good grammar, correct spelling, short and to the point written documents win many brownie points in industry. Researchers are often tasked with providing an executive summary of findings and recommendations for the stakeholders: this is our typical abstract in bullet points and even shorter word count.

Speaking Publicly

Lecturing in a classroom, department brownbags, conference presentations, and even Master’s thesis or dissertation defenses are all examples of public speaking experiences. The verbal presentation of ideas as well as the experience of preparing supporting materials transfer directly to client meeting presentations (of which there are always plenty!), when ideas need to be pitched, or defended.

Teaching is a great example of making complex material accessible to novices and non-experts. In the business world such skills are always handy whether during internal or client meetings.

Working in Teams // Managing Others

These days researchers rarely work in isolation. There is always an advisor, colleagues, graduate students or postdocs, research assistants and various other entities outside of the academic lab that in one form or another influence scientific inquiries. Teamwork in business is critical. Experience in supervising junior researchers (even if they are undergraduate students) help develop management skills that can be valuable as one climbs the career ladder in industry.

Working on Multiple Projects

Researchers often run several experiments in parallel; some may be in the data analysis stage, others in material preparation. Juggling multiple projects and prioritizing daily tasks is an absolute must for a researcher in industry who wants to retain her or his sanity in the busy and tight-deadline world.

Coding

Matlab, Python, R, SAS and even scripting in SPSS may not be required or even ever used on the job in industry, but these are impressive (STEM-like, if not STEM-exactly) skills.

 

These are just a few examples of skills academics possess and which can be greatly utilized in industry. I hope this list kindles inspiration!


Blog is part of series on transitioning to industry from academia

Color, Culture and User Experience

Artists and designers often talk about different emotions that colors elicit, and suggest specific use for them in mobile app and web designs. Interestingly, across cultures different colors can be associated with very distinct emotions. For example, while anger is represented by red in the Western world, it is depicted as black in Hindu culture. Love is yellow to Native Americans and blue to Africans! So, while crafting away at the next project, keep these cultural flavors in mind. David McCandless has created a handy color-culture reference!

User Research as a Business Strategy

I originally published this blog in January 2015. Instead of rewriting the same ideas in new words, I’m reposting the contents of that post and discuss why conducting user research (not market research!) is a smart business strategy.


Why user research?

Mobile app demand is at an all-time high in both the business-to-business (B2B) and business-to-enterprise (B2E) domains as companies strive for an advantage against their competition. While third-party mobile tools are emerging and might seem like a quick and good solution, they are not always the optimal mobile strategy. Such off-the-shelf solutions, even when industry or work-role specific, are developed for no particular combination of users and company. Sometimes such products meet business needs, but often times they do not. Because success of any software project critically depends on user adoption, generic mobile tools can be an unprofitable investment if they fail to support users in the context of their work. Specifically, a mobile solution must optimize operations as performed in a specific company – even a well developed software package that does not fit within the particular ecosystem of a given business will not be used.

This is particularly true of B2B and B2E domains where workers with identical job titles that are employed by two different companies operate in distinct environments using specific tools and follow disparate workflows. In fact, 60% of software development efforts produce substandard or ineffective products in large part due to lack of clear project requirements that address business and user needs. Investing in user research as part of the company’s mobile strategy, on the other hand, can greatly improve user adoption of technology, target specific business objectives, and yield returns beyond the initial investment.

Stakeholders Carry Incomplete or Incorrect Assumptions about Users

Project stakeholders often outline broad business objectives, but not the specific requirements that are truly necessary for the implementation of a mobile solution. For example, should a mobile data-entry form be in a portrait or a landscape view? Should it have an automatic save feature? Do certain fields need to be emphasized over others? Do users need to export the forms or look up old ones? While stakeholders may have answers to some questions pertaining to a particular mobile project, they often carry incomplete or wrong assumptions about the users. Lack of clear requirements for a product and relying on an opinion or an uninformed preference can jeopardize the project.

User Research Determines Specific Product Requirements

User research uncovers and formulates very specific and explicit requirements for a product. Ethnographic research or contextual inquiries yield insights into User Experience (UX) by exposing user needs, tasks and workflows, environment, tools, motivations and attitudes. For instance, UX research on a mobile data-entry form for industrial warehouse inspections might reveal that additional features beyond those originally requested by stakeholders would drive bigger business improvements than the initially defined feature set. Research may reveal that inspections would be more efficient if users could attach photos of equipment and tag them with notes and information about the visited location and pull up historical records of previous inspections. These insights allow for better formulated user personas that not only help in deciding the best solution between using an off-the-shelf package, customizing an existing product, or building a full custom application, but also focus and guide the design, and any development efforts that follow.

User research also significantly informs visual direction of a product by employing the User-Centered Design (UCD) methodology. For example, if users wear gloves and their interaction with the mobile device requires a stylus, the interactions with the interface will differ from what it would have been if the app was created for complex hand gestures. Similarly, the color palette and contrast will be greatly influenced by users’ lighting conditions. A mobile solution developed with UCD methodology improves UX by removing workflow bottlenecks, facilitating tasks, and presenting users with intuitive, user-friendly and aesthetically appealing interfaces. The likelihood of accomplishing this is greatly reduced without user research.

UX and UCD Target Business Objectives

Engaging in UX and UCD processes with industry experts like ChaiOne uncovers very particular product requirements that throw the one-size-fits-all attitude out the window and targets specific business objectives. First, expertise in an industry allows for smooth communication among all parties (e.g., stakeholders, users, researchers, designers, developers, managers, etc.) because everyone shares a common understanding of the terminology, context of the industry, its operations, business motivations, and challenges. Second, the expert possesses collective background knowledge about user needs, experiences, motivations, attitudes, workflows, user pain-points and so on. This allows a better focus in research and targeting of questions to uncover areas for improvement to the specific mobile product that is being implemented, people who will use it, and the business that the mobile app is intended to profit.

UX Yields Significant ROI

In a typical scenario, failure to conduct user research creates the risk of spending scarce time and resources to build a generic tool that fails to increase the effectiveness and efficiency of users or optimize the company’s processes, and therefore yields no sizable return on investment. In the worst case, as software failures in the enterprise illustrate, ignoring UX and UCD can significantly harm an organization by implementing a tool that negatively impacts operations, slows processes, and leaves the company behind its competition or worse.

The seriousness of such consequences is underlined by 93% of executives placing UX as one of the strategic priorities of their businesses. As mobile solutions become integral to business operations, and as the Internet of Things (IoT) and wearable technology find their ways into the B2B and B2E space, companies wishing to remain leaders in their industry must persist in developing tools that continue to optimize their business processes. Understanding the value of UX and its benefits for a business is the first sure step toward success.

Leaving Academia: Intro to Series

I had a dream of being a professor at a university, running experiments to learn about the intricacies of visual perception, teaching a course or two, and leaving a legacy of academic accomplishments and proteges. Then, as the number of academic job applications crossed over the 50, and then the 100, and then the 150 marks, my dream began to shrink. In the hopes of getting a faculty position and putting my postdoc behind, I became open to small schools in dusty corners of the country and schools that have high teaching loads, tiny research programs, high expectations for tenure and even salaries lower than my postdoc pay.

Thankfully, I was also exposed to opportunities outside of academia that seemed as intellectually challenging and engaging as a life of an academic had been. Explorations of these options revealed many exciting careers that have been overshadowed by the glamour of academia. Working in industry meant no more pressure to publish (or perish!), to secure grants and pay salaries of graduate students. Love flexible work hours? You got it! Want benefits that adjunct faculty never get? Done! Want job security? Well, it’s going away in academia too…

The reality of the shrinking academic job market is pushing more and more graduates, postdocs and researchers into industry. So, how to make that transition? I’ll share with you tips on:

  • Converting academic skills to industry
  • Going from CV to resume
  • What kind of jobs to search for
  • Where to search for jobs and company background
  • Utilizing LinkedIn and social media
  • Learning the lingo and lay of the land
  • Power of networking
  • Interviewing and negotiating

Blog is part of series on transitioning to industry from academia