Data storytelling is a powerful communication tool that combines data analysis with narrative techniques to create impactful stories. It goes beyond presenting raw numbers by transforming complex data into meaningful insights that can drive decisions, influence behavior, and spark action.

When done right, data storytelling simplifies complex information, engages the audience, and compels them to act. Effective data storytelling allows UX professionals to effectively communicate the “why” behind their design choices, advocate for user-centered improvements, and ultimately create more impactful and persuasive presentations. This translates to stronger buy-in for research initiatives, increased alignment across teams, and, ultimately, products and experiences that truly meet user needs.

For instance, The New York Times’ Snow Fall data story (Figure 1) used data to immerse readers in the tale of a deadly avalanche through interactive visuals and text, while The Guardian’s The Counted (Figure 2) powerfully illustrated police violence in the U.S. by humanizing data through storytelling. These examples show that effective data storytelling can leave lasting impressions, prompting readers to think differently, act, or make informed decisions.

The importance of data storytelling lies in its ability to:

• Simplify complexity
  It makes data understandable and actionable.
• Engage and persuade
  Emotional and cognitive engagement ensures audiences not only understand but also feel compelled to act.
• Bridge gaps
  Data storytelling connects the dots between information and human experience, making the data relevant and relatable.

While there are numerous models of data storytelling, here are a few high-level areas of focus UX practitioners should have a grasp on:

Narrative Structures: Traditional storytelling models like the hero’s journey (Vogler, 1992) or the Freytag pyramid (Figure 3) provide a backbone for structuring data stories. These models help create a beginning, rising action, climax, falling action, and resolution, keeping the audience engaged.

Data Visualization: Broadly speaking, these are the tools and techniques for visualizing data in our stories. Interactive charts, maps, and infographics (Cairo, 2016) transform raw data into digestible visuals, making complex information easier to understand and remember.

Narrative Structures For Data

Moving beyond these basic structures, let’s explore how more sophisticated narrative techniques can enhance the impact of data stories:

• The Three-Act Structure
  This approach divides the data story into setup, confrontation, and resolution. It helps build context, present the problem or insight, and offer a solution or conclusion (Few, 2005).
• The Hero’s Journey (Data Edition)
  We can frame a data set as a problem that needs a hero to overcome. In this case, the hero is often the audience or the decision-maker who needs to use the data to solve a problem. The data itself becomes the journey, revealing challenges, insights, and, ultimately, a path to resolution.

Example:
Presenting data on declining user engagement could follow the hero’s journey. The “call to adventure” is the declining engagement. The “challenges” are revealed through data points showing where users are dropping off. The “insights” are uncovered through further analysis, revealing the root causes. The “resolution” is the proposed solution, supported by data, that the audience (the hero) can implement.

Problems With Widely Used Data Storytelling Models

Many data storytelling models follow a traditional, linear structure: data selection, audience tailoring, storyboarding with visuals, and a call to action. While these models aim to make data more accessible, they often fail to engage the audience on a deeper level, leading to missed opportunities. This happens because they prioritize the presentation of data over the experience of the audience, neglecting how different individuals perceive and process information.

While existing data storytelling models adhere to a structured and technically correct approach to data creation, they often fall short of fully analyzing and understanding their audience. This gap weakens their overall effectiveness and impact.

• Cognitive Overload
  Presenting too much data without context or a clear narrative overwhelms the audience. Instead of enlightenment, they experience confusion and disengagement. It’s like trying to drink from a firehose; the sheer volume becomes counterproductive. This overload can be particularly challenging for individuals with cognitive differences who may require information to be presented in smaller, more digestible chunks.
• Emotional Disconnect
  Data-heavy presentations often fail to establish an emotional connection, which is crucial for driving audience engagement and action. People are more likely to remember and act upon information that resonates with their feelings and values.
• Lack of Personalization
  Many data stories adopt a one-size-fits-all approach. Without tailoring the narrative to specific audience segments, the impact is diluted. A message that resonates with a CEO might not land with frontline employees.
• Over-Reliance on Visuals
  While visuals are essential for simplifying data, they are insufficient without a cohesive narrative to provide context and meaning, and they may not be accessible to all audience members.

These shortcomings reveal a critical flaw: while current models successfully follow a structured data creation process, they often neglect the deeper, audience-centered analysis required for actual storytelling effectiveness. To bridge this gap,

Data storytelling must evolve beyond simply presenting information — it should prioritize audience understanding, engagement, and accessibility at every stage.

“

Improving On Traditional Models

Traditional models can be improved by focusing more on the following two critical components:

Audience understanding: A greater focus can be concentrated on who the audience is, what they need, and how they perceive information. Traditional models should consider the unique characteristics and needs of specific audiences. This lack of audience understanding can lead to data stories that are irrelevant, confusing, or even misleading.

Effective data storytelling requires a deep understanding of the audience’s demographics, psychographics, and information needs. This includes understanding their level of knowledge about the topic, their prior beliefs and attitudes, and their motivations for seeking information. By tailoring the data story to a specific audience, storytellers can increase engagement, comprehension, and persuasion.

Psychological principles: These models could be improved with insights from psychology that explain how people process information and make decisions. Without these elements, even the most beautifully designed data story may fall flat. Traditional models of data storytelling can be improved with two critical components that are essential for creating impactful and persuasive narratives: audience understanding and psychological principles.

By incorporating audience understanding and psychological principles into their storytelling process, data storytellers can create more effective and engaging narratives that resonate with their audience and drive desired outcomes.

Persuasion In Data Storytelling

All storytelling involves persuasion. Even if it’s a poorly told story and your audience chooses to ignore your message, you’ve persuaded them to do that. When your audience feels that you understand them, they are more likely to be persuaded by your message. Data-driven stories that speak to their hearts and minds are more likely to drive action. You can frame your message effectively when you have a deeper understanding of your audience.

Applying Psychological Principles To Data Storytelling

Humans process information based on psychological cues such as cognitive ease, social proof, and emotional appeal. By incorporating these principles, data storytellers can make their narratives more engaging, memorable, and persuasive.

Psychological principles help data storytellers tap into how people perceive, interpret, and remember information.

The Theory of Planned Behavior

While there is no single truth when it comes to how human behavior is created or changed, it is important for a data storyteller to use a theoretical framework to ensure they address the appropriate psychological factors of their audience. The Theory of Planned Behavior (TPB) is a commonly cited theory of behavior change in academic psychology research and courses. It’s useful for creating a reasonably effective framework to collect audience data and build a data story around it.

The TPB (Ajzen 1991) (Figure 5) aims to predict and explain human behavior. It consists of three key components:

1. Attitude
   This refers to the degree to which a person has a favorable or unfavorable evaluation of the behavior in question. An example of attitudes in the TPB is a person’s belief about the importance of regular exercise for good health. If an individual strongly believes that exercise is beneficial, they are likely to have a favorable attitude toward engaging in regular physical activity.
2. Subjective Norms
   These are the perceived social pressures to perform or not perform the behavior. Keeping with the exercise example, this would be how a person thinks their family, peers, community, social media, and others perceive the importance of regular exercise for good health.
3. Perceived Behavioral Control
   This component reflects the perceived ease or difficulty of performing the behavior. For our physical activity example, does the individual believe they have access to exercise in terms of time, equipment, physical capability, and other potential aspects that make them feel more or less capable of engaging in the behavior?

As shown in Figure 5, these three components interact to create behavioral intentions, which are a proxy for actual behaviors that we often don’t have the resources to measure in real-time with research participants (Ajzen, 1991).

UX researchers and data storytellers should develop a working knowledge of the TPB or another suitable psychological theory before moving on to measure the audience’s attitudes, norms, and perceived behavioral control. We have included additional resources to support your learning about the TPB in the references section of this article.

How To Understand Your Audience And Apply Psychological Principles

OK, we’ve covered the importance of audience understanding and psychology. These two principles serve as the foundation of the proposed model of storytelling we’re putting forth. Let’s explore how to integrate them into your storytelling process.

Introducing The Audience Research Informed Data Storytelling Model (ARIDSM)

At the core of successful data storytelling lies a deep understanding of your audience’s psychology. Here’s a five-step process to integrate UX research and psychological principles effectively into your data stories:

Step 1: Define Clear Objectives

Before diving into data, it’s crucial to establish precisely what you aim to achieve with your story. Do you want to inform, persuade, or inspire action? What specific message do you want your audience to take away?

Why it matters: Defining clear objectives provides a roadmap for your storytelling journey. It ensures that your data, narrative, and visuals are all aligned toward a common goal. Without this clarity, your story risks becoming unfocused and losing its impact.

How to execute Step 1: Start by asking yourself:

• What is the core message I want to convey?
• What do I want my audience to think, feel, or do after experiencing this story?
• How will I measure the success of my data story?

Frame your objectives using action verbs and quantifiable outcomes. For example, instead of “raise awareness about climate change,” aim to “persuade 20% of the audience to adopt one sustainable practice.”

Example:
Imagine you’re creating a data story about employee burnout. Your objective might be to convince management to implement new policies that promote work-life balance, with the goal of reducing reported burnout cases by 15% within six months.

Step 2: Conduct UX Research To Understand Your Audience

This step involves gathering insights about your audience: their demographics, needs, motivations, pain points, and how they prefer to consume information.

Why it matters: Understanding your audience is fundamental to crafting a story that resonates. By knowing their preferences and potential biases, you can tailor your narrative and data presentation to capture their attention and ensure the message is clearly understood.

How to execute Step 2: Employ UX research methods like surveys, interviews, persona development, and testing the message with potential audience members.

Example:
If your data story aims to encourage healthy eating habits among college students, your research might conduct a survey of students to determine what types of attitudes exist towards specific types of healthy foods for eating, to apply that knowledge in your data story.

Step 3: Analyze and Select Relevant Audience Data

This step bridges the gap between raw data and meaningful insights. It involves exploring your data to identify patterns, trends, and key takeaways that support your objectives and resonate with your audience.

Why it matters: Careful data analysis ensures that your story is grounded in evidence and that you’re using the most impactful data points to support your narrative. This step adds credibility and weight to your story, making it more convincing and persuasive.

How to execute Step 3:

• Clean and organize your data.
  Ensure accuracy and consistency before analysis.
• Identify key variables and metrics.
  This will be determined by the psychological principle you used to inform your research. Using the TPB, we might look closely at how we measured social norms to understand directionally how the audience perceives social norms around the topic of the data story you are sharing, allowing you to frame your call to action in ways that resonate with these norms. You might run a variety of statistics at this point, including factor analysis to create groups based on similar traits, t-tests to determine if averages on your measurements are significantly different between groups, and correlations to see if there might be an assumed direction between scores on various items.

Example:
If your objective is to demonstrate the effectiveness of a new teaching method, analyzing how your audience perceives their peers to be open to adopting new methods, their belief that they are in control over the decision to use a new teaching method, and their attitude towards the effectiveness of their current teaching methods to create groups that have various levels of receptivity in trying new methods, allowing you to later tailor your data story for each group.

Step 4: Apply The Theory of Planned Behavior Or Your Psychological Principle Of Choice [Done Simultaneous With Step 3]

In this step, you will see that The Theory of Planned Behavior (TPB) provides a robust framework for understanding the factors that drive human behavior. It posits that our intentions, which are the strongest predictors of our actions, are shaped by three core components: attitudes, subjective norms, and perceived behavioral control. By consciously incorporating these elements into your data story, you can significantly enhance its persuasive power.

Why it matters: The TPB offers valuable insights into how people make decisions. By aligning your narrative with these psychological drivers, you increase the likelihood of influencing your audience’s intentions and, ultimately, their behavior. This step adds a layer of strategic persuasion to your data storytelling, making it more impactful and effective.

How to execute Step 4:

Here’s how to leverage the TPB in your data story:

Influence Attitudes: Present data and evidence that highlight the positive consequences of adopting the desired behavior. Frame the behavior as beneficial, valuable, and aligned with the audience’s values and aspirations.

This is where having a deep knowledge of the audience is helpful. Let’s imagine you are creating a data story on exercise and your call to action promoting exercise daily. If you know your audience has a highly positive attitude towards exercise, you can capitalize on that and frame your language around the benefits of exercising, increasing exercise, or specific exercises that might be best suited for the audience. It’s about framing exercise not just as a physical benefit but as a holistic improvement to their life. You can also tie it to their identity, positioning exercise as an integral part of living the kind of life they aspire to.

Shape Subjective Norms: Demonstrate that the desired behavior is widely accepted and practiced by others, especially those the audience admires or identifies with. Knowing ahead of time if your audience thinks daily exercise is something their peers approve of or engage in will allow you to shape your messaging accordingly. Highlight testimonials, success stories, or case studies from individuals who mirror the audience’s values.

If you were to find that the audience does not consider exercise to be normative amongst peers, you would look for examples of similar groups of people who do exercise. For example, if your audience is in a certain age group, you might focus on what data you have that supports a large percentage of those in their age group engaging in exercise.

Enhance Perceived Behavioral Control: Address any perceived barriers to adopting the desired behavior and provide practical solutions. For instance, when promoting daily exercise, it’s important to acknowledge the common obstacles people face — lack of time, resources, or physical capability — and demonstrate how these can be overcome.

Step 5: Craft A Balanced And Persuasive Narrative

This is where you synthesize your data, audience insights, psychological principles (including the TPB), and storytelling techniques into a compelling and persuasive narrative. It’s about weaving together the logical and emotional elements of your story to create an experience that resonates with your audience and motivates them to act.

Why it matters: A well-crafted narrative transforms data from dry statistics into a meaningful and memorable experience. It ensures that your audience not only understands the information but also feels connected to it on an emotional level, increasing the likelihood of them internalizing the message and acting upon it.

How to execute Step 5:

Structure your story strategically: Use a clear narrative arc that guides your audience through the information. Begin by establishing the context and introducing the problem, then present your data-driven insights in a way that supports your objectives and addresses the TPB components. Conclude with a compelling call to action that aligns with the attitudes, norms, and perceived control you’ve cultivated throughout the narrative.

Example:
In a data story about promoting exercise, you could:

• Determine what stories might be available using the data you have collected or obtained. In this example, let’s say you work for a city planning office and have data suggesting people aren’t currently biking as frequently as they could, even if they are bike owners.
• Begin with a relatable story about lack of exercise and its impact on people’s lives. Then, present data on the benefits of cycling, highlighting its positive impact on health, socializing, and personal feelings of well-being (attitudes).
• Integrate TPB elements: Showcase stories of people who have successfully incorporated cycling into their daily commute (subjective norms). Provide practical tips on bike safety, route planning, and finding affordable bikes (perceived behavioral control).
• Use infographics to compare commute times and costs between driving and cycling. Show maps of bike-friendly routes and visually appealing images of people enjoying cycling.
• Call to action: Encourage the audience to try cycling for a week and provide links to resources like bike share programs, cycling maps, and local cycling communities.

Evaluating The Method

Our next step is to test our hypothesis that incorporating audience research and psychology into creating a data story will lead to more powerful results. We have conducted preliminary research using messages focused on climate change, and our results suggest some support for our assertion.

We purposely chose a controversial topic because we believe data storytelling can be a powerful tool. If we want to truly realize the benefits of effective data storytelling, we need to focus on topics that matter. We also know that academic research suggests it is more difficult to shift opinions or generate behavior around topics that are polarizing (at least in the US), such as climate change.

We are not ready to share the full results of our study. We will share those in an academic journal and in conference proceedings. Here is a look at how we set up the study and how you might do something similar when either creating a data story using our method or doing your own research to test our model. You will see that it closely aligns with the model itself, with the added steps of testing the message against a control message and taking measurements of the actions the message(s) are likely to generate.

Step 1: We chose our topic and the data set we wanted to explore. As I mentioned, we purposely went with a polarizing topic. My academic background was in messaging around conservation issues, so we explored that. We used data from a publicly available data set that states July 2023 was the hottest month ever recorded.

Step 2: We identified our audience and took basic measurements. We decided our audience would be members of the general public who do not have jobs working directly with climate data or other relevant fields for climate change scientists.

We wanted a diverse range of ages and backgrounds, so we screened for this in our questions on the survey to measure the TPB components as well. We created a survey to measure the elements of the TPB as it relates to climate change and administered the survey via a Google Forms link that we shared directly, on social media posts, and in online message boards related to topics of climate change and survey research.

Step 3: We analyzed our data and broke our audience into groups based on key differences. This part required a bit of statistical know-how. Essentially, we entered all of the responses into a spreadsheet and ran a factor analysis to define groups based on shared attributes. In our case, we found two distinct groups for our respondents. We then looked deeper into the individual differences between the groups, e.g., group 1 had a notably higher level of positive attitude towards taking action to remediate climate change.

Step 4 [remember this happens simultaneously with step 3]: We incorporated aspects of the TPB in how we framed our data analysis. As we created our groups and looked at the responses to the survey, we made sure to note how this might impact the story for our various groups. Using our previous example, a group with a higher positive attitude toward taking action might need less convincing to do something about climate change and more information on what exactly they can do.

Table 1 contains examples of the questions we asked related to the TPB. We used the guidance provided here to generate the survey items to measure the TPB related to climate change activism. Note that even the academic who created the TPB states there are no standardized questions (PDF) validated to measure the concepts for each individual topic.

Table 1: Examples of questions we used to measure the TPB factors. We asked multiple questions for each factor and then generated a combined mean score for each component.

Step 5: We created data stories aligned with the groups and a control story. We created multiple stories to align with the groups we identified in our audience. We also created a control message that lacked substantial framing in any direction. See below for an example of the control data story (Figure 7) and one of the customized data stories (Figure 8) we created.

Step 6: We released the stories and took measurements of the likelihood of acting. Specific to our study, we asked the participants how likely they were to “Click here to LEARN MORE.” Our hypothesis was that individuals would express a notably higher likelihood to want to click to learn more on the data story aligned with their grouping, as compared to the competing group and the control group.

Step 7: We analyzed the differences between the preexisting groups and what they stated was their likelihood of acting. As I mentioned, our findings are still preliminary, and we are looking at ways to increase our response rate so we can present statistically substantiated findings. Our initial findings are that we do see small differences between the responses to the tailored data stories and the control data story. This is directionally what we would be expecting to see. If you are going to conduct a similar study or test out your messages, you would also be looking for results that suggest your ARIDS-derived message is more likely to generate the expected outcome than a control message or a non-tailored message.

Overall, we feel there is an exciting possibility and that future research will help us refine exactly what is critical about generating a message that will have a positive impact on your audience. We also expect there are better models of psychology to use to frame your measurements and message depending on the audience and topic.

For example, you might feel Maslow’s hierarchy of needs is more relevant to your data storytelling. You would want to take measurements related to these needs from your audience and then frame the data story using how a decision might help meet their needs.

Elevate Your Data Storytelling

Traditional models of data storytelling, while valuable, often fall short of effectively engaging and persuading audiences. This is primarily due to their neglect of crucial aspects such as audience understanding and the application of psychological principles. By incorporating these elements into the data storytelling process, we can create more impactful and persuasive narratives.

The five-step framework proposed in this article — defining clear objectives, conducting UX research, analyzing data, applying psychological principles, and crafting a balanced narrative — provides a roadmap for creating data stories that resonate with audiences on both a cognitive and emotional level. This approach ensures that data is not merely presented but is transformed into a meaningful experience that drives action and fosters change. As data storytellers, embracing this human-centric approach allows us to unlock the full potential of data and create narratives that truly inspire and inform.

Effective data storytelling isn’t a black box. You can test your data stories for effectiveness using the same research process we are using to test our hypothesis as well. While there are additional requirements in terms of time as a resource, you will make this back in the form of a stronger impact on your audience when they encounter your data story if it is shown to be significantly greater than the impact of a control message or other messages you were considering that don’t incorporate the psychological traits of your audience.

Please feel free to use our method and provide any feedback on your experience to the author.

(yk)

Illustration has been used for 10,000 years. One of the first ever recorded drawings was of a hand silhouette found in Spain, that is more than 66,000 years old. Fast forward to the introduction of the internet, around 1997, illustration has gradually increased in use. Popular examples of this are Google’s daily doodles and the Red Bull energy drink, both of which use funny cartoon illustrations and animations to great effect.

Typically, illustration was done using pencils, chalk, pens, etchings, and paints. But now everything is possible — you can do both analog and digital or mixed media styles.

As an example, although photography might be the most popular method to communicate visuals, it is not automatically the best default solution. Illustration offers a wider range of styles that help companies engage and communicate with their audience. Good illustrations create a mood and bring to life ideas and concepts from the text. To put it another way, visualisation.

Good illustrations can also help give life to information in a better way than just using text, numbers, or tables.

How do we determine what kind of illustration or style would be best? How should illustration complement or echo your corporate identity? What will your main audience prefer? What about the content, what would suit and highlight the content best, and how would it work for the age range it is primarily for?

Before we dive into the examples, let’s discuss the qualities of good illustration and the importance of understanding your audience. The rubric below will help you make good choices for your audience’s benefit.

What Makes A Good Illustration

• Visualises something from the content (something that does not exist or has been described but not visualised).
• Must be aesthetically pleasing, interesting, and stimulating to look at (needs to have qualities and harmonies between colour, elements, proportions, and subject matter).
• Must have a feel, mood, dramatic edge, or attitude (needs to create a feeling and describe or bring to life an environment).
• The illustration should enhance and bring to life what is described in text and word form.
• Explains or unpacks what is written in any surrounding text and makes it come to life in an unusual and useful way (the illustration should complement and illuminate the content so readers better understand the content).

Just look at what we are more often than not presented with.

The importance of knowing about different audiences

It is really important to know and consider different audiences. Not all of us are the same and have the same physical, cognitive, education, or resources. Our writing, designs, and illustrations need to take into account users’ make-up and capabilities.

There are some common categories of audiences:

• Child,
• Teenager,
• Middle-aged,
• Ageing,
• Prefer a certain style (goth, retro, modern, old fashioned, sporty, branded).

Below are interesting examples of illustrations, in no particular order, that show how different styles communicate and echo different qualities and affect mood and tone.

Watercolour

Good for formal, classy, and sophisticated imagery that also lends itself to imaginative expression. It is a great example of texture and light that delivers a really humane and personal feel that you would not get automatically by using software.

Strengths

• Feeling, emotion, and sense of depth and texture.

Drawing With Real-life objects

A great option for highly abstract concepts and compositions with a funny, unusual, and unreal aspect. You can do some really striking and clever stuff with this style to engage readers in your content.

Strengths

• Conceptual play.

Surreal Photomontage

Perfect for abstract hybrid illustration and photo illustration with a surreal fantasy aspect. This is a great example of merging different imagery together to create a really dramatic, scary, and visually arresting new image that fits the musician’s work as well.

Strengths

• Conceptual mixing and merging, leading to new unseen imagery.

Cartoon

Well-suited for showing fun or humorous aspects, creating concepts with loads of wit and cleverness. New messages and forms of communication can be created with this style.

Strengths

• Conceptual.

Cartoon With Block Colour

Works well for showing fun, quirky, or humorous aspects and concepts, often with loads of wit and cleverness. The simplicity of style can be quite good for people who struggle with more advanced imagery concepts, making it quite accessible.

Strengths

• Simplicity and unclutteredness.

Clean Vector

Designed for clean and clear illustrations that are all-encompassing and durable. Due to the nature of this illustration style, it works quite well for a wide range of people as it is not overly stylistic in one direction or another.

Strengths

• Realism, conceptual, and widely pleasing.

Textured Vintage Clean Vector

Best suited for imagining rustic imagery, echoing a vintage feel. This a great example of how texture and non-cleanliness can create and enhance the feeling of the imagery; it is very Western and old-fashioned, perfect for the core meaning of the illustration.

Strengths

• Aged feeling and rough impression.

Pictogram

Highly effective for clean, legible, quickly recognizable imagery and concepts, especially at small sizes as well. It is no surprise that many pictograms are to be seen in quick viewing environments such as airports and show imagery that has to work for a wide range of people.

Strengths

• Legibility, speed of comprehension (accessibility).

Abstract Geometric

A great option for visually attractive and abstract imagery and concepts. This style lends itself to much customising and experimentation from the illustrator, giving some really cool and visually striking results.

Strengths

• Visual stimulation and curiosity.

Lithography Etching

Ideal for imagery that has an old, historic, and traditional feel. Has a great feel achieved through sketchy markings, etchings, and a greyscale colour palette. You would not automatically get this from software, but given the right context or maybe an unusual juxtaposed context (like the clash against a modern, clean, fashionable corporate identity), it could work really well.

Strengths

• Realism and old tradition.

3D gradient

It serves as a great choice for highly realistic illustration with a friendly, widely accessible character element. This style is not overly stylistic and lends itself to being accepted by a wider range of people.

Strengths

• Widely acceptable and appropriate.

Sci-fi Comic Book And Pop Art

It’s especially useful for high-impact, bright, animated, and colourful concepts. Some really cool, almost animated graphic communication can be created with this style, which can also be put to much humorous use. The boldness and in-your-face style promote visual engagement.

Strengths

• Animation.

Tatoo

Well-suited for bold block-coloured silhouettes and imagery. It is so bold and impactful, and there is still loads of detail there, creating a really cool and sharp illustration. The illustration works well in black and white and would be further enhanced with colour.

Strengths

• Directness and clarity.

Pencil

Perfect for humane, detailed imagery with plenty of feeling and character. The sketchy style highlights unusual details and lends itself to an imaginative feeling and imagery.

Strengths

• Humane and detailed imaginative feeling.

Gradient

Especially useful for highly imaginative and fantasy imagery. By using gradients and a light-to-dark color palette, the imagery really has depth and says, ‘Take me away on a journey.’

Strengths

• Fantasy (through depth of colour) and clean feeling.

Charcoal

It makes an excellent option for giving illustration a humane and tangible feel, with echoes of old historical illustrations. The murky black-and-white illustration really has an atmosphere to it.

Strengths

• Humane and detailed feeling.

Woodcut

It offers great value for block silhouette imagery that has presence, sharpness, and impact. Is colour even needed? The black against the light background goes a long way to communicating the imagery.

Strengths

• Striking and clear.

Fashion

A great option for imagery that has motion and flare to it, with a slight feminine feel. No wonder this style of illustration is used for fashion illustrations, great for expressing lines and colours with motion, and has a real fashion runway flare.

Strengths

• Motion and expressive flare.

Caricature

Ideal for humorous imagery and illustration with a graphic edge and clarity. The layering of light and dark elements really creates an illustration with depth, perfect for playing with the detail of the character, not something you would automatically get from a clean vector illustration. It has received more thought and attention than clean vector illustration typically does.

Strengths

• Detail and humour.

Paint

It serves as a great choice for traditional romantic imagery that has loads of detail, texture, and depth of feeling. The rose flowers are a good example of this illustration style because they have so much detail and colour shades.

Strengths

• Tradition and emotions.

Chalk

Well-suited for highly sketchy imagery to make something an idea or working concept. The white lines against the black background have an almost animated effect and give the illustrations real movement and life. This style is a good example of using pure lines in illustration but to great effect.

Strengths

• Hand-realised and animation.

Illustration Sample Card

How To Start Doing Illustration

There are plenty of options, such as using pencils, chalk, pens, etchings, and paints, then possibly scanning in. You can also use software like Illustrator, Photoshop, Procreate, Corel Painter, Sketch, Inkscape, or Figma. But no matter what tools you choose, there’s one essential ingredient you’ll always need, and that is a mind and vision for illustration.

Recommended Resources

• Association of Illustrators
• “20 Best Illustration Agents In The UK, And The Awesome Illustrators They Represent,” Tom May
• It’s Nice That
• Behance Illustration

(yk)

For a multistep form, planning involves structuring questions logically across steps, grouping similar questions, and minimizing the number of steps and the amount of required information for each step. Whatever makes each step focused and manageable is what should be aimed for.

In this tutorial, we will create a multistep form for a job application. Here are the details we are going to be requesting from the applicant at each step:

• Personal Information
  Collects applicant’s name, email, and phone number.
• Work Experience
  Collects the applicant’s most recent company, job title, and years of experience.
• Skills & Qualifications
  The applicant lists their skills and selects their highest degree.
• Review & Submit
  This step is not going to collect any information. Instead, it provides an opportunity for the applicant to go back and review the information entered in the previous steps of the form before submitting it.

You can think of structuring these questions as a digital way of getting to know somebody. You can’t meet someone for the first time and ask them about their work experience without first asking for their name.

Based on the steps we have above, this is what the body of our HTML with our form should look like. First, the main <form> element:

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->
  <!-- Step 2: Work Experience -->
  <!-- Step 3: Skills & Qualifications -->
  <!-- Step 4: Review & Submit -->
</form>

Step 1 is for filling in personal information, like the applicant’s name, email address, and phone number:

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->
  <fieldset class="step" id="step-1">
    <legend id="step1Label">Step 1: Personal Information</legend>
    <label for="name">Full Name</label>
    <input type="text" id="name" name="name" required />
    <label for="email">Email Address</label>
    <input type="email" id="email" name="email" required />
    <label for="phone">Phone Number</label>
    <input type="tel" id="phone" name="phone" required />
  </fieldset>

  <!-- Step 2: Work Experience -->
  <!-- Step 3: Skills & Qualifications -->
  <!-- Step 4: Review & Submit -->
</form>

Once the applicant completes the first step, we’ll navigate them to Step 2, focusing on their work experience so that we can collect information like their most recent company, job title, and years of experience. We’ll tack on a new <fieldset> with those inputs:

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->

  <!-- Step 2: Work Experience -->
  <fieldset class="step" id="step-2" hidden>
    <legend id="step2Label">Step 2: Work Experience</legend>
    <label for="company">Most Recent Company</label>
    <input type="text" id="company" name="company" required />
    <label for="jobTitle">Job Title</label>
    <input type="text" id="jobTitle" name="jobTitle" required />
    <label for="yearsExperience">Years of Experience</label>
    <input
      type="number"
      id="yearsExperience"
      name="yearsExperience"
      min="0"
      required
    />
  </fieldset>

  <!-- Step 3: Skills & Qualifications -->
  <!-- Step 4: Review & Submit -->
</form>

Step 3 is all about the applicant listing their skills and qualifications for the job they’re applying for:

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->
  <!-- Step 2: Work Experience -->

  <!-- Step 3: Skills & Qualifications -->
  <fieldset class="step" id="step-3" hidden>
    <legend id="step3Label">Step 3: Skills & Qualifications</legend>
    <label for="skills">Skill(s)</label>
    <textarea id="skills" name="skills" rows="4" required></textarea>
    <label for="highestDegree">Degree Obtained (Highest)</label>
    <select id="highestDegree" name="highestDegree" required>
      <option value="">Select Degree</option>
      <option value="highschool">High School Diploma</option>
      <option value="bachelor">Bachelor's Degree</option>
      <option value="master">Master's Degree</option>
      <option value="phd">Ph.D.</option>
    </select>
  </fieldset>
  <!-- Step 4: Review & Submit -->
  <fieldset class="step" id="step-4" hidden>
    <legend id="step4Label">Step 4: Review & Submit</legend>
    <p>Review your information before submitting the application.</p>
    <button type="submit">Submit Application</button>
  </fieldset>
</form>

And, finally, we’ll allow the applicant to review their information before submitting it:

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->
  <!-- Step 2: Work Experience -->
  <!-- Step 3: Skills & Qualifications -->

  <!-- Step 4: Review & Submit -->
  <fieldset class="step" id="step-4" hidden>
    <legend id="step4Label">Step 4: Review & Submit</legend>
    <p>Review your information before submitting the application.</p>
    <button type="submit">Submit Application</button>
  </fieldset>
</form>

Notice: We’ve added a hidden attribute to every fieldset element but the first one. This ensures that the user sees only the first step. Once they are done with the first step, they can proceed to fill out their work experience on the second step by clicking a navigational button. We’ll add this button later on.

Adding Styles

To keep things focused, we’re not going to be emphasizing the styles in this tutorial. What we’ll do to keep things simple is leverage the Simple.css style framework to get the form in good shape for the rest of the tutorial.

If you’re following along, we can include Simple’s styles in the document <head>:

<link rel="stylesheet" href="https://cdn.simplecss.org/simple.min.css" />

And from there, go ahead and create a style.css file with the following styles that I’ve folded up.

<details>
  <summary>View CSS</summary>
  body {
    min-height: 100vh;
    display: flex;
    align-items: center;
    justify-content: center;
  }
  main {
    padding: 0 30px;
  }
  h1 {
    font-size: 1.8rem;
    text-align: center;
  }
  .stepper {
    display: flex;
    justify-content: flex-end;
    padding-right: 10px;
  }
  form {
    box-shadow: 0px 0px 6px 2px rgba(0, 0, 0, 0.2);
    padding: 12px;
  }
  input,
  textarea,
  select {
    outline: none;
  }
  input:valid,
  textarea:valid,
  select:valid,
  input:focus:valid,
  textarea:focus:valid,
  select:focus:valid {
    border-color: green;
  }
  input:focus:invalid,
  textarea:focus:invalid,
  select:focus:invalid {
    border: 1px solid red;
  }
</details>

Form Navigation And Validation

An easy way to ruin the user experience for a multi-step form is to wait until the user gets to the last step in the form before letting them know of any error they made along the way. Each step of the form should be validated for errors before moving on to the next step, and descriptive error messages should be displayed to enable users to understand what is wrong and how to fix it.

Now, the only part of our form that is visible is the first step. To complete the form, users need to be able to navigate to the other steps. We are going to use several buttons to pull this off. The first step is going to have a Next button. The second and third steps are going to have both a Previous and a Next button, and the fourth step is going to have a Previous and a Submit button.

<form id="jobApplicationForm">
  <!-- Step 1: Personal Information -->
  <fieldset>
    <!-- ... -->
    <button type="button" class="next" onclick="nextStep()">Next</button>
  </fieldset>

  <!-- Step 2: Work Experience -->
  <fieldset>
    <!-- ... -->
    <button type="button" class="previous" onclick="previousStep()">Previous</button>
    <button type="button" class="next" onclick="nextStep()">Next</button>
  </fieldset>

  <!-- Step 3: Skills & Qualifications -->
  <fieldset>
    <!-- ... -->
    <button type="button" class="previous" onclick="previousStep()">Previous</button>
    <button type="button" class="next" onclick="nextStep()">Next</button>
  </fieldset>

  <!-- Step 4: Review & Submit -->
  <fieldset>
    <!-- ... -->
    <button type="button" class="previous" onclick="previousStep()">Previous</button>
    <button type="submit">Submit Application</button>
  </fieldset>
</form>

Notice: We’ve added onclick attributes to the Previous and Next buttons to link them to their respective JavaScript functions: previousStep() and nextStep().

The “Next” Button

The nextStep() function is linked to the Next button. Whenever the user clicks the Next button, the nextStep() function will first check to ensure that all the fields for whatever step the user is on have been filled out correctly before moving on to the next step. If the fields haven’t been filled correctly, it displays some error messages, letting the user know that they’ve done something wrong and informing them what to do to make the errors go away.

Before we go into the implementation of the nextStep function, there are certain variables we need to define because they will be needed in the function. First, we need the input fields from the DOM so we can run checks on them to make sure they are valid.

// Step 1 fields
const name = document.getElementById("name");
const email = document.getElementById("email");
const phone = document.getElementById("phone");

// Step 2 fields
const company = document.getElementById("company");
const jobTitle = document.getElementById("jobTitle");
const yearsExperience = document.getElementById("yearsExperience");

// Step 3 fields
const skills = document.getElementById("skills");
const highestDegree = document.getElementById("highestDegree");

Then, we’re going to need an array to store our error messages.

let errorMsgs = [];

Also, we would need an element in the DOM where we can insert those error messages after they’ve been generated. This element should be placed in the HTML just below the last fieldset closing tag:

<div id="errorMessages" style="color: rgb(253, 67, 67)"></div>

Add the above div to the JavaScript code using the following line:

const errorMessagesDiv = document.getElementById("errorMessages");

And finally, we need a variable to keep track of the current step.

let currentStep = 1;


Now that we have all our variables in place, here’s the implementation of the nextstep() function:

function nextStep() {
  errorMsgs = [];
  errorMessagesDiv.innerText = "";

  switch (currentStep) {
    case 1:
      addValidationErrors(name, email, phone);
      validateStep(errorMsgs);
      break;

    case 2:
      addValidationErrors(company, jobTitle, yearsExperience);
      validateStep(errorMsgs);
      break;

    case 3:
      addValidationErrors(skills, highestDegree);
      validateStep(errorMsgs);
      break;
  }
}

The moment the Next button is pressed, our code first checks which step the user is currently on, and based on this information, it validates the data for that specific step by calling the addValidationErrors() function. If there are errors, we display them. Then, the form calls the validateStep() function to verify that there are no errors before moving on to the next step. If there are errors, it prevents the user from going on to the next step.

Whenever the nextStep() function runs, the error messages are cleared first to avoid appending errors from a different step to existing errors or re-adding existing error messages when the addValidationErrors function runs. The addValidationErrors function is called for each step using the fields for that step as arguments.

Here’s how the addValidationErrors function is implemented:

function addValidationErrors(fieldOne, fieldTwo, fieldThree = undefined) {
  if (!fieldOne.checkValidity()) {
    const label = document.querySelector(`label[for="${fieldOne.id}"]`);
    errorMsgs.push(`Please Enter A Valid ${label.textContent}`);
  }

  if (!fieldTwo.checkValidity()) {
    const label = document.querySelector(`label[for="${fieldTwo.id}"]`);
    errorMsgs.push(`Please Enter A Valid ${label.textContent}`);
  }

  if (fieldThree && !fieldThree.checkValidity()) {
    const label = document.querySelector(`label[for="${fieldThree.id}"]`);
    errorMsgs.push(`Please Enter A Valid ${label.textContent}`);
  }

  if (errorMsgs.length > 0) {
    errorMessagesDiv.innerText = errorMsgs.join("n");
  }
}

This is how the validateStep() function is defined:

function validateStep(errorMsgs) {
  if (errorMsgs.length === 0) {
    showStep(currentStep + 1);
  }
}

The validateStep() function checks for errors. If there are none, it proceeds to the next step with the help of the showStep() function.

function showStep(step) {
  steps.forEach((el, index) => {
    el.hidden = index + 1 !== step;
  });
  currentStep = step;
}

The showStep() function requires the four fieldsets in the DOM. Add the following line to the top of the JavaScript code to make the fieldsets available:

const steps = document.querySelectorAll(".step");

What the showStep() function does is to go through all the fieldsets in our form and hide whatever fieldset is not equal to the one we’re navigating to. Then, it updates the currentStep variable to be equal to the step we’re navigating to.

The “Previous” Button

The previousStep() function is linked to the Previous button. Whenever the previous button is clicked, similarly to the nextStep function, the error messages are also cleared from the page, and navigation is also handled by the showStep function.

function previousStep() {
  errorMessagesDiv.innerText = "";
  showStep(currentStep - 1);
}

Whenever the showStep() function is called with “currentStep - 1” as an argument (as in this case), we go back to the previous step, while moving to the next step happens by calling the showStep() function with “currentStep + 1” as an argument (as in the case of the validateStep() function).

Improving User Experience With Visual Cues

One other way of improving the user experience for a multi-step form, is by integrating visual cues, things that will give users feedback on the process they are on. These things can include a progress indicator or a stepper to help the user know the exact step they are on.

Integrating A Stepper

To integrate a stepper into our form (sort of like this one from Material Design), the first thing we need to do is add it to the HTML just below the opening <form> tag.

<form id="jobApplicationForm">
  <div class="stepper">
    <span><span class="currentStep">1</span>/4</span>
  </div>
  <!-- ... -->
</form>

Next, we need to query the part of the stepper that will represent the current step. This is the span tag with the class name of currentStep.

const currentStepDiv = document.querySelector(".currentStep");

Now, we need to update the stepper value whenever the previous or next buttons are clicked. To do this, we need to update the showStep() function by appending the following line to it:

currentStepDiv.innerText = currentStep;

This line is added to the showStep() function because the showStep() function is responsible for navigating between steps and updating the currentStep variable. So, whenever the currentStep variable is updated, the currentStepDiv should also be updated to reflect that change.

Storing And Retrieving User Data

One major way we can improve the form’s user experience is by storing user data in the browser. Multistep forms are usually long and require users to enter a lot of information about themselves. Imagine a user filling out 95% of a form, then accidentally hitting the F5 button on their keyboard and losing all their progress. That would be a really bad experience for the user.

Using localStorage, we can store user information as soon as it is entered and retrieve it as soon as the DOM content is loaded, so users can always continue filling out their forms from wherever they left off. To add this feature to our forms, we can begin by saving the user’s information as soon as it is typed. This can be achieved using the input event.

Before adding the input event listener, get the form element from the DOM:

const form = document.getElementById("jobApplicationForm");

Now we can add the input event listener:

// Save data on each input event
form.addEventListener("input", () => {
  const formData = {
    name: document.getElementById("name").value,
    email: document.getElementById("email").value,
    phone: document.getElementById("phone").value,
    company: document.getElementById("company").value,
    jobTitle: document.getElementById("jobTitle").value,
    yearsExperience: document.getElementById("yearsExperience").value,
    skills: document.getElementById("skills").value,
    highestDegree: document.getElementById("highestDegree").value,
  };
  localStorage.setItem("formData", JSON.stringify(formData));
});

Next, we need to add some code to help us retrieve the user data once the DOM content is loaded.

window.addEventListener("DOMContentLoaded", () => {
  const savedData = JSON.parse(localStorage.getItem("formData"));
  if (savedData) {
    document.getElementById("name").value = savedData.name || "";
    document.getElementById("email").value = savedData.email || "";
    document.getElementById("phone").value = savedData.phone || "";
    document.getElementById("company").value = savedData.company || "";
    document.getElementById("jobTitle").value = savedData.jobTitle || "";
    document.getElementById("yearsExperience").value = savedData.yearsExperience || "";
    document.getElementById("skills").value = savedData.skills || "";
    document.getElementById("highestDegree").value = savedData.highestDegree || "";
  }
});

Lastly, it is good practice to remove data from localStorage as soon as it is no longer needed:

// Clear data on form submit
form.addEventListener('submit', () => {
  // Clear localStorage once the form is submitted
  localStorage.removeItem('formData');
}); 

Adding The Current Step Value To localStorage

If the user accidentally closes their browser, they should be able to return to wherever they left off. This means that the current step value also has to be saved in localStorage.

To save this value, append the following line to the showStep() function:

localStorage.setItem("storedStep", currentStep);

Now we can retrieve the current step value and return users to wherever they left off whenever the DOM content loads. Add the following code to the DOMContentLoaded handler to do so:

const storedStep = localStorage.getItem("storedStep");

if (storedStep) {
    const storedStepInt = parseInt(storedStep);
    steps.forEach((el, index) => {
      el.hidden = index + 1 !== storedStepInt;
    });
    currentStep = storedStepInt;
    currentStepDiv.innerText = currentStep;
  }

Also, do not forget to clear the current step value from localStorage when the form is submitted.

localStorage.removeItem("storedStep");

The above line should be added to the submit handler.

Wrapping Up

Creating multi-step forms can help improve user experience for complex data entry. By carefully planning out steps, implementing form validation at each step, and temporarily storing user data in the browser, you make it easier for users to complete long forms.

For the full implementation of this multi-step form, you can access the complete code on GitHub.

(gg, yk)

Good technical illustrations and data visualisations allow users and clients to, in a manner of speaking, take time out, ponder and look at the information in a really accessible and engaging way. It can obviously also help you communicate certain categories of information and data.

The aim of the article is to inspire and show you how, by using different technical illustrations and data visualisations, you can really engage and communicate with your users and do much more good for the surrounding content.

Below are interesting and not commonly seen examples of technical illustration and data visualisation, that show data and information. As you know, more commonly seen examples are bar graphs and pie charts, but there is so much more than that!

So, keep reading and looking at the following examples, and I will show you some really cool stuff.

Technology

Typically, technical illustration and data visualisations were done using paper, pens, pencils, compasses, and rulers. But now everything is possible — you can do analog and digital. Since the mainstream introduction of the internet, around 1997, data (text, numerical, symbol) has flourished, and it has become the current day gold currency. It is easy for anyone to learn who has the software or knows the coding language. And it is much easier to do technical illustrations and data visualisation than in previous years. But that does not always mean that what is done today is better than what was done before.

What Makes Data And Information Good

• It must be aesthetically pleasing, interesting, and stimulating to look at.
• It must be of value and worth the effort to read and digest the information.
• It must be easy to understand and logical.
• To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, as Vitaly Friedman in his article “Data Visualization and Infographics” has pointed out.
• It must be legible and have well-named and easy-to-understand axes and labels.
• It should help explain and show data and information in a more interesting way than if it were presented in tabular form.
• It should help explain or unpack what is written in any surrounding text and make it come to life in an unusual and useful way.
• It must be easy to compare and contrast the data against the other data.

The Importance Of Knowing About Different Audiences

There are some common categories of audiences:

• Expert,
• Intermediate,
• Beginner,
• Member of the public,
• Child,
• Teenager,
• Middle-aged,
• Ageing,
• Has some prior knowledge,
• Does not have any prior knowledge,
• Person with some kind of disability (vision, physical, hearing, mobility).

Sara Dholakia in her article “A Guide To Getting Data Visualization Right” points out the following considerations:

• The audience’s familiarity with the subject matter of your data;
• How much context they already have versus what you should provide;
• Their familiarity with various methods of data visualisation.

Just look at what we are more often than not presented with.

So, let us dive into some cool examples that you can understand and start using today that will also give your work and content a really cool edge and help it stand out and communicate better.

3D Flow Diagram

It provides a great way to show relationships and connections between items and different components, and the 3D style adds a lot to the diagram.

Card Diagram

It’s an effective way to highlight and select information or data in relation to its surrounding data and information.

Pyramid Graph

Being great at showing two categories of information and comparing them horizontally, they are an alternative to typical horizontal or vertical bar graphs.

3D Examples Of Common Graphs

They are an excellent way to enliven overused 2D pie and bar graphs. 3D examples of common graphs give a real sense of quality and depth whilst enhancing the data and information much more than 2D versions.

Sankey Flow Diagram

This diagram is a good way to show the progression and the journey of information and data and how they are connected in relation to their data value. It’s not often seen, but it’s really cool.

Stream Graph

A stream graph is a great way to show the data and how it relates to the other data — much more interesting than just using lines as is typically seen.

3D Map

It provides an excellent way to show a map in a different and more interesting form than the typically seen 2D version. It really gives the map a sense of environment, depth, and atmosphere.

Tree Map

It’s a great way to show the data spatially and how the data value relates, in terms of size, to the rest of the data.

Waterfall Chart

A waterfall chart is helpful in showing the data and how it relates in a vertical manner to the range of data values.

Doughnut Chart

It shows the data against the other data segments and also as a value within a range of data.

Lollipop Chart

A lollipop chart is an excellent method to demonstrate percentage values in a horizontal manner that also integrates the label and data value well.

Bubble Chart

It’s an effective way to illustrate data values in terms of size and sub-classification in relation to the surrounding data.

How To Start Doing Technical Illustration And Data Visualisation

There are many options available, including specialized software like Flourish, Tableau, and Klipfolio; familiar tools like Microsoft Word, Excel, or PowerPoint (with redrawing in software like Adobe Illustrator, Affinity Designer, or CorelDRAW); or learning coding languages such as D3, Three.js, P5.js, WebGL, or the Web Audio API, as Frederick O’Brien discusses in his article “Web Design Done Well: Delightful Data Visualization Examples.”

But there is one essential ingredient that you will always need, and that is a mind and vision for technical illustration and data visualisation.

Worthwhile Practitioners And Links To Look At

• Stefanie Posavec
• Yuri Engelhardt
• Giorgia Lupi
• Edward Tufte
• Information is Beautiful Awards

(yk)