{"id":562,"date":"2025-04-04T12:04:00","date_gmt":"2025-04-04T12:04:00","guid":{"rendered":"https:\/\/pressbooks.palni.org\/leveragingdatavisualizationtocommunicateeffectively\/?post_type=chapter&p=562"},"modified":"2025-07-07T21:33:41","modified_gmt":"2025-07-07T21:33:41","slug":"what-is-data-visualization","status":"publish","type":"chapter","link":"https:\/\/pressbooks.palni.org\/leveragingdatavisualizationtocommunicateeffectively\/chapter\/what-is-data-visualization\/","title":{"raw":"What Is Data Visualization?","rendered":"What Is Data Visualization?"},"content":{"raw":"
\r\n

Good business leaders create a vision, articulate the vision, passionately own the vision, and relentlessly drive it to completion.<\/p>\r\n

\u2014Jack Welch, former CEO of General Electric<\/p>\r\n<\/blockquote>\r\n

\r\n

What Do You Think?\u00a0<\/strong><\/p>\r\n

Callie Noble started working at the United Way in 2018, and her first assignment was building reports for Vigo County about ALICE families. ALICE is an acronym for A<\/strong>sset L<\/strong>imited, I<\/strong>ncome C<\/strong>onstrained, E<\/strong>mployed. Essentially, an ALICE family earns more than the federal poverty level but less than the basic cost of living in the county. Callie has lots of great data, and her team worked diligently to create reports but soon realized that they needed to visualize their data to connect with their social media\u2013based audiences to increase awareness of this issue. Their first report shows 2016 data. The research center shows information for 2023 and a 2024 update.<\/p>\r\n\"\"\r\n

ALICE in Vigo County (2016). United Way Agency. Link to report<\/span><\/a><\/p>\r\n \r\n\r\n\"\"\r\n

Research Center\u2014Indiana (2024). United for ALICE. Link to Research Center<\/span><\/a><\/p>\r\n

The report has lots of charts but fails to tell a story that makes the information personal to the viewer. The team sits down to brainstorm how they might make their data visualizations more effective when presented to the community through social media. Imagine you are part of the brainstorming session as you visit both sites. What are your suggestions for the team? How do you tell the story of an ALICE family?<\/p>\r\n\r\n<\/div>\r\n

Introduction<\/h1>\r\n[caption id=\"\" align=\"alignleft\" width=\"275\"]\"A Source: Campfire image by generated by Copilot\u2014Public Domain<\/a>[\/caption]\r\n

According to Al Shalloway, CEO of Net Objectives, \u201c[Data] visualizations act as a campfire<\/em> around which we gather to tell stories\u201d (Twitter, Feb 12, 2014). Probably not what you were expecting right? But you most likely have a vivid memory of a story you heard around a campfire. Does this memory trigger all kinds of senses that help you remember the story? According to statistics, 65% of the population consists of visual learners (Zopf et al., 2004).<\/p>\r\n

The setting here is a campfire, but the real art comes in the storytelling: \u201cStorytelling requires an understanding of your audience, your topic, and your goal\u201d (Dale Carnegie, 2018, p. 6). Connecting your storytelling to your audience\u2019s senses helps them remember it. In business, the real magic emerges when the user can interact with visuals, drilling deeper into the data, generating more insightful questions. Sometimes, you don\u2019t know all the questions to ask until you start \u201cplaying and analyzing\u201d the data. You can\u2019t drill deeper if the data are presented in a static manner or have barriers that hinder access. Every organization wants data-driven decisions. However, the deluge of the information age inundates us with tweets, emails, texts, direct messages (DMs), memes, news, and other data streams. Data surround us, but few know how to tell an effective story with them. So where and how do we start?<\/p>\r\n

Chapter 2 addresses the following learning objectives.<\/p>\r\n\r\n

\r\n

Learning Objectives<\/p>\r\n\r\n<\/header>\r\n

\r\n

At the end of this chapter, students should be able to:<\/p>\r\n\r\n

    \r\n \t
  1. LO 1: Evaluate a data storytelling project for effectiveness.<\/li>\r\n \t
  2. LO 2: Differentiate between effective and ineffective data visualizations.<\/li>\r\n \t
  3. LO 3: Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics.<\/li>\r\n \t
  4. LO 4: Differentiate between static, interactive, and video infographics.<\/li>\r\n \t
  5. LO 5: Design an effective infographic using a free application.<\/li>\r\n \t
  6. LO 6: Recognize bias and unethical uses of data.<\/li>\r\n<\/ol>\r\n

    Key Terms:<\/em> AI, call to action, Canva, CASS template, data smog, design thinking, Gapminder, Iliinsky\u2019s four pillars of visualization, infobesity, infographic, infoxication, interactive infographic, relative frequency, Snapp, storytelling, Venngage<\/p>\r\nDownload Chapter 2 <\/a>PowerPoint<\/a> (10 MB).<\/a>\r\n\r\nDownload Chapter 2 Student Files (34 MB).<\/a>\r\n\r\n<\/div>\r\n<\/div>\r\n

    2.1 The Art of Storytelling When Data Are Complex<\/h1>\r\n
    \r\n

    Every great design begins with an even better story.<\/p>\r\n

    \u2014Lorinda Mamo, designer<\/p>\r\n<\/blockquote>\r\n

    We live in an era where an overwhelming amount of information surrounds us, creating a vast and constantly expanding sea of facts and figures. Decision-makers often find it challenging to navigate in this environment of excessive information, commonly referred to as \u201ctoo much information\u201d (TMI). The concept of information overload and the phrase \u201cinformation explosion\u201d gained recognition as early as the 1960s. Alvin Toffler, a renowned \u201cfuturist,\u201d popularized these terms in his book Future Shock<\/em>, published in 1970.<\/p>\r\n

    With the advancement of technology, particularly the rise of HTML and the internet in the 1980s and 1990s, the channels for accessing and sharing information multiplied. From RSS feeds and email to instant messaging, tweets, blogs, and social media platforms like Facebook, the ease of creating and disseminating information led to a sense of being overwhelmed. This abundance of information often leads to distraction and loss of focus.<\/p>\r\n

    To describe the challenges faced by decision-makers in this environment, several terms emerged. Infoxication<\/em>, infobesity<\/em>, data smog<\/em>, and information anxiety<\/em> are some of the terms used to denote the combination of TMI, duplication of data, misplacement of data, and the resulting anxiety and difficulty in staying focused.<\/p>\r\n

    In summary, the exponential growth of available information, coupled with technological advancements and the proliferation of communication channels, has created a scenario where decision-makers face challenges in effectively processing and making sense of the overwhelming amount of data surrounding them.<\/p>\r\n\r\n

    When Data Find Their Voice<\/h2>\r\n

    We know complex data hold immense potential to generate efficiencies, expand markets, and empower discoveries\u2014driving software developers to find new ways of making data visualization easier to create. For example, when you upload an Excel file into Power Business Intelligence (BI), you can quickly get insights on those data with a click of a button. However, don\u2019t expect to throw lots of graphs on a slide deck and have the audience understand them as well as you do. The more complex a problem, the easier it is to leave your audience disengaged or yearning for clarity. It is at this juncture that the power of storytelling shines, offering a path through the tangle of complexity, transforming numbers and concepts into relatable \u201chuman\u201d stories.<\/p>\r\n

    Effective storytelling requires the storyteller to unravel the intricacies and nuances of the data, but they must also possess the skill to distill the \u201ccomplexity\u201d into a narrative and visualization that resonates with the audience. The purposeful weaving of visualizations with effective storytelling creates the bridge to audience understanding, but more importantly, it sets the path so the audience is willing to take a journey of understanding and discovery. Cognitive psychologist Jerome Bruner suggests, \u201cWe are 22 times more likely to remember a fact when it has been wrapped in a story\u201d (Harrison, 2015, para. 3). In the past two decades, organizations have embraced the importance of data visualization to the point where data visualization skills are required of all managers. Data are driving this change as decision-makers increasingly rely on data. Let\u2019s look at an example of how design thinking has become a tool for innovation.<\/p>\r\n\r\n\r\n[caption id=\"attachment_517\" align=\"alignleft\" width=\"126\"]\"A Source: Airplane image generated by Copilot\u2014Public Domain<\/a>[\/caption]\r\n

    At Boeing, managers of the Osprey program needed to improve the aircraft\u2019s takeoffs and landings, but every time the plane taxis off the runway or lands, the \u201csensors create a terabyte of data [and] ten takeoffs and landings produce as much data as is held in the Library of Congress\u201d (Berinato, 2016, para 2). When Ryan Kiggins (2016) presented at the Global Product Data Interoperability Summit, he shared his role at Boeing, which included \u201cdesign thinking trainer and facilitator\u201d and why design thinking should become part of an organization\u2019s culture and mindset. But why link data visualization to it?<\/p>\r\n

    Incorporating design thinking into data storytelling and data visualizations can lead to a user-centric, engaging, and memorable experience that enhances clarity, comprehension, and impact. Stories like this reinforce why storytelling is so important.<\/p>\r\n

    There are compelling reasons to combine storytelling with data visualization using design thinking. Design thinking, a concept introduced by Tim Brown (2008) in his Harvard Business Review<\/em> article, has become a widely adopted approach in various organizations to gain a deeper understanding of the user, customer, or client needs. According to Esther Han (2022) of Harvard Business School Online, \u201cDesign thinking is a mindset and approach to problem-solving and innovation anchored around human-centered design\u201d (para 5). It prioritizes<\/em> understanding the needs and experiences of users or customers (Kitch, 2023). It puts user needs at the center and provides a way to help storytellers understand their audience. The fundamental nature of design thinking lies in its five-stage process, often associated with creating a product. The five-stage process is (1) empathize, (2) define, (3) ideate, (4) prototype, and (5) test.<\/p>\r\n

    One of the key contributions of design thinking is the cultivation of empathy<\/strong>. How do you create empathy in storytelling? It starts with knowing your audience through user research, persona development, storytelling from the user\u2019s perspective, and incorporating elements that evoke emotional appeal.<\/p>\r\n

    Supporters of design thinking as part of the data visualization process are laser-focused on knowing the audience. One such supporter of design thinking is Google. According to Pferdt (2019), \u201cAt Google, design thinking helps us foster a culture of innovation, and lets our teams generate and test ideas creatively and effectively\u201d (para. 3). Google storytellers use three principles of design thinking: (1) empathy<\/strong>: know your audience; (2) expansive thinking<\/strong>: consider different perspectives; and (3) experimentation<\/strong>: practice before an audience and be willing to embrace constructive criticism and\/or feedback (Konrad, 2023). Essentially, using the three principles from Google\u2019s design thinking allows you to prototype your presentation. And keep in mind, a story that works for one audience might not work for another! And what happens when the data has to speak for itself?<\/p>\r\n\r\n

    \r\n
    \r\n\r\nData visualization is a process where complexity meets creativity, where data find their voice, and where storytelling becomes the compass.\r\n\r\n<\/div><\/blockquote>\r\n

    Start with setting up the story so data can find their voice. In the realm of data visualization, the story lies in the weave of visual elements and words to form a cohesive narrative. The challenge lies in ensuring that our visualizations can still tell a compelling story even when we are not physically present to guide the audience. Data in their raw form seldom evoke emotions or captivate attention. Unlocking the power of visualization transcends numbers and statistics, giving data their own voice. The answer lies in carefully considering the elements that make up a compelling narrative. Design thinking encompasses design choices for a specific audience<\/em> and thoughtful arrangement of visual elements to contribute to the story being told.<\/p>\r\n

    The following list conveys the compelling reasons to use design thinking when your main goal is to use data visualizations to tell a story, and you are not physically with the audience:<\/p>\r\n

    1. Contextualization<\/p>\r\n

    Example 1:<\/em> The design must be relevant to the audience and include real-life examples and ministories that annotate the text. For example, using apps like ThingLink to pop up information. (Besides text, ThingLink can also pop up video or audio files.)<\/p>\r\n

    Example 2:<\/em> Prerecorded video narrative to explain key insights, serve as a guide, and include a video description. For example, provide a link to a YouTube video to provide components of the story.<\/p>\r\n

    Example 3:<\/em> Make sure the story arc takes your audience on an emotional journey. In Linda DeLuca\u2019s (2018) article \u201cHow Story Arcs Help You Design a Better Presentation,\u201d she presents six common story arcs. DeLuca doesn\u2019t share these six as the only structures available; instead, she suggests, \u201cIt\u2019s not as important to use a particular arc, but rather to understand that stories have shapes\u201d (para. 12). So if you are not<\/strong> physically present for the storytelling, explore ways to shape<\/strong> the story arc. It might include background music, video, and short animated clips. A story arc structure connects to design thinking in the context but also in the retention and comprehension. In a story arc, the storyteller sets the context, establishes empathy, and connects with the audience. Think of the story arc as an overlay. It should create a foundation of understanding, just as design thinking emphasizes understanding the users and perspectives.<\/p>\r\n

    2. Engagement<\/p>\r\n

    Example 1:<\/em> Interactive platforms that allow the audience to explore. For example, a dashboard that allows the user to interact and learn more.<\/p>\r\n

    Example 2:<\/em> Interactive elements\u2014tooltips, filters, and linked visualizations that provide additional information.<\/p>\r\n

    Example 3:<\/em> Expansive thinking where you have existing Q&A prompts (essentially thinking of the questions the audience will ask) or have a platform like Power BI that has live query capability.<\/p>\r\n

    Example 4:<\/em> Use foreshadowing to build anticipation and suspense by gradually revealing insights or trends in data. This aspect of engagement is somewhat game-like.<\/p>\r\n

    3. Emotional connection<\/p>\r\n

    Example 1:<\/em> Use expansive thinking to embrace creativity and innovation by incorporating metaphors, analogies, or storytelling devices like anecdotes, personification of data, and symbolism (visual cues that make sense like stoplight colors or gauges).<\/p>\r\n

    Example 2:<\/em> Use design thinking principles of prototyping. For example, finding data\u2019s true voice may require multiple iterations and feedback loops, essentially testing different visualizations to refine and evolve the data\u2019s voice to connect to the audience.<\/p>\r\n

    Example 3:<\/em> Considering ethical and social implications. By integrating ethical considerations, the data\u2019s voice can become more responsible, inclusive, and accountable.<\/p>\r\n

    4. Simplification and clarity (remove ambiguity)<\/p>\r\n

    Example 1:<\/em> By fostering collaboration and diverse perspectives, design (expansive) thinking ensures a more comprehensive and better representation of the data\u2019s voice. For example, a CPA can clearly understand all aspects of ratio analysis, but a nonaccountant might struggle with the interpretation. That\u2019s why it\u2019s important that an accountant creating a data visualization considers collaborating with nonaccountants, especially if the audience is mostly nonaccountants.<\/p>\r\n

    5. Guiding attention and focus<\/p>\r\n

    Example 1:<\/em> Use design thinking to identify desired outcomes. For example, if the organization uses key performance indicators (KPIs) in current reports, use KPIs in visualizations. Use components your audience will recognize.<\/p>\r\n

    Example 2:<\/em> Use design thinking to connect to feasibility by considering current goals and strategies.<\/p>\r\n

    6. Retention and comprehension<\/p>\r\n

    Example 1:<\/em> Use design thinking to consider the personalization of the user\u2019s experience. For example, a technique that uses personalization of data-driven storytelling is possible by responding to the user\u2019s cues to guide the individual user through the narrative. This just means that the user makes a choice based on their preference and that decision drives the storytelling narrative. As a user, I gain comprehension of what I find important.<\/p>\r\n

    Example 2:<\/em> Use design thinking to test your data visualization. For example, use a pilot audience to test your data visualization where you are not physically with the data visualization. Then use eye-tracking technology or analyze user interactions and engagement patterns. This can provide insights into where users focus their attention, how they navigate through content, and where they interact with elements.<\/p>\r\n

    Example 3:<\/em> Use design thinking to test retention. For example, conduct a follow-up of your pilot audience with open-ended questions to gauge their recollection of key insights.<\/p>\r\n

    In data storytelling, the framework should start with a captivating introduction, a clear problem statement, a climax (like the most important findings or insights), supporting evidence, and a memorable conclusion that contextualizes the problem. The key thing to remember is that data storytelling is not the same thing as data visualization. Visuals are an element of the process.<\/p>\r\n

    Before we go further, let\u2019s look at a simple example to define what is meant by data, information, and insights shown in Table 2.1.<\/p>\r\n

    Table 2.1:<\/span> Examples of Drilling to Insights<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n
    \r\n

    Example of Data<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Example of Information<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Example of Insights<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    Phoenix 97 7\/1<\/p>\r\n<\/td>\r\n

    		\r\n

    On July 1st<\/sup>, the temperature will reach 97\u00b0F in Phoenix, Arizona<\/p>\r\n<\/td>\r\n

    		\r\n

    It will reach 97\u00b0F in Phoenix, Arizona on July 1st<\/sup>, which is 10\u00b0F below average compared to prior years<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

    The main components of an engaging story include plot, characters, setting, theme, conflict, point of view, narrative style, conflict resolution, pacing, and dialogue. It is important to note that the framework you use to tell a story depends on the topic and the audience. At their basic core, stories have a beginning, middle, and end<\/strong>. This text will weave \u201ctelling stories\u201d throughout each chapter as you learn to create dynamic data visualizations. It takes practice to be good at both and even more practice to integrate the two.<\/p>\r\n

    For more than two decades, data storytelling consultants have played a crucial role as intermediaries, bridging the gap between data and decision-makers. Their expertise lies in presenting the right information in a compelling manner. To gain insights into the possibilities of data storytelling, we can explore the examples shared by these consultants on their websites. Numerous consultants exist in the field, including Storytelling With Data, Duarte, and Juice Analytics.<\/p>\r\n

    Zach Gemignani (2023) of Juice Analytics showcases an intriguing blog topic titled \u201cHow to Summarize Data Using ChatGPT,\u201d which demonstrates the incorporation of artificial intelligence (AI) in storytelling. This highlights the growing trend of consultants embracing AI technologies to enhance their data storytelling capabilities. AI can assist in data analysis, pattern recognition, and even generating narratives, thereby augmenting the storytelling process.<\/p>\r\n

    Furthermore, it is worth noting that some consultants have evolved their focus to emphasize using data for social good. Examples include Viz for Social Good and Periscopic: Do Good With Data, where consultants aim to apply data storytelling techniques to address social and environmental challenges.<\/p>\r\n

    Overall, these consultants offer a wealth of resources and workshops.<\/p>\r\n

    \"\"Viz for Social Good<\/strong>. (2022). Connecting data enthusiasts with mission-driven organizations. https:\/\/www.vizforsocialgood.com\/<\/span><\/a>. This is a not-for-profit organization; expect to see everything from static infographics to video infographics, as well as the datasets for each project.<\/p>\r\n

    \"\"Storytelling With Data<\/strong>. (2023). We help people and organizations create graphs that make sense and weave them into compelling, action-inspired stories. https:\/\/www.storytellingwithdata.com<\/span><\/a>. This organization shares 100s of videos and podcasts.<\/p>\r\n

    If the data storytelling is designed so the user can interact with the data (like through a dashboard), the creator may employ data-driven animations to enhance the narrative. To see an example of this, review Neil Halloran\u2019s interactive documentary, \u201cThe Fallen of World War II.\u201d This type of storytelling is called narrative visualization<\/em>.<\/p>\r\n\"\"\r\n

    Halloran, N. (2016, Oct 26). The Fallen of World War II: An Interactive documentary<\/em>. http:\/\/www.fallen.io\/ww2\/<\/span><\/a><\/p>\r\n[embed]https:\/\/vimeo.com\/128373915[\/embed]\r\n

    When to Not Use Data Storytelling<\/h2>\r\n

    While visuals are an essential part of data storytelling, visuals are used to inform, explore, or analyze the data. For example, a weekly meeting that reports facts or a status report is most likely used to inform. Informative data are usually broad, while insightful data are deep. Some data communication doesn\u2019t work well with data storytelling. For example, if your dashboard updates continuously or is automated frequently (like daily), it\u2019s hard to tell a story because the data are changing. An interactive dashboard allows the user to explore and discover their own insights, and during this exploration stage, they don\u2019t need a data storyteller. Let\u2019s look at a comparison in Table 2.2 of when less storytelling is needed and when to use data storytelling.<\/p>\r\n

    Table 2.2:<\/span> Is Storytelling Needed in Every Situation?<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    \r\n

    Less Storytelling Needed<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Use Data Storytelling<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

    \r\n

    \"\"Data are informative<\/p>\r\n<\/td>\r\n

    		\r\n

    \"\"Data are insightful<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    \"\"Purpose is exploratory<\/p>\r\n<\/td>\r\n

    		\r\n

    \"\"Purpose is explanatory<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    \"\"Data are concrete (raw data tables)<\/p>\r\n<\/td>\r\n

    		\r\n

    \"\"Concrete data for context<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    \"\" Data updated continuously<\/p>\r\n<\/td>\r\n

    		\r\n

    \"mazeAbstract data visualizations for insight<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

    Consider if your friend gave you the entire week\u2019s forecast if you asked, \u201cShould I bring an umbrella today?\u201d So what is the difference between concrete and abstract data? A few definitions are in order to support Table 2.2.<\/p>\r\n

    Concrete data<\/strong> in data visualization refers to the actual dataset that is being visualized. It includes the raw data points, their attributes, and their values. For example, a dataset would contain concrete sales figures for different products over time. In the context of storytelling with data, concrete data refers to the specific facts, figures, and statistics that are present to support the narrative. As a rule of thumb, always compare with prior periods (month, quarter, year) to easily see growth or contraction. Revenue trends are considered concrete data.<\/p>\r\n

    Abstract data<\/strong> in data visualization refers to the high-level representation and organization of data for visualization purposes. Essentially it involves transforming concrete data into a visual form that can be easily understood and interpreted. It entails selecting the appropriate visualization technique\u2014like a chart, graph, map, or infographic\u2014to convey insight and patterns in the data. The abstract data capture the overall story or narrative that the visualization aims to communicate.<\/p>\r\n\r\n

    Data Storytelling Framework Templates<\/h2>\r\n

    To assist with your storytelling skill, let\u2019s look at a few data storytelling frameworks. Frameworks adopt a model of storytelling and how data can be integrated within the story. The acronyms define a key focus area, provide structure, and ensure a logical flow that engages your audience. Some models will fit your situation and data better than others. In the next section, we provide an example of a modified framework template (context, audience, story, and situation) and how to use it. You can also find it in the PowerPoint file that accompanies this chapter.<\/p>\r\n

    Table 2.3:<\/span> Data Storytelling Framework Templates<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
    \r\n

    Source<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Source Resource<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Model Acronym<\/strong><\/p>\r\n<\/td>\r\n

    		\r\n

    Model<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

    \r\n

    LucidChart Blog<\/p>\r\n<\/td>\r\n

    		\r\n

    How to Tell a Story With Data<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    CAST<\/p>\r\n<\/td>\r\n

    		\r\n

    Context, Action, Subject, Takeaway Context<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    The Analyst Academy<\/p>\r\n<\/td>\r\n

    		\r\n

    PowerPoint Storytelling<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    SCQA<\/p>\r\n<\/td>\r\n

    		\r\n

    Situation, Complication, Question, Answer<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    Microsoft<\/p>\r\n<\/td>\r\n

    		\r\n

    What Is Data Storytelling and Data Storytelling Examples<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    HERO<\/p>\r\n<\/td>\r\n

    		\r\n

    Hypothesis, Evidence, Relevance, Outcome<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    Storytelling With Data<\/p>\r\n<\/td>\r\n

    		\r\n

    The Structure(s) of Story<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    DATA<\/p>\r\n<\/td>\r\n

    		\r\n

    Dramatic Arc, Tension, Alleviation<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    SEO Analytics<\/p>\r\n<\/td>\r\n

    		\r\n

    PICA Protocol: A Visualization Prescription for Impactful Data Storytelling<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    PICA<\/p>\r\n<\/td>\r\n

    		\r\n

    Purpose, Insight, Context, Aesthetics<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    Sykes, Malik, West, & Malik<\/p>\r\n<\/td>\r\n

    		\r\n

    Stories That Move Mountains: Storytelling and Visual Design for Persuasive Presentations<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    CAST<\/p>\r\n<\/td>\r\n

    		\r\n

    Content, Audience, Story, Tell<\/p>\r\n<\/td>\r\n<\/tr>\r\n

    \r\n

    Mitchell & Deckard<\/p>\r\n<\/td>\r\n

    		\r\n

    Leveraging Visualization to Communicate Effectively<\/em><\/p>\r\n<\/td>\r\n

    		\r\n

    CASS<\/p>\r\n<\/td>\r\n

    		\r\n

    Context, Audience, Story, and Situation<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

    \"\"Source: Sykes, M., Malik, A. N., West, M. D., & Malik, A. N. (2012). Stories that move mountains: Storytelling and visual design for persuasive presentations<\/em>. John Wiley & Sons, Incorporated. ProQuest eBook Central Database.<\/p>\r\n\"\"\r\n

    A great resource or handout is available from the book, Stories that move mountains: Storytelling and visual design for persuasive presentations<\/em>. Link: https:\/\/www.martinjsykes.com\/storiesthatmovemountains<\/span><\/a><\/p>\r\n\r\n

    CASS: A Data Storytelling Framework Template<\/h2>\r\n

    Review Figure 2.1 to see the modified (context, audience, story, and situation) CASS template. This framework combines several of the models and is a great template to evaluate existing data storytelling or build your own storytelling with data when you have a large dataset with several insights to share.<\/p>\r\n

    Context<\/strong>\u2014Determines how the data will be shown (trends, correlations, comparisons), including variables, the need for variables to change, the \u201chow\u201d variables that need to change, and the \u201cso what\u201d (essentially, what are the consequences of change or no change?).<\/p>\r\n

    Audience<\/strong>\u2014Who makes up the audience, and who are the decision-makers? How are the data relevant? What information do decision-makers already know about this topic?<\/p>\r\n

    Story<\/strong>\u2014How are the data used to frame the story or make your point? How is the story engaging? What characters motivate the audience to make a change? How is the problem linked to changes, and why does it change now? What story structure sequences the events so the audience understands the key insights?<\/p>\r\n

    Situation<\/strong>\u2014Is the design framed in a relevant format for the visual content, and does it meet the audience\u2019s expectations? Does data transformation engage and motivate the audience? How does the story frame the data? This framework has 10 key criteria for the data storyteller to consider:<\/p>\r\n

    1. Are the data valid and relevant (for this situation)?<\/p>\r\n

    2. Are the data credible?<\/p>\r\n

    3. Do the data support an action or decision? (#3 and #4 work together)<\/p>\r\n

    4. Do the data support desirability, feasibility, and viability?<\/p>\r\n

    a. Desirability<\/em> refers to the degree to which a data visualization or data storytelling approach meets the needs and expectations of its intended audience. It involves understanding the preferences, goals, and requirements of the users or audience.<\/p>\r\n

    b. Feasibility<\/em> refers to the practicality and achievability of implementing a data visualization or data storytelling concept. It involves considering the available resources, technical capabilities, and constraints.<\/p>\r\n

    c. Viability<\/em> refers to the sustainability and long-term success of a data visualization or data storytelling initiative. Viability considers factors such as the value proposition, potential impact, scalability, and alignment with the goals and objectives of the organization and its stakeholders.<\/p>\r\n

    5. Do all visuals use an appropriate scale (and avoid other manipulations)?<\/p>\r\n

    6. Are the data insightful? (Do they successfully transform concrete data into abstract visualizations?)<\/p>\r\n

    7. Are the data self-explanatory, and are there enough data to provide context?<\/p>\r\n

    8. Do the data support an emotional connection?<\/p>\r\n

    9. Does the story arc support retention and comprehension?<\/p>\r\n

    10. Was the data storytelling tested on a pilot group?<\/p>\r\nFigure 2.1\u2014CASS: A Data Storytelling Framework Template<\/strong>\r\n\r\n[caption id=\"attachment_672\" align=\"alignnone\" width=\"724\"]\"CASS Note: Author adaptation of CASS template in Sykes et al. (2012)[\/caption]\r\n\r\nIn order to see how this template can help you build your own or evaluate existing data storytelling with data visualizations, let\u2019s use Wes O\u2019Donnell\u2019s Information Is Beautiful: How Data Visualization Will Save the World<\/em> (2015) for an example. Please watch the video before you review the completed CASS template (Figure 2.2).\r\n

    <\/span>A copy of the CASS template is available in the PowerPoint file that accompanies this chapter.<\/p>\r\n

    \"\"Wes O\u2019Donnell, (2015), Information Is Beautiful<\/em>: How Data Visualization Will Save the World<\/em>. TEDx<\/sup> Muskegon (x = independently organized TED event) Length: 14:37 minutes<\/p>\r\n[embed]https:\/\/youtu.be\/PZ4D_OM1EPE[\/embed]\r\n\r\nFigure 2.2\u2014CASS Completed Template Using Information Is Beautiful: How Data Visualization Will Save the World<\/strong>\r\n\r\n[caption id=\"attachment_674\" align=\"alignnone\" width=\"724\"]\"A Note: Author completed template using Wes O\u2019Donnell\u2019s Ted Talk (2025)[\/caption]\r\n

    Note: This completed template is also available in the accompanying resources for Chapter 2.<\/span><\/p>\r\n

    1. You will notice a few items with questions in the CASS template. The first issue is in the audience section. Under the decision prompt, where or what is the call to action? Is the purpose to build an awareness of how climate change will impact our great-grandchildren?<\/p>\r\n

    2. The 10 criteria help determine the data validity, credibility, and so on. There are two areas where it is unclear. The first item still in question is \u201cfocus on desirability, feasibility, and viability,\u201d which may be an issue because it is not clear whether a call to action is made or is not clearly stated. The second item is \u201ctested on pilot group.\u201d Wes O\u2019Donnell\u2019s presentation was made at an independent TED Talk in Muskegon; however, it\u2019s unsure if he tested his presentation on a pilot group.<\/p>\r\n

    Fun Fact:<\/strong> If you recognize Wes O\u2019Donnell\u2019s name, he is an occasional TV cohost on Mysteries Decoded<\/em>!<\/p>\r\n\r\n

    \r\n

    2.1 Exercise 1: Use the CASS Framework Template to Assess Data Storytelling<\/p>\r\n\r\n<\/header>\r\n

    \r\n

    Learning Objective #1\u2014Evaluate a data storytelling project for effectiveness<\/p>\r\n

    The following three examples are data storytelling projects that use big datasets. Choose one<\/strong> of the examples and assess it using the CASS data storytelling framework in Figure 2.1. There is a PowerPoint file to accompany this exercise (Link: \u201cCASS Template\u2014Case 2.1\u201d).<\/p>\r\n

    \"\" Example 1:<\/span> Hans Rosling\u2019s \u201c200 Counties, 200 years in 4 Minutes\u2014BBC News\u201d<\/p>\r\n[embed]https:\/\/youtu.be\/Z8t4k0Q8e8Y[\/embed]\r\n\r\n\"\"\r\n

    Example 2: UN Sustainable Development Report 2022: From Crisis to Sustainable Development: The SDGs as Roadmap to 2030 and Beyond\u2014Choose Explore Data.\u00a0Link: https:\/\/dashboards.sdgindex.org\/<\/span><\/a><\/p>\r\n

    \"\" Example 3:<\/span> Matthew Klein, Bloomberg View (2014) How Americans Die.\u00a0Link: https:\/\/www.bloomberg.com\/graphics\/dataview\/how-americans-die\/<\/span><\/a><\/p>\r\n

    Post your CASS template to the discussion board and point out areas where you struggled to answer the template questions.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

    2.2 The Good, the Bad, and the Ugly of Data Visualizations<\/h1>\r\n
    \r\n

    The purpose of visualization is insight, not pictures.<\/p>\r\n

    \u2014Ben Shneiderman, American computer scientist<\/p>\r\n<\/blockquote>\r\n

    The purpose of this section is to compare the good, the bad, and the ugly of data visualizations, but let\u2019s discuss and define the terms good<\/em>, bad<\/em>, and ugly<\/em> in a data storytelling context.<\/p>\r\n

    Good data visualization<\/strong> represents projects that effectively communicate and clarify complex data. It avoids issues found in a bad data visualization or an ugly data visualization. Reviewing the CASS template in Figure 2.1, a good<\/em> data visualization has these characteristics about data:<\/p>\r\n\r\n

      \r\n \t
    1. data tell the truth, and visuals use appropriate scale;<\/li>\r\n \t
    2. data are valid, accurate, relatable, and credible;<\/li>\r\n \t
    3. data are insightful, explanatory, and concrete;<\/li>\r\n \t
    4. data are transparent and supports the action or decision; and<\/li>\r\n \t
    5. the consumer of the data is provided with relevant context that impacts the data (e.g., maps are representative of population density centers).<\/li>\r\n<\/ol>\r\n

      Bad data visualization<\/strong> represents projects that are incomplete or contain misleading information or incorrect conclusions. Data storytellers should never lie or misrepresent the data. In bad data visualizations, storytellers might exaggerate or downplay certain data points, distorting the story. Other characteristics of bad data visualization are when storytellers cherry-pick, manipulate (like compressing the scale), mislabel, or intentionally hide some data. Some bad data visualization projects cross the ethical line.<\/p>\r\n

      An ugly data visualization<\/strong> refers to a project that usually has overly cluttered charts, excessive labels, ambiguous color schemes, and crowded graphs. In some cases, it might have correct data, but the wrong type of chart was used. Some storytellers might be inconsistent without realizing it. If the inconsistency is intentional, it moves from ugly to bad.<\/p>\r\n

      A good data visualization storytelling project<\/strong> provides contextualization, engagement and an emotional connection, clarity to complex data, and guides attention and focus. In addition, the story should flow logically with a story arc structure and summarization that supports comprehension and retention. Every data visualization storytelling project should be designed to create a compelling and impactful narrative that effectively communicates data-driven insights to your audience. Data projects can be static and simple (like infographics) or complex with interactive elements.<\/p>\r\n\r\n

      Did You Unintentionally Mislead Your Audience?<\/h2>\r\n

      You work with charts every day, but did you realize you could mislead your viewer by using count instead of frequency? The best way to demonstrate this is to show you! Comparative bar charts provide a visual comparison of univariate, categorical data for two or more groups.<\/p>\r\n

      Each year the Princeton Review<\/em> conducts a survey. One of the questions is asked to parents and students. It is, \u201cIdeally, how far from home would you like the college you (your child) attend(s) to be?\u201d As you might expect, parents and students do not agree. Let\u2019s look at the frequency table along with the relative frequency table. Frequency<\/strong> measures how often a particular event occurs (essentially the count). In Figure 2.3, 2,878 students and 1,230 parents voted that an ideal college is less than 250 miles from home. Relative frequency<\/strong> indicates a proportion in relation to the total (shown as a percentage).<\/p>\r\nFigure 2.3\u2014Showing Count and Frequency for Students and Parents <\/strong>\r\n\r\n[caption id=\"attachment_1395\" align=\"alignnone\" width=\"688\"]\"Data Note: Author adaptation of data from Princeton Review College Hopes & Worries Survey Report (2019)[\/caption]\r\n

      So 2,878\/9,282 = .31<\/strong> (or 31% of students voted for a college less than 250 miles from home).<\/p>\r\n

      And 1,230\/2,618 = .47<\/strong> (or 47% of parents voted for a college less than 250 miles from home).<\/p>\r\n

      Now let\u2019s look at the two charts. Both charts have a clear title, an appropriate legend, and appropriate labels, and the column colors make sense. But using count<\/strong> is inappropriate since the sample size is not the same.<\/p>\r\n

      Exhibit 2.1\u2014Use a Relative Frequency Table for Intended Representation<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      \r\n

      Bad Chart\u2014misleading information<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      Good Chart<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \r\n

      \"This<\/p>\r\n<\/td>\r\n

      		\r\n

      \"\"<\/p>\r\n<\/td>\r\n<\/tr>\r\n

      \r\n

      This chart uses count and because the sample size is quite different, can lead the audience to a different conclusion.<\/p>\r\n<\/td>\r\n

      		\r\n

      Use relative frequencies on the vertical axis instead of the frequency (count). The use of relative frequency helps make a more meaningful comparison when sample sizes are not the same.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      Next, let\u2019s look at some charts that work with the same data but represent the good, the bad, and the ugly (Exhibit 2.2). These charts were created in Power BI and show the results of a year of revenue for eight salespeople. The key point here is that you can use very powerful software to create your dashboard and still have bad and ugly data visualizations if you don\u2019t use the right charts, correct scales, or good design protocols.<\/p>\r\n

      Exhibit 2.2\u2014The Good, the Bad, and the Ugly in Power BI Charts<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      \r\n

      The Good<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      The Bad<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      The Ugly<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \"Three<\/td>\r\n\"The<\/td>\r\n\"The<\/td>\r\n<\/tr>\r\n
      \r\n

      All bars are labeled and sorted properly.<\/p>\r\n<\/td>\r\n

      		\r\n

      One salesperson\u2019s name is not shown, no dollar amount. Too many slices\u2014pie chart not the best option.<\/p>\r\n<\/td>\r\n

      		\r\n

      This chart is best to show an increase and decrease and it does not work well for this kind of data.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      There is room for improvement in all the charts. For example, if this is a stand-alone chart on a slide, adding the year and company name would be appropriate. However, if the chart resides on a dashboard, the year and name can be at the top of the dashboard and the chart doesn\u2019t need them.<\/p>\r\n\r\n

      Does Your Pie Graph Add Up to 100%?<\/h2>\r\n

      Figure 2.4 was modeled from a chart shown on a news broadcast in 2012. The names shown on the broadcast are removed. Although the intent was to show the closeness of the race, pie charts should always add up to 100%<\/strong>. Notice that this one does not. This one also falls under the category of bad and ugly: bad because the pie chart doesn\u2019t add up to 100% and ugly because the colors might unintentionally sway the audience to think more positively of one candidate over another. The colors used here duplicate the chart shown during the news broadcast. Both pie charts and doughnut charts should be avoided. Your audience will struggle to estimate a quantity from angles, and matching labels to the slices can be especially difficult if you have small percentages\u2014and that small percentage might be important!<\/p>\r\nFigure 2.4\u2014Example of a Bad and Ugly Chart<\/strong>\r\n\r\n[caption id=\"attachment_197\" align=\"alignnone\" width=\"329\"]\"This Note: Author Illustration (2024)[\/caption]\r\n

      Did Your Image Size or Proportion Mislead Your Audience?<\/h2>\r\n

      Figure 2.5 shows another misleading graphic where size matters. The news station misled the audience because the size was not depicted in actual proportions. The following was a graph showing gas prices with the highest price in California and the lowest in Mississippi. But notice the misuse of proportion.<\/p>\r\nFigure 2.5\u2014Misuse of Proportion: Bad Chart<\/strong>\r\n\r\n[caption id=\"attachment_198\" align=\"alignnone\" width=\"307\"]\"Image Note: Author Illustration (2024)[\/caption]\r\n

      Did the Scale Used in the Graph Mislead Your Audience?<\/h2>\r\n

      The same news channel produced a graph that did not have the scale set to zero. In Exhibit 2.3, the chart on the left shows the axis scale starting at 32% and the chart on the right shows the scale starting at 0.00%. I\u2019m sure you can see that the chart on the left is misleading!<\/p>\r\n

      Exhibit 2.3\u2014Example of Misleading Scale for a Tax Rate Increase<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      \r\n

      Bad Chart\u2014Misleading<\/p>\r\n<\/td>\r\n

      		\r\n

      Good Chart\u2014Starts With 0.00 Scale<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \"The<\/td>\r\n\"This<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      Now that you have seen examples of good, bad, and ugly data visualizations, let\u2019s see if you can differentiate them in the following exercise.<\/p>\r\n\r\n

      \r\n

      2.2 Exercise 1: Critical Evaluation of Good and Bad Data Visualizations<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #2\u2014Differentiate between effective and ineffective data visualizations<\/p>\r\n

      \"\"Part 1:<\/em> Find three examples of bad and\/or ugly data visualizations (just type \u201cbad data visualizations\u201d in the search box of your browser to find examples). Screenshot the examples and indicate why they fit this category. What changes would you make to correct them?<\/p>\r\n

      Part 2:<\/em> Type \u201cbad visualization + Purdue Pharma\u201d in the search box of your browser. Share your findings in a discussion. Did Purdue Pharma cross an ethical line?<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      2.3 The Four Pillars of Visualization Work Better With Context<\/h1>\r\n
      \r\n

      Visualization and belief in a pattern of reality activates the creative power of realization.<\/p>\r\n

      \u2014A. L. Linall Jr.<\/p>\r\n<\/blockquote>\r\n

      Now that you have seen how good, bad, and ugly data visualizations are prevalent, let\u2019s take a step into becoming a data storyteller. If you\u2019ve been creating PowerPoints at your organization, you have certainly told a story. But how well did you tell that story? Did you use the best visualization techniques to tell it? Creating your own infographic or data visualization may start with available templates (much like PowerPoint slide decks or infographic templates in an infographic app). Do you remember the first time you learned PowerPoint? Did you explore one<\/em> PowerPoint theme design after another<\/em> or have trouble modifying an existing slide deck to make it \u201cfit\u201d your purpose? When you start creating your own data visualizations, don\u2019t be surprised at how often you need to fall back on good data visualization design techniques like those presented by Noah Iliinsky. Noah Iliinsky, a data visualization expert, cautions how quickly a presenter can get off track.<\/p>\r\n

      When Larry Swanson of Content Strategy Insights asked, \u201cAre there benchmarks, or how do you know you\u2019re on track?\u201d Iliinsky answered, \u201cIt goes back to purpose, know your customer and what they care about\u201d (Swanson, 2020, Podcast 89\u201424:32). You must start with the purpose and be able to check off the \u201cwhy,\u201d \u201cwho,\u201d and \u201cwhat does my audience need\u201d to understand (What actions do you want to support? What is the most important takeaway message? How will it be consumed?). The more specifically you drill down to purpose<\/strong>, the better. As shown in Exhibit 2.4, before building an interactive data visualization, make sure to embrace Noah Iliinsky\u2019s four pillars of visualization with an added component of context.<\/p>\r\n

      Exhibit 2.4\u2014Iliinsky\u2019s Four Pillars Plus Context<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      \r\n

      Iliinsky\u2019s Four Pillars of Visualization<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      Plus Context<\/em><\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      Equals<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      Elevated Understanding<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \r\n

      \u2022Purpose,<\/p>\r\n

      \u2022Content (what to visualize),<\/p>\r\n

      \u2022Structure (how to visualize it)<\/p>\r\n

      \u2022Format (appeal and focus).<\/p>\r\n<\/td>\r\n

      		\r\n

      +<\/p>\r\n<\/td>\r\n

      		\r\n

      CONTEXT<\/p>\r\n<\/td>\r\n

      		\r\n

      =<\/p>\r\n<\/td>\r\n

      		\r\n

      The audience will understand better and remember longer.<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      In this case, context<\/em> means making the topic easier to understand by giving extra information about the time, place, situation, history, background, and so on. That\u2019s part of the storytelling process! So how does storytelling relate to interactive data visualizations?<\/p>\r\n

      \"\"Iliinsky, N. (2018, Apr 12). Four Pillars of Visualization\u2014English version<\/em>. StarsConf. San Diego, USA. [Video]. YouTube.<\/p>\r\n[embed]https:\/\/www.youtube.com\/watch?v=3eZ15VplE3o[\/embed]\r\n

      \"\"Knaflic, C. (2015, Nov 2). Storytelling <\/em>With Data<\/em>: A Data Visualization Guide for Business Professionals<\/em>. Wiley Publishing. ProQuest eBook Central Database.<\/p>\r\n

      \"\"Wexler, S., Shaffer, J., & Cotgreave, A. (2017). The big book of dashboards: Visualizing your data using real-world business scenarios<\/em>. Wiley Publishing. ProQuest eBook Central Database.<\/p>\r\n

      Another data visualization expert is David McCandess. David McCandess, at his Information Is Beautiful<\/em> website, shares a graphic that interacts when you roll your mouse over certain areas to give you more details. McCandess considers all the aspects of data visualization but includes more of the storytelling components than Noah Iliinsky.<\/p>\r\n\"\"\r\n

      McCandess, D. (2023). What Makes a Good Visualization? Information <\/em>Is Beautiful<\/em>. https:\/\/www.informationisbeautiful.net\/visualizations\/what-makes-a-good-data-visualization\/<\/span><\/a><\/p>\r\n

      According to McCandess (2020), to have a successful visualization you need (1) good and accurate information, (2) the goal of visualization, (3) the story or concept that accompanies the visualization, and (4) the actual visual form that works best for the visualization.<\/p>\r\n\r\n

      \r\n

      2.1 Self-Assessment: Four Pillars of Visualization<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #3\u2014Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics<\/p>\r\n

      \"\"Watch the YouTube video \u201cFour Pillars of Visualization\u201d by Noah Iliinsky. [h5p id=\"2\"]<\/p>\r\n

      \"\" [h5p id=\"3\"]<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      \r\n

      2.3 Exercise 1: Using the Four Pillars to Help the United Way Team Explain ALICE<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #3\u2014Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics<\/p>\r\n

      \"\"In the \u201cWhat Do You Think?\u201d scenario beginning this chapter, a United Way team is brainstorming ways to present social media infographics that explain ALICE families and what that means. They also want a call to action to request donations.<\/p>\r\n

      How would you complete the four pillars + context (see Exhibit 2.4) to help the ALICE team present their data in a social media post?<\/em><\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      2.4 Infographic Basics<\/h1>\r\n
      \r\n

      The greatest value of a picture is when it forces us to notice what we never expected to see.<\/p>\r\n

      \u2014John Wilder Tukey, American mathematician and statistician<\/p>\r\n<\/blockquote>\r\n

      To begin, let\u2019s look at the simplest data visualization\u2014an infographic. An infographic is a graphical representation intended to communicate information in a concise and meaningful way. Most infographics are \u201cstatic,\u201d but the trend is moving toward interactive infographics.<\/p>\r\n

      You have probably used infographics more than you think! According to an old study by Levie & Lentz (1982), people following directions with text and illustrations<\/strong> do 323% better than people following directions without illustrations (p. 195\u2013232). Perhaps you\u2019ve assembled something using diagrams\u2014think about your experience if the instructions were all text describing what needed to be done. Did the visual help you understand each step?<\/p>\r\n

      You expect infographics to take visual form in social media, but don\u2019t be surprised to see them in scientific communication to explore datasets and communicate findings on websites, theses, dissertations, and resumes (Jaleniauskiene & Kasperiuniene, 2023).<\/p>\r\n

      The designs of infographics range from very simple to complex, but all include content and visual elements. It takes practice before it becomes a basic digital skill. Studies across several disciplines (computer science, education, health sciences, digital marketing and media management, sociology, mathematics, ESL, and sports management) show that students who learned to create or analyze infographics \u201cgained metacognitive strategies, better recall, enhanced depth of understanding, better communication skills, and saw the process as engaging and enjoyable\u201d (Jaleniauskiene & Kasperiuniene, 2023, p. 200)!<\/p>\r\n

      According to Beegle (2013), the \u201cbest infographics build a narrative in much the same way a story does, starting with the premise and then revealing something new at each turn\u201d (p. 65). In Figure 2.6, the infographic reveals layers of information about immigrants. The use of flags brings the user focus to the immigrant\u2019s country of origin. There are charts to show the education attainment level and the percentage of immigrants who speak English proficiently. In addition, the infographic indicates how much immigrants contribute to Indiana\u2019s economy through taxes paid. As you look at this chart, what other information would you consider including or would you want to know?<\/p>\r\n\r\n

      The Simplest Evaluation of an Infographic<\/h2>\r\n

      The first step to creating an infographic and then evaluating it is a simple process of informing yourself using the 5 Ws (who, what, when, where, and why). Use the 5 Ws to help convey information in a relevant way. In this illustration (Figure 2.6), let\u2019s go through the 5 Ws.<\/p>\r\nFigure 2.6\u2014How to Use the 5 Ws to Review an Infographic<\/strong>\r\n\r\n[caption id=\"attachment_199\" align=\"alignnone\" width=\"417\"]\"Infographic Note: Author Illustration (2024)[\/caption]\r\n

      Who?<\/strong>\u2014immigrants in Indiana<\/p>\r\n

      What?<\/strong>\u2014raise awareness<\/p>\r\n

      When?<\/strong>\u20142019 data<\/p>\r\n

      Where?<\/strong>\u2014Indiana<\/p>\r\n

      Why?<\/strong>\u2014bring awareness of education levels and contribution to Indiana\u2019s economy<\/p>\r\n

      This illustration was made in the app Venngage, using source information from the American Immigration Council. All infographic software has a variety of icons that are available for use, but it also includes great charting tools.<\/p>\r\n

      The next step is analyzing some existing infographics. Just doing a search on infographics will yield all kinds of ideas in both design and content. Notice the use of color, symbols, and images. Ignore interactive infographics for now.<\/p>\r\n

      Try to gain a sense of an infographic that tells a story that prompts emotional tension. As you search for an infographic you like, see if you can pick out the 5 Ws. If you find one that you don\u2019t understand, see if you can do a little research to find the context. An infographic is not good if it doesn\u2019t meet the definition (communicating information in a concise and meaningful way<\/em>). It\u2019s like hearing a story without knowing all the characters. And more importantly, infographics should contain a driver or \u201ccall to action.\u201d<\/strong><\/p>\r\n

      To illustrate the importance of context, look at Exhibit 2.5, where both infographics are from the Wabash Valley United Way\u2019s Facebook presence. Are these graphics designed to raise awareness (educational<\/strong>) or do they call you to do<\/em> something?<\/p>\r\n

      Exhibit 2.5\u2014Do These Infographics Have a Call to Action?<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      \r\n

      United Way of the Wabash Valley Facebook post, August 31, 2018<\/p>\r\n<\/td>\r\n

      		\r\n

      United Way of the Wabash Valley Facebook post, November 11, 2018<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \r\n\r\n[caption id=\"attachment_54\" align=\"alignleft\" width=\"213\"]\""I Source: United Way of the Wabash Valley\u2014Used with permission[\/caption]<\/td>\r\n\r\n\r\n[caption id=\"attachment_55\" align=\"alignleft\" width=\"267\"]\"This Source: United Way of the Wabash Valley\u2014Used with permission[\/caption]<\/td>\r\n<\/tr>\r\n
      \r\n

      It took a minute to understand that ALICE = A<\/strong>sset L<\/strong>imited + I<\/strong>ncome C<\/strong>onstrained + E<\/strong>mployed<\/p>\r\n<\/td>\r\n

      		\r\n

      This message is clear, and a good start to a call to action.<\/p>\r\n<\/td>\r\n<\/tr>\r\n

      \r\n

      This is the related message: Over 30,000 families in the Wabash Valley are ALICE or struggling working poor. Join the Fight to help us make a difference in the lives of ALICE!<\/p>\r\n<\/td>\r\n

      		\r\n

      This is the related message: This isn\u2019t just a household problem; it\u2019s a community problem. How can our community thrive when over 40% of our households don\u2019t earn enough to achieve a \u201csurvival budget\u201d and must make tough choices on what to pay? Do they pay the electric bill, or do they pay the rent?<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      Note that these infographics are specific to the Wabash Valley in Indiana; therefore, someone from a different state might not understand the context of ALICE since not all states have adopted the ALICE project. If you visit the United Way of the Wabash Valley, you will find reference to their bold goal\u2014to move 10,000 families out of financial struggles and into stability along with a very visual strategic plan.<\/p>\r\n\"\"\r\n

      Strategic Plan. (2023). United Way of the Wabash Valley. Terre Haute, IN. https:\/\/uwwv.org\/storage\/2018\/01\/Strategic-Plan.pdf<\/span><\/a><\/p>\r\nDozens of high-quality infographics help communicate the context of this report. The corresponding Facebook pages include a variety of infographics related to the strategies of the United Way.\r\n

      Key Components to Evaluating an Infographic<\/h3>\r\n

      Keep in mind that \u201c80% of learning takes place visually; the average person retains only 20% of what is read when it\u2019s delivered in text-only format; images on Facebook are liked 200% more than text [and perhaps most important for your organization<\/em>]; websites register a 12% increase in traffic after publishing an infographic\u201d (Beegel, 2014, p. 8\u20139). But perhaps the most important characteristic of an infographic is presenting complex information in a way that is easily understood.<\/p>\r\n

      The Encyclopedia of World Problems & Human Potential<\/em> (UIA.org) identifies the proliferation of information<\/strong> using new terms like infoxication, infobesity, and data smog. Interestingly, the UIA connects this problem to the UN Sustainable Development Goal #12, Responsible Consumption and Production, and the UN Sustainable Development Goal #16, Peace, Justice, and Strong Institutions. So what do data smog, infoxication, and infobesity have to do with infographics?<\/em><\/p>\r\n

      All three terms describe information overload. An infographic can cut through information overload, separate the signal from the noise, and break down complex information into bite-sized chunks making it easier for your infographic audience to understand and retain key insights without feeling overwhelmed by the sheer volume of information available.<\/p>\r\n\r\n

      Infographic Basic Components<\/h2>\r\n

      Regardless of the type of infographic, every infographic should have the same basic components or considerations, and Figure 2.7 provides a good checklist to assess a basic infographic. The checklist is a compilation of Beegle (2014) and Wallace (2015).<\/p>\r\nFigure 2.7\u2014Infographic Quality Checklist for Basic Infographics<\/strong>\r\n\r\n[caption id=\"attachment_200\" align=\"alignnone\" width=\"325\"]\"Infographic Note: Author Illustration (2024)[\/caption]\r\n

      Infographic Components\u2014Sound or Video<\/h2>\r\n

      Other basic components will apply if you create an infographic with motion, sound, or interaction. If you move past the static infographic, consider these additional<\/em> components as shown in Table 2.4.<\/p>\r\n

      Table 2.4:<\/span> When to Add Sound or Video<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
      (Beegle, 2014, p. 29\u201332)<\/em><\/strong><\/caption>\r\n
      \r\n

      Use sound or voice narration to\u00a0.\u00a0.\u00a0.<\/strong><\/p>\r\n<\/td>\r\n

      		\r\n

      Use (or add) video to\u00a0.\u00a0.\u00a0.\u00a0.<\/strong><\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/thead>\r\n

      \r\n

      \u2713Establish a mood<\/p>\r\n<\/td>\r\n

      		\r\n

      \u2713Keep the user on a linear path set by the designer.<\/p>\r\n<\/td>\r\n<\/tr>\r\n

      \r\n

      \u2713Create a sense of authority<\/p>\r\n<\/td>\r\n

      		\r\n

      \u2713Explain more complex concepts (like the workings of a mechanical device)<\/p>\r\n<\/td>\r\n<\/tr>\r\n

      \r\n

      \u2713Connect to viewers<\/p>\r\n<\/td>\r\n

      		\r\n

      \u2713Make it more personal<\/p>\r\n<\/td>\r\n<\/tr>\r\n

      \r\n

      \u2713Provide emotional depth<\/p>\r\n<\/td>\r\n

      		\r\n

      \u2713Testimonial (like for branding)<\/p>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

      \r\n

      2.2 Self-Assessment 1: Basic Design<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #3\u2014Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics<\/p>\r\n

      \"\"You\u2019ve been asked to compare the performance of two companies in the same sector. [h5p id=\"4\"]<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      \r\n

      2.4 Exercise 1: Apply the 5 Ws of an Infographic<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #3\u2014Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics<\/p>\r\n

      \"\"Find three infographics related to immigration in your state. See if you can answer the 5 Ws: who, what, when, where, and why. Share the image(s) and discuss the 5 Ws on the discussion board. Is there a call to action?<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      \r\n

      2.4 Exercise 2: Evaluate a Static Infographic<\/p>\r\n\r\n<\/header>\r\n

      \r\n

      Learning Objective #3\u2014Apply design principles and use models (like CASS) to evaluate the infographic to ensure understanding of the information presented in charts and infographics<\/p>\r\n

      \"\"Part 1:<\/strong> Find a static infographic from your organization. Use the basic design criteria listed in this section to evaluate it. Share the image and discuss what changes you would make to improve it. Part 2:<\/strong> Now use the CASS template to review the same infographic. What differences are observed?<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

      Types of Infographics<\/h2>\r\n

      There are a variety of infographics, and the type of infographic you build will be driven by what works best for your message. Let\u2019s review a few of these with examples.<\/p>\r\n\r\n