The fundamentals of capturing the audience’s interest

As a storyteller, you need to create a strong emotion to strengthen the link with your reader. That way, your story will catch your audience’s interest and they will be so invested in that story that they will continue to read it. There are several ways to create that emotional connection, including design and story flow. These elements need to work together: A good design definitely attracts the reader, but if the story or analysis is empty, the reader will not get involved or stay interested until the end.

Maybe you recall the story of 1001 Nights, where the heroine, Shéhérazade, tells a story every night to the Sultan, but stops in the middle of the intrigue when the dawn comes, leaving him wanting more. (Because the Sultan, disappointed by his previous spouse, used to kill every new bride at the break of dawn, Sheherazade certainly adopted a smart strategy). Maybe we don’t need to reach such extremes (the stakes, in our case, are not so high), but we still need to create some kind of emotion to connect with our readers at a deeper level, and make them continue wanting more or remembering our data story.

The importance of connecting at a deeper level

What gets the reader interested is often a gut reaction, also known as visceral level of processing (according to design expert Don Norman). It is quite simple: You either like what you see or you don’t. If you like what you see, you will naturally dive deeper into it. If you don’t, you will push it aside for as long as possible until you are really forced to have a look at it (which is usually what I do with the tasks that bore me).  

Let’s take a simple example. When you have a magazine in your hands, like for instance the latest issue of Nightingale, how do you pick the article to read first? Do you start from page one and read until the end? Or do you first browse the entire magazine until a title or visual catches your eyes and pulls you in to start reading? Sometimes, without even realizing it, we are “victims” of this visceral level of processing.

Another parallel can be made with advertisements. When a publicist wants to sell a product, they will try to touch the target consumer with any kind of emotion: make them smile, laugh, cry…feel anything other than indifference, so they remember the product and end up buying it at a later stage. Why? Because that emotional impact puts your interaction with the product into your long-term memory. You may forget the brand name of the product but not the feeling or emotion it creates. For data visualization, it’s best to design in a way that communicates a feeling, as it will be remembered.

We have to be aware of this aspect to be able to keep an open mind and be able to push new ideas in our stakeholders’ minds. When designing business dashboards containing new visuals, the first reaction of my stakeholders is often rejection. The reason may be that they are not familiar with it, they don’t know how to use it, or that we haven’t yet created this sparkle of curiosity in them. That is why I often explain the benefits of the new dashboard and give them some time to play with it before even collecting any feedback.

Creating curiosity

When I publish a data story, what I am wishing for is to connect with other people. I want to intrigue them or simply make them smile while sharing a strange discovery or a fun fact. According to Don Norman, you need to create a pleasurable experience for any product you design, whether it is a coffee machine or a business dashboard. I tend to agree, and I am trying to use these principles in my work or my personal project.

Some time ago, I discovered a version of the following chart about air pollution and its evolution across time.

By the simplicity of the color scheme, and its intriguing pattern, it appealed immediately to me. Because it surprised me, I immediately started reading the visualization and spent a long time going through it. 

Because this visualization intrigued me so much, I couldn’t stop thinking about it for several weeks. And I thought that it would be a nice challenge for myself to recreate this chart in Tableau, but using a different dataset. Not only would I be challenged to generate the mathematical coordinates necessary to draw it, but also to attract the reader’s attention on a subject that matters. I came across a military dataset that has similar characteristics to the air pollution dataset, as they both describe a long period of time across different countries, for which the phenomenon is not homogenous. Here is how my version turned out:

We immediately see that China and the US are outliers in terms of military expenses. Of course, we need a “how to read it” box explaining how to read the chart, but if your curiosity is piqued, you will make the effort to understand that the evolution lines go clocklike as they deviate from the origin point, and that the blue triangles indicate a reduction of the military expenses (which in my view is a good thing) compared to the previous year (and red triangle represents an increase). I also added the possibility for the user to drill down into the other countries and zoom in, which didn’t exist in the original piece.

But it could have been a line chart, right?

Yes of course it could have been a line chart, and it should have been a line chart in a business environment. The first version of this analysis used a line chart, but it was barely seen (based on the views on Tableau Public) and nobody mentioned to me that they found the analysis interesting nor that it created the awareness about the preponderance of US and China’s military expenses. This is because the line chart did not conjure an emotion, so it didn’t pique people’s interest or make them stop and engage with it.

Remember Florence Nightingale ? 

Her most famous chart (above), known as the Nightingale’s rose, highlights the number of unnecessary deaths during the Crimean War as a result of preventable infections that could have been controlled by a variety of factors including proper nutrition, ventilation, and shelter. This coxcomb graph created in 1858 had a huge impact during its presentation. But the reality is that Nightingale tried to present this data in 1857 with a more “traditional” approach, with bars, but it didn’t make an impact until the data was presented as a rose. 

If it was true for the beginning of the 19th century, it is definitely relevant for our digital world, where everything competes to catch our attention.  If you want to generate some interest and create some consciousness, you need an impactful visual.

 Put your heart on the table

I have also designed some visualizations as a catharsis of my own emotions. When my dog died, I created a visualization to celebrate the courage of the most courageous animals. I will probably never know if this one stirs emotion among my readers, but it helps me to process my own emotions. 

But I know that the story didn’t really reach my audience, and if I am being honest, the visualization did not present the story I wanted to talk about. I wanted to talk about my dog, and his fight to live another day. I was monitoring his pain level and his mood to be able to decide when the time would come, but I lacked courage to tell this story and visualize it, because it was too painful to me. So I did this visualization instead, and even if it is nice, it is not as impactful the original idea was.

I often vizzed about animal stories, to bring awareness to their extraordinary achievements, such as Jimmy the Raven crow (who had a 20-year long career as an actor in American movies between the 1930s and 1950s or Brinzola (a vulture that traveled 3,000 km in 25 days). But I can see clearly that I had just created curiosity for myself with those stories without really touching my audience, and few people remember them. Of course it is not about the animals’ stories, which are remarkable, but about my own failed attempts to convey them. 

But from time to time, I design a visual story that people remember, because it made them smile, and touched them deeper than I would have ever imagined. The following visualization is my biggest hit in Tableau Public with 12,000 views (modest success compared to very talented authors but still considerable for me). The visual below is about the final destination of a little mouse. In a nutshell, a little mouse asked me to do a risk assessment analysis on the European countries, so she could decide where she would feel safe and most enjoy the food.

When I created this story, I only had a few Tableau skills, but it didn’t matter, because the story was touching. To my surprise, a journalist at an Italian newspaper took up my story and wrote an article about it. Did I expect such an impact? Certainly not, but as I designed my story I was crying and praying that my sense of humor and my ideas would be accepted by the data community…and it worked. I found my tribe on that day, even if my expectation was just to have fun, and somehow I transmitted this emotion. It was fresh and unexpected. 

You never know what reaction or emotion you will have to others, so be true to yourself, and put your heart on the table the next time you viz; that is the only way to truly connect with others and with yourself.

The post Curiosity Piques Interest, An Emotional Story Sustains It appeared first on Nightingale.

For me, that means finding creative ways to weave together numbers, charts, and context in a meaningful narrative to help someone understand or communicate a complex topic. 

But I don’t think we all mean the same thing when we use the phrase “data storytelling” — particularly across data visualization creators and our clients, whether we’re in formal consulting roles or supporting an internal team. There’s a demand for data stories even when the team’s analytical needs aren’t demanding narratives.

Over the past year, I’ve observed a spike in blog posts, eLearning courses, workshops, and more, all promising to teach you the secrets of great data storytelling. These are absolutely worthwhile skills to learn. I’ve also seen an increase in demands from clients in my own work around more data storytelling — using those specific words.

How did we get to this peak demand for data storytelling? Why might we want to probe a bit deeper around what someone means when they talk about doing MORE data storytelling, where perhaps the analytical goals are more around story-finding than storytelling? And how might a more nuanced shared language about our analytical products help designers and users alike?

This article is for the dashboard designers tasked with more data storytelling (even when the goal is exploration), the language enthusiasts who are curious how words and phrases evolve, and the data visualization experts who love to get in the weeds of “how we got here.” 

Let’s dive in, and meander towards the questions to ask when we start a new project in this era of excitement for data storytelling.

A decade+ of data storytelling

The enthusiasm for data storytelling isn’t new. 

Go back a decade, and there’s a rich discussion and debate among people recognized as leading voices in the field (Robert Kosara, Lynn Cherny, Moritz Stefaner, Alberto Cairo, and others) about the role of storytelling, narrative, and “punchlines” in data visualization. Dated April 6, 2013, Mortiz Stefaner writes: “Storytelling has been one of the big buzzwords in data visualization the last year.” 

One of the most commonly mentioned “storytelling” books in analytics is Cole Nussbaumer Knaflic’s Storytelling With Data, first released in 2015. Five years later, Brent Dykes authored a masterclass on blending data and narrative in “Effective Data Storytelling” published in 2020. 

But numbers don’t lie: Data storytelling is popping up across social posts, blogs, and client requests today at a frequency unmatched in previous years — and you can see the climb in the Google search trends over the last decade both in the US and worldwide.

Industry analysts also agree that data storytelling is having a moment. Let’s take a peek into reports from a leader in creating reports in the world of analytics: Gartner.  

If you know Gartner and work in data viz and business intelligence, you likely know the Magic Quadrant reports, which position different tools based on completeness of vision and ability to execute. A second group of reports worth your time are the hype cycles for different segments of the tech industry including analytics and business intelligence. Hype cycle graphics (and their very lengthy associated reports, which you can access through a partner vendor or as a paying Gartner subscriber) have five key phases over the long arc of time, as shown below. Importantly, they’re written with Gartner clients (often not technologists by trade) who want to make informed decisions on where to invest resources.

In the early climb up the curve, early adopters in the industry begin to experiment with a technology or practice. Then, at the “peak of inflated expectations,” there’s all-out fervor about the new approach, after which it slides into the “trough of disillusionment” before leveling out in the “plateau of productivity” with stable adoption and use of the technology or tactic.

For Gartner, data storytelling “combines interactive data visualization with narrative techniques to deliver insights in compelling, easily assimilated forms. Analytic data stories aim to prompt discussion and collaborative decision making.” Since 2019, data storytelling has moved through the “peak of inflated expectations,” with an estimated plateau of productivity in two to five years. In 2022, the topic was coasting towards the “trough of disillusionment” (which is perhaps where I am personally in my relationship with the phrase!). 

The rise in interest has come with increased client demands for data storytelling, which brings us back to our original question: Do we all mean the same thing when we ask analysts and designers to do more data storytelling?

Aligning on what we mean by “data storytelling”

In the world of data visualization, we often conceptualize the graphics we create along continuums. Static or interactive. Conceptual or quantitative. Digital or physical. And perhaps most often: designed to enable the user to explore or to explain an analysis finding.

Having a shared language to talk about the kinds of analytical and communication products we’re creating is powerful, and plays out across other kinds of data visualizations. What is a set of interactive charts displayed on one page and designed to explore data? Many people would call that a dashboard. 

Having a term to describe that deliverable helps us envision the same end when we create workplans, collaborate on teams, or hire consultants to build something. While your more narrow requirements for a dashboard may vary, if I ask a group of data visualization designers to sketch a picture of a data dashboard, I’d expect some consistency.

Data storytelling can take many paths and present across many analytical products. Some stories may be more expository in nature, stating facts, while others take a clear narrative arc. Given these different interpretations, we have to be careful and explicit when we use the words “data storytelling.”

Robert Kosara’s definition of a data story from those conversations nearly a decade ago is still one of my favorites and most succinct. A data story…

• ties facts together: there is a reason why this particular collection of facts is in this story, and the story gives you that reason
• provides a narrative path through those facts: guides the viewer/reader through the world, rather than just throwing them in there
• presents a particular interpretation of those facts: a story is always a particular path through a world, so it favors one way of seeing things over all others

The three-pronged definition allow for space to craft both short and long stories. And it’s the last point that aligns so clearly with the declutter-and-focus recommendations of modern data visualization and data journalism.

If we’re tasked with data storytelling, we also have to think about scale. The demand could be for very short stories. We can take the same data (here, sample data on cervical cancer screening coverage), and shape the story with the headline and our design decisions:

Being enthusiastic about sharing a more complete picture, we can also seek out additional context, research, and data to tell a more complete story. 

In the below example, additional text annotations and highlighted timeframes clue the reader into the narrative. Between the descriptive title, the helpful callouts with key bolded words, and design choices including the background color and marked data points, there’s a natural progression—or short story arc—for the reader to follow. (I’ll note that these strategies run counter to my public health training where we titled charts with the indicator, location, period, etc. represented in the data).

But is your analytical product really a ‘data story’?

Words and phrases, like “data storytelling” create opportunities for alignment between clients/users/readers and data visualization designers. When the world is demanding and promoting data storytelling in all cases, it can feel a bit like having a hammer and seeing every data communication challenge as a nail.

I don’t want to squash the enthusiasm for data storytelling. Well crafted data stories can encourage and spark excitement for engaging with data in amazing ways. But I’m also perpetually frustrated by the insistence that every analytical product be designed for data storytelling and the seemingly-endless loop of realigning expectations.

Josh Smith, a folklorist-turned-UXer and former Tableau Visionary, won a spot on the Iron Viz stage with a long format scrollytelling dashboard on farming. Storytelling was on the judging matrix, but Smith argued in an introspective piece, “Is Your Data Story Actually a Story?”, that his submission wasn’t an excellent piece of storytelling: no cast of characters, no clear narrative.

Instead, he points out that “storytelling is often used as a blanket statement to describe how well the information is presented in an interpretable presentation with a logical flow.” Storytelling isn’t always the goal, especially on dashboards designed more for exploration, rather than explanation. Or, designed more for story-finding than storytelling.

Thinking back to our cervical cancer screening data, the single chart with its headline and annotations give me a snapshot of the data. But what if we want to explore and slice the data by location, age, race, or other meaningful disaggregate? What if we want to know which groups saw the biggest drop in screenings during the pandemic?

Then we need a different tool — something interactive to drill into the available data. That’s where a dashboard can help, enabling you to explore and find the stories in the data. The often data dense displays that we see on highly effective dashboards aren’t designed to export beautifully into a slide deck, but they can be great for identifying trends, outliers, causes for celebration, and data points of concern. 

The role of data storytelling in modern analytics

Gartner’s advice: “Task members of your analytics team with investigating data storytelling as an extension to their use of interactive visual exploration and analytic dashboarding. This will provide a richer delivery of information by adding narrative and context.”

As data visualization creators, aligning with our clients and end users at the requirements stage or when you’re setting up your design brief is a critical part of the creation process. Next time a conversation opens with, “We need to do more data storytelling!” probe to understand more:

• Why does the client want to do more storytelling with data?
• Who are they looking to develop analytical products for?
• What is their vision for delivering on data storytelling, and is “storytelling” really their catch-all word for a well-structured analysis product?
• Should the product you build be designed for storytelling or is the goal exploration and  story-finding, as a first step on the path towards more data storytelling? 

Data storytelling is here to stay. In fact, it probably predates many of our careers and will outlive any of us (perhaps with a bit of AI enablement added on). The conversation around data storytelling extends beyond our niche data viz community and is a fun and seemingly simple concept to get excited about, expanding the enthusiasm and the (inflated) expectations. 

Let’s make sure we’re crafting a shared language and set of intentions, particularly with our clients and end users, rather than jumping on the storytelling bandwagon. Because not every piece of data needs to be communicated as a story—sometimes we need to start with story-finding or just a well structured chart, rather than a full narrative arc.

This essay was adapted from the discussion on Chart Chat (July 2023), a monthly livestream hosted by Steve Wexler, Jeff Shaffer, Andy Cotgreave, and Amanda Makulec. You can find past episodes on chartchat.live.

The post Has Data Storytelling Reached Its Peak? appeared first on Nightingale.

No data storytelling, no understanding of AI, no innovation: it is that simple.

As part of a team of data scientists, I’ve watched many organizations fail to scale their AI pilots due to a lack of data storytelling skills. Surprised? Well, you shouldn’t be. Not using stories to explain and contextualize the outcome of AI solutions results in executives struggling to grasp their business value and not making any investments to advance them. I’ve experienced firsthand the consequences of failures in communicating data. 

On many occasions, the charts and analysis my team presented to explain the functionality of ML models and algorithms left the users wondering about their utility and reliability. A wrong decision based on misunderstood data could result in revenue erosion and angry customers. To trust and adopt AI, users and business stakeholders must understand the implications of the data produced by the AI systems on both the organization and people. 

Having spotted this gap repeatedly, I worked on infusing data storytelling throughout AI projects, from strategy to execution. I started by repurposing my role as a data journalist from merely designing data visualizations for AI tools to developing and communicating data and AI strategies. 

Today, many organizations want to become fully AI-powered by implementing systems that improve customer experience and accelerate, optimize, and innovate business processes. However, the fast pace of technological advancement doesn’t go hand in hand with employees’ skills. Insufficient proficiency in reading, interpreting, synthesizing, and communicating data — competencies that define data literacy — prevents organizations from capturing AI’s full potential. 

Let’s be honest: deciphering the value of machine learning models is still a mystery to many business leaders. Trustworthy AI is a priority for many organizations, but often only data scientists can wrap their brains around stats describing models’ fairness or quality. Reading a confusion matrix is still, to most professionals, a challenge equal to codifying the Rosetta Stone. Not to mention translating models’ performance metrics into business KPIs. Forget about it! 

There is still a chasm between business strategy and technology among organizations caused by a lack of data literacy. According to a data literacy survey by Accenture, only 21 percent of employees feel confident in their data skills. 

It’s time to leave behind the assumption that data literacy is merely analyzing data and plotting charts. We need to combine different skills to produce stories that transform data into actionable knowledge and unfold the AI’s complexity. To progress with digital transformation, leaders must rethink the role of data literacy, which has become a fundamental requirement to fathom the world through the new gnoseological paradigms produced by our computational culture. 

We are no longer dealing with only numeracy or graphicacy. Don’t get me wrong: of course, these skills set the foundations of data literacy. However, the automated systems that permeate our lives have exposed the workforce to much more complex challenges that call for new lenses to interpret data: which are no longer only a massive amount of structured and unstructured information, but also the fuel and outcome of the AI systems that people use or are affected by. 

So, how could leaders solve this conundrum? There is no “one-size-fits-all” approach. Yet, by working on data and AI projects, I found that data storytelling functions as an enabler of data literacy. 

During my time at IBM, I initiated many efforts to improve data literacy across the company’s business units and innovate people’s perception of data by bringing them closer to code, algorithms, and numbers through visual stories that read data but speak humans. Eventually, the initiatives I led culminated in the launch of the company’s first data storytelling-certified learning program.

Whether or not enterprises will succeed with digital transformation depends on their workforce’s ability to transform data into new opportunities, so leaders, you’d better act! Here are a few steps you might want to follow, and I’ll tell you why.   

Disperse data competencies

The Data Literacy Project by Qlikq — a software company— reports that enterprises that have higher corporate data literacy scores can have $320-$534 million in higher enterprise value. Yet, few companies invest in data literacy efforts. According to Gartner’s 7th Chief Data Officer (CDO) Survey, only just over half of respondents (53 percent) said that they invested in the area of data literacy during 2021, while less than a third (29 percent) reported having successfully seen “ROI from data and analytics investments.” 

Part of the problem lies in leaders making isolated investments in analytics teams instead of approaching data literacy from a holistic perspective: they often build data science communities and programs, but don’t implement a data literacy strategy addressing the entire workforce. Leaders should disperse data skills across business units by decentralizing the expertise of technical practitioners in data science, IT, analytics, or CDO teams. 

Yet, making data competencies fluid also includes investing in education. Fostering a shift in how data is perceived and evolving the skills needed to become data literate leads people to discover new pockets of growth and generate new ideas to achieve the company’s strategic objectives more effectively through analytics. 

Given that the vast majority of people — from HR to Finance, from Design to Sales — work, directly or indirectly, with data and algorithms, data literacy should be incorporated through learning programs suited to the challenges employees face in today’s ever-changing digital enterprises. 

Teach data through rhetoric 

Training employees on unrelated technical skills like data analysis, coding, and data visualization doesn’t necessarily bring data and algorithms into their workflows: it’s like learning a language’s grammar without putting it into practice in everyday life by writing and speaking. ​​By crafting data into narratives, people give purpose to the skills they learn and articulate them under a well-defined intent: communicating.

Data has become our tool for communication and expression. This is why we should perceive data as a language rather than merely a collection of digital information. To see clearly through the world and codify its dynamics, everyone should be proficient in the language of data. 

As with language, where rhetorical thinking gives strategic purpose to the words, in the same way, rhetorical thinking applied to data gives strategic purpose to it. Thinking rhetorically about data brings us to discern the available means of analytics and combine them with ethos, logos, and pathos —data expertise, logical arguments, and emotions— to argue, communicate and persuade. MIT professor Catherine D’Ignazio and research scientist Rahul Bhargava include arguing with data among the abilities defining data literacy, recognizing as crucial being able to “use data to support a larger narrative that is intended to communicate some message or story to a particular audience.” 

For over two years, I was in charge of curating and managing an internal newsletter to report my team’s achievements. I used that project to convert the traditional newsletter format into a data-driven one. I experimented with creative ways to uncover overlooked stories hidden in data by leveraging Natural Language Processing to extract deeper meaning from the text of the stories I published. Making data less intimidating with aesthetics and rhetoric helped non-technical experts approach the realm of analytics, while revealing the human side of data to data scientists and engineers.

Data storytelling is a means leaders should employ to promote the rise of what Tsedal Neeley and Paul Leonardi define as a digital mindset: “a set of attitudes and behaviors that enable people and organizations to see how data, algorithms, and AI open up new possibilities […].” In fact, by intriguing people with unusual visual stories, the newsletter got more than 20K subscribers, raised cross-disciplinary collaborations, and spread new ideas on employing data and algorithms in mainstream business operations.

Bridge the divide between analytics and business  

Crafting data stories that visually narrated the what, why, and how of AI models proved crucial to complete the hard work of data scientists and connected algorithms to the users’ daily experience through stories that they related to.  However, visually explaining AI solutions is not enough. 

To capture value from data and AI, leaders must develop a clear vision that aligns the technology’s intents with business and people’s needs. This is not an easy task, given the gap between AI and business: that’s why data storytelling is critical. 

By researching this realm, I expanded the purpose of data storytelling and transformed it into a strategic tool to frame AI use cases through the lens of people’s needs. By combining data design with design thinking, I transformed workshops’ insights into data and data into visual stories allowing both technical and business stakeholders to gain a common language to translate complex analytical scenarios into a clear vision for their AI strategy. Elevating data storytelling to a strategic tool sparked a collaboration with the IBM Design Program Office that gave birth to a new design thinking framework that fundamentally changed how the organization addressed data and AI problems with a human-centered method. 

By building bridges between unrelated domains, stories allow different experts to open up a dialogue on data to understand why and how to embed it into the fabric of their organization.

To bring analytics to the heart of the enterprise strategy, we must make an effort to weave data into people’s daily work through stories. As one of my favorite Harvard Business Review articles reads: “The ability to present data insights as a story will, more than anything else, help close the communication gap between algorithms and executives.” 

Breach your organization’s guardrails 

We can’t improve how data circulates in companies without getting rid of guardrails that restrict its domain exclusively to analytics teams: this holds organizations back and can hamper innovation. Leaders should combine data scientists and engineers with professionals who, by crafting stories, can activate data into the business dynamics. 

To innovate mainstream workflows and develop new approaches to analytics, we should try to infuse data storytelling into every project we work on, independently from the role we cover in the organization and whether or not we are data scientists. 

Over the years, I collaborated with developers, data scientists, designers, and even executives, to expose me to their approach and expose them to mine. For example, I started developing interactive narratives adopting techniques like scrollytelling to make quarterly reports more engaging, instead of using traditional formats like reports, slides, and spreadsheet tables. I positioned storytelling at the core of the development of a new series of sales and revenue dashboards to let stories drive the understanding and design of the data the executives wanted to see to efficiently run their business. I even brought data storytelling into the software design team, where we explored how it could reinvent UX practices. 

With time, the adoption of data storytelling raised people’s interest and curiosity. Many started asking for mentorship and coaching sessions. I understood it was time to develop an official learning program.

Catalyze a digital mindset

Although practicing is vital, a well-structured learning program is also needed to develop a data storytelling mindset and enhance the workforce’s data literacy. Even the MIT Management Education Executive program has a module on communicating data through storytelling.

By realizing the need for scaling data storytelling skills, I partnered with two experts — one leads IBM’s Data Science Profession worldwide, while the other is a Principal Data Scientist and Data Visualization Researcher— to devise the first data storytelling curriculum for IBM’s employees. We designed it as a set of three modules —associate, professional, and advanced— to offer everyone the choice of how deeply to get into the learning content. In fact, people don’t need to acquire the same skills: different roles call for different levels of knowledge to work with data. Not everyone must become a Machine Learning engineer or a data scientist! The intent of the program was to establish a baseline for data and AI skills from which everyone could start learning and progressing. 

To teach how to craft data narratives, we assembled in a single program skills that usually are not taught together, ranging from data analysis with Python and machine learning fundamentals to data visualization design, visual storytelling, design thinking for data and AI, and AI explainability. 

We laid out the learning program with the support of the IBM Data Science Profession and the IBM AI Skills Academy to also get executives involved in the data literacy effort and raise awareness of its importance. Since its launch in December 2021, the program has contributed to infusing a data-driven culture and has been attended by more than 1,700 people. 

I see this program as a stepping stone for people to discover new opportunities and uplift themselves thanks to the data abilities they acquire that bring them to fulfilling their daily job more creatively, efficiently, and faster. By training people to use data as a rhetorical device, which comes with thinking critically and strategically about it, companies will nurture a new generation of leaders. By embodying both the analytical and rhetorical side of data, these new leaders will respond to arguments with clear and thoughtful communication by using data to back up hypotheses and challenge assumptions. These data-savvy humanists will be the ones that will keep algorithms in check to preserve the governance and trustworthiness of enterprises’ models; they will be the future of organizations, the ones pushing them forward by transforming data and algorithms into great visions illuminating companies’ path towards a responsible digital transformation.

The post Data Storytelling Is the Conduit for Modern Data Literacy appeared first on Nightingale.

Kat proposes a framework that can help you find and define your hero. As part of this framework, you should ask yourself three key questions:

1. Am I trying to discover insights for myself?
2. Am I trying to inform others?
3. Am I trying to educate others?

When the hero is you, your job is to find a story. 

When you’re discovering insights for yourself, you’re like a detective, using data to find the story behind the facts. As you search, you compose new questions. But you’re not working with physical evidence, your job is to find your clues in graphs, charts, and tables. As you search, new questions emerge. As you collect, collate, refine, revisit, and organize facts, patterns appear. That pattern is the back-beat of your story. 

Once your discovery is done, and you find your story arc, you have a story to tell. 

When you’re informing others, present facts.

When your hero is somebody else such as a client that needs information, your job is to supply the facts, not tell a story. You’re Agent M, feeding James Bond information along his way. Bond, James Bond, is on the Hero’s Journey. In business, James Bond is the project manager who needs a Gantt chart, the sales manager who needs a pipeline breakdown, or the CFO who needs a cash flow statement. 

When you’re educating others, you’re a storyteller.

When your hero is hapless, helpless, or naive, you must become a storyteller. Your job is to change hearts and minds. You must: 

• Find a story (return to Hero #1)
• Choose visualizations that create “aha” moments
• Find vivid words for annotations
• Select compelling colors, callouts, and comparisons
• Write titles that stick
• And so on

Like writing a book, storytelling with data is grinding, detail-oriented, and sometimes thankless work. I changed the title of this article ten times — is it the best one, the one that will stick? I’m still not sure and tortured that there’s a better one.

You’re on a Hero’s Journey, too.

Whichever path you’re on, you’re on a Hero’s Journey, too. As you stumble through ideas, you discover what you’re trying to say. As you explore, you enlighten yourself so that you might enlighten others. As Issac Asimov said, “writing is thinking with my fingers.” The same holds for you when you work with data. 

You’re on a Hero’s Journey when you’re a data storyteller. You’re out to change your hero’s mind. And you’re on the journey together. 

The post Start with the Hero, Not the Story appeared first on Nightingale.

Over the past two years, our team at NLC built this into a powerful data storytelling tool with almost 5,000 policies tracked across 800 cities. Through the process, we not only gathered invaluable insights into innovative city policies, but we also discovered the power of data storytelling to democratize shared lessons, improve local advocacy, and evaluate policy interventions.

Democratize shared lessons

The early stages of the COVID-19 pandemic saw a proliferation of data graphics highlighting trend lines that showcased the change in case numbers, death rates, and other relevant public health information. These types of data visualizations were incredibly valuable for the general public and for city leaders to assess the situation within their communities. Building off these baseline statistics, our Local Action Tracker combined data-driven trends on popular policy areas with narrative-oriented examples of how cities were addressing their public health, economic, and social challenges. Rather than reinventing the wheel for good policy initiatives, local elected officials were able to identify relevant insights from peer cities and apply them in their own contexts. For example, many cities, ranging from Nashville, Tennessee to Puyallup, Washington, partnered with local businesses to provide gift cards as an incentive for receiving vaccines last summer.

Data storytelling humanizes aggregated, high-level statistics and roots them in the reality of the lived experiences of the stories’ “characters.” Faced with sharply dropping city revenues and increasing expenditures, we saw mayors, council members and other local leaders navigate uncharted waters. City leaders in Cincinnati, Ohio, took a pay cut to their own salaries amidst revenue shortfalls and prioritized city budget spending on supporting those most impacted by the pandemic, such as local small businesses.  By showcasing their stories using data in real time, we were able to help them navigate these challenges together and democratize their shared lessons.

Improve local advocacy

In addition to humanizing high-level statistics, data storytelling also has the power to connect the dots of individual anecdotes and weave them into a compelling thematic narrative. For example, it was immediately clear from the beginning of the pandemic that cities would face added financial pressure due to an overall decrease in economic activity. However, it was not clear how much this would impact cities. There were stories of some cities whose budgets had been hit exceptionally hard while other cities appeared to have experienced only minor setbacks. So, what was driving these differences?

Our data storytelling efforts uncovered a key storyline demonstrating the critical role of municipal tax revenue structures, as cities heavily reliant on more dynamic sources such as income and sales tax saw more immediate financial impact compared to cities dependent on property tax. Overall, our research showed that cities in 2020 experienced $90 billion in revenue loss. This data-driven insight empowered cities to collectively advocate for federal assistance to ensure that essential city services were maintained. The financial challenges that they faced were not one-off examples, but rather part of a collective story that, when amplified, eventually led to the passage of the American Rescue Plan Act (ARPA) State and Local Fiscal Recovery Funds, which provided more than $65 billion to all municipalities across the country.

Evaluate policy interventions

Data storytelling is an effective tool to both find human stories amidst large datasets and identify data-driven themes woven throughout individual anecdotes. It can also provide us with a lens to reflect and evaluate where we have come from in order to inform best practices in the future. Our COVID-19 Local Action Tracker recently celebrated its two-year anniversary. In celebrating the occasion, we collected secondary data indicators that measured the top five key themes of policies that cities had pursued – public health, city operations, infrastructure, housing, and economic/workforce development. In the graphic below, we examined how local government employment numbers had fluctuated during the past two years and were able to demonstrate the effectiveness of ARPA in significantly increasing those job numbers. We can thus see that there is significant value both in leveraging data storytelling in real-time to shine light on current scenarios and in using it as a reflective tool to identify successful policies.

Our team’s experience building out the COVID-19 Local Action Tracker during the past two years has taught us valuable lessons about how to use data to tell city stories. We empathized with our stories’ heroes recognizing them as more than dots on a scatterplot and instead as public servants fighting for their local residents. We energized cities so that they could see themselves as more than singular entities and instead as part of a broader community collectively experiencing a similar storyline. And with the benefit of hindsight, we evaluated the success of both federal and local policies to identify and promote best practices for cities to adopt moving forward in building back better from this pandemic.

The post Telling the Story of Urban Innovation and Pandemic Response with Data appeared first on Nightingale.

The aim of this project was to translate the film script of Dog Lady (La mujer de los perros) – an Argentinian movie by Laura Citarella and Verónica Llinás – into navigable pieces of schematics and data visualization. 

In my illustrated projects I usually use high degrees of detail and sensitivity, so it was a big challenge to translate concrete and hard data into a soft and sensitive language. 

In my work, I´ve always preferred to go from the small to the big, from the particular to the general. I formulate those units as cellular microorganisms that constitute a whole. I strive for the close look, the relief in the detail, and the confidence in the repetition. 

I’ve found a great revelation in the translation of my illustrated project methodology to data visualization. This practice allows for infinite combinatorial creativity, crossing data to generate information, weaving a story, playing at being a cartographer of the subjective. 

At the end of the day, a scheme is a rapport of information, a mesh of data. 

The challenge of the unassimilable arises from the need for information not to be limited to material elements such as locations, costumes, or inventories, but to run the risk of trying to account for the invisible, the non accumulative, the subjective: that emotional atmosphere that is sewn and it sneaks into the interstices of that world of apparent material sequences. 

Sometimes, color and textures reach places that language does not allow. 

And in the useless attempt to escape words, ask yourself: Where does time accumulate? What does the cold look like? What smell does silence have? In an increasingly convulsive and quantitative world, where every action seems to unleash a river of statistics and algorithms, where everything seems calculable and reducible to a number, perhaps it is a relief not to find answers.

The post SPOTLIGHT: Visualization of Dog Lady (La mujer de los perros) appeared first on Nightingale.

Dataviz professionals are known for getting excited about the intricacies of charts, fonts, and color schemes, or for getting into heated disputes about the pros and cons of pie charts, 3D graphics, and other visualization tools. What we aren’t necessarily known for, though, is telling compelling stories. That’s a problem, because while raw information — no matter how well it’s packaged and displayed — is soon forgotten, an effectively told story can grab people’s attention, change their outlook, compel them to action, and linger in their memory for weeks or months to come.

As we in the dataviz community look to 2022, then, it’s time to commit to making some changes. There will always be a place for obsessing over design details, and making numbers come to life with impactful visualizations. But we also need to remember the big picture, and commit to using all our dataviz skills and design savvy to foreground and amplify the power of the stories that our data tells. 

What does that mean in practice? Here are four ways that dataviz teams can start putting storytelling front and center: 

1. Narrative, not just numbers 

You can use a pie chart to show that you bought a dozen apples, of which seven were rotten, with admirable clarity. But imagine how much more vividly you can communicate those facts if you turn it into a narrative: “Imagine my horror when I bit into the luscious-looking apple and found that it was rotten! Not once, but again, and again, until seven brown, mushy apples lay discarded in the trash.” 

Narrative is powerful because it can create urgency and instil simple information with visceral energy — it gives a sense of the stakes, and an emotional connection to the data underlying the story. That’s important because people don’t typically think in terms of information; they think (and, just as importantly, make decisions) based on how they feel about information. Your job, as a dataviz storyteller, is to use your skills to forge those emotional connections — and that starts with understanding and prioritizing the narrative, not just the numbers. 

2. One graph, one story 

Of course, that doesn’t mean every narrative needs to be crafted with words. We’re in the data visualization business, after all! But the graphs and charts you create should be designed with a story in mind — and that means making sure each graph or chart you present has a clear viewpoint and single clear narrative. After all, the goal of a data visualization isn’t to present all the data you have available. It’s to present the right data, in the right way, to communicate a single compelling message. 

To see why this matters, consider a pie chart: if you keep things simple, and offer up a chart with just two categories — “People who love pie charts” and “People who hate pie charts,” say — then you can use that chart to tell a clear story. Start adding in more categories — “People who eat pie” and “People who can remember pi to sixteen decimal places,” for instance — and you’ll only confuse things. Staying focused is usually the best way to tell an effective story.

3. Less is more

Once you’ve figured out the story you’re trying to tell, and managed to avoid throwing all the data you have into the mix, the next step is to pare things back even further. All those charts and fancy design features might look pretty — but are they making the story more compelling, or just cluttering things up? Remember, the goal isn’t to show what a great designer you are — it’s to tell the story in the most effective way possible.

That might mean jettisoning a beloved 3D bar chart, and opting for a simpler, cleaner line graph that ultimately does a better job of communicating the story at hand. It might mean using a more vivid color scheme, rather than aesthetically pleasing pastels, so that casual readers can quickly grasp the most salient facts. Above all, it means being willing to kill your darlings, and to purge your dataviz of anything that doesn’t directly and effectively serve the story you’re trying to tell.

4. Persuade, don’t tell

A key step toward data storytelling is the realization that our job as dataviz pros isn’t just to tell people things — it’s to persuade them of a particular viewpoint. The raw data is simply a collection of facts: if our goal were merely to transmit those facts, we could simply display a table listing columns of numbers. The magic happens when we start interpreting those numbers, shaping them into stories, and persuading people of the power of our point of view.

It’s our job not merely to repackage data, but rather to understand why the data matters in the first place, and to reveal the powerful stories that lie beneath the numbers. Have confidence in the value of your viewpoint, and work to convince rather than merely to inform.

Serve the story

The key insight here is that data visualizations are storytelling tools — so as dataviz specialists, we need to become the best storytellers we can be. That doesn’t mean putting our design knowhow on the backburner, but rather putting our design skills in the service of the narratives we put forward, and making our storytelling as efficient and as effective as possible. 

Done right, data storytelling doesn’t just inform: it reshapes the way that customers, colleagues, employees, and decision-makers understand their world. With effective storytelling, data becomes more than just numbers: it becomes a way to convince, persuade, inspire, and incite to action. That’s something we should all aspire to — so let’s step up, and put storytelling at the center of our dataviz strategies in the coming year.

The post Effective Data Visualizations Should Focus on Narrative, Not Numbers appeared first on Nightingale.

I found myself focusing deeply on the word “dashboard,” wondering if that’s indeed what my client had in mind and whether that type of display would truly be persuasive. You see, when someone says dashboard, I think of COVID dashboards like the one above that we’ve all become accustomed to seeing (and, admittedly for some of us, to nervously checking at regular intervals). 

In addition, there are the modular-looking dashboard displays used for internal monitoring purposes  – for example, a business dashboard to summarize KPIs (key performance indicators) for senior management.

In the health field, in particular, dashboards have become commonplace. This very journal, for example, featured a post by Tricia Aung on the “The Global Health Dashboard Epidemic” – and that blog post was published about a year before the pandemic brought to us the proliferation of COVID dashboards. 

So, you may ask: What’s my issue with dashboards? Some may point out that dashboards are a terrific way to display findings, certainly better than showing a singular graph to summarize a complex topic. 

I agree that dashboards have their place, but here are a few things that I find problematic with a typical dashboard display: 

• Dashboards prioritize the graphs, meaning there’s typically no room for context or explanation about the significance of the overall findings.
  
• Dashboards don’t often have enough space to explain the meaning of individual visualizations. It’s as if we’re supposed to be familiar with what each indicator is conveying, including its acronyms and other inside-baseball terminology.
  
• There’s often no sense of hierarchy with dashboards. All visualizations are given equal prominence. 
  
• And some common elements that we leverage in data storytelling to help readers better absorb and find meaning in the information – narratives about individuals, quotes, photos – are foreign to most dashboard displays.

That’s not to say that all dashboards should be banished. With COVID, for example, the use of dashboards is appropriate, given that we all quickly became familiar with the pandemic’s data terminology. For that same reason, a car dashboard works well; we collectively understand what a fuel gauge or speedometer is communicating.

The same reasoning applies to dashboards used internally in business settings – for example, at the start of monthly meetings to help a division of a company know where to focus efforts. After all, when everyone on the team is well-acquainted with the measures, context isn’t necessary, so dashboards work well.

But what about that new employee who needs to be onboarded? It would be helpful to educate them about the significance of the measures on the company’s dashboard. 

You see, in so many instances, our work as data visualization practitioners require us to explain concepts that are not as familiar as a car’s speed or COVID hospitalizations. In other words, much of the time when we communicate data, we don’t have the luxury of the user knowing what we know, because we’re talking about a topic our audiences don’t understand. There are many occasions, too, when we won’t always be able to control where, or how, our data is shared.

The bottom line is that we want to be able to compel people to take an action with the information we’re sharing, even if we’re not there in the room or on the Zoom call to describe the data, and that means that we need to explain and unpack the concepts for the visualizations we’re sharing. 

It’s in these settings that it would be helpful for us to realize that dashboards may not be the best display format. In fact, I’d go one step further: 

Dashboards ≠ Data Stories

The sooner we recognize that dashboards don’t adequately address the assignment we’re often given as visualization specialists – “create a story from the data” – the more willing we’ll be to consider formats beyond the dashboard. Sure, our colleagues not steeped in dataviz may say they want a dashboard – as my client above did – but that’s often because dashboards have become shorthand for data display these days.

It’s our job as data practitioners to find the right format by interviewing end-users and lifting up the actions our clients want audiences to take with the data. If we always fall back to the familiar dashboard display, our efforts may fail. The end user won’t understand the significance of the findings and won’t be persuaded to take an action, which means our work to find, analyze, and visualize the data will have been for nought.

Fortunately, we have many options for how to display data. To assist with this work of expanding beyond dashboards, I recently catalogued other display formats for a continuing education course I’m teaching through George Washington University, “From Spreadsheet to Story: A Step-by-Step Guide to Communicating Data.”

Here are some data story formats I highlighted for that course that can be more helpful than a dashboard can at driving towards action: 

The Fact Sheet: Often geared for print consumption, this one-pager highlights – and explains – some key findings that can be shared as a print handout or attached to an email.

A Slideshow: The emphasis here is on chunking out data findings into snack-size bites of information, so that you’re providing helpful context without describing too much at once.

An infographic: Sometimes cartoon-like in its presentation of information, an infographic often will dive into a specific topic by providing an abbreviated summary of key data findings.

The Persuasive Narrative: In my mind, the purest form of data storytelling is when you relate a story about an individual and use data to describe how this issue impacts more than just this one person. A persuasive narrative is one such way to humanize the data, telling an individual’s story from problem to potential solution. 

A Q&A: I particularly appreciate how the story linked below uses a combination of questions, then small blurbs + data, to answer them. The questions provide a useful cadence that leads the reader through the material.

A List: People engage with lists, such as the ‘top five things to know’ about a topic. Perhaps when we know the end point (e.g., you are on fact, seven out of 10), we’re more likely to digest the findings by reading a paragraph of explanation.

A Data Game: When we transform data into an intellectual exercise or quiz, it’s more likely that our information will be remembered and shared. Simply put, people like participating; it’s often far better than just talking at people with data.

Long-Form Scrollytell or Data Movie: Sometimes we have a lot to say, and need digital space to let the data breathe. There are some wonderful examples of long-form data stories, including “The Desperate Journeys of Rohingya Refugees.” By leveraging a combination of photos, artwork, audio clips, data maps, and graphs, this data story from kontinentalist educates us about the plight of 2,400 Rohingya who have fled Myanmar and Bangladesh. 

Another one of my favorites is this poignant data movie about World War II.

I’m sure this is an incomplete list. Know of any other data storytelling formats? Please share. I’d love to see what other forms of data stories exist out there in the wild.

The good news for those of us attached to our dashboards is that it actually doesn’t need to be an either/or choice between data stories like the examples above and dashboards. I often advise my clients that they can start with a data story to educate and explain. Then, you can let readers explore the findings on their own through an interactive dashboard that allows them to find their own narrative.

So rather than: 

Dashboards ≠ Data Stories

Let’s think of it as:

Dashboards & Data Stories

What did I end up doing for that community health collaborative project, you may ask? Well, the project is still very much in progress, but the template is taking shape as a fact sheet, one that leaves space for quotes, poignant photos, and other forms of content that help paint the data on a broader canvas than a dashboard can and help steer audiences to take an action that’s important to the health collaboratives.

The post Dashboards Are Not Data Stories appeared first on Nightingale.

What it is

Data Visualization: Storytelling is a 1.5 hour course on LinkedIn’s smart education platform, which delivers content previously found on Lynda.com. If you decide to take it, you’ll be guided through the why’s and how’s of data storytelling by data visualization expert Bill Shander.

As you’d hope, the story arc of this course is elegantly structured, demonstrating in itself many of the methodologies presented in its content. The course videos, which are the primary mode of delivery, accompany the instructor’s voice with relevant, quality visual material and interactive transcripts. I hadn’t used LinkedIn Learning before, and I was pleasantly surprised by the user experience. There are a few nice additional features such as sections for personal notes, discussions with the course leader and other attendees, and a list of recommended courses to aid you in continuing your learning journey. Most importantly, the primary functions (video and transcripts) worked flawlessly for me across all my devices, allowing me to digest the course content with ease.

The first of the four main sections of the course – “Why Storytelling?” – makes a compelling argument for why we, as data communicators, should care about storytelling, as well as defining situations when it isn’t needed (for example, when the content is “reader driven,” as in a dashboard). The following two sections, full of case studies, progressively reveal the theory and application of “Story Structure” and “Story Mechanisms.” I found this to be very well paced. The “Final Touches” delivered in section four effectively wrap up the ideas and include many tips for data visualization best practices.

Who it’s for

“This course is intended for anyone who works with data and has to communicate it to others.”

course description on LinkedIn Learning

The course is designated as intermediate, but I wouldn’t hesitate to recommend it to anyone with an interest in the topic. I believe that the content is structured in such a way that beginners could gain an insightful overview from it. Taking the course doesn’t require any technical knowledge or skill. I encourage you to give it a listen even if it would be your first encounter with data communication.

That said, those who  are actively working in data communication would probably benefit most from the course, because they would be able to apply the techniques to real life problems. This is not a beginner course offering in-depth training on data analysis or visualization. It’s a guide to the specific practice of data storytelling, which encompasses a number of related but often separate professions. As a designer and engineer of data visualizations, I found it gave me insights into the thought processes an analyst might undertake, and I recognized many that I undertake in my work. It was useful for me to see them knitted together through the overarching framework of storytelling. Moreover, I was left convinced that I should invest more energy into refining this essential craft.

What it costs

The time investment, at 93 minutes, is nothing short of a bargain. Of course, there are likely to be sections you’ll want to re-listen to and times you’ll want to pause to read the transcript, make notes, or dig deeper into specific topics and case studies. All in all, I spent around three hours with the material, and I expect I’ll go back to it again in the future.

I also feel that the LinkedIn Learning platform is well worth its financial investment. Many organizations offer it to their students and employees free of charge, but if you find yourself needing to pay, it is included in the LinkedIn premium subscription, which offers a one-month free trial and both monthly and annual plans. Membership gives you access to thousands of on-demand courses “taught by real world professionals,” like Bill. You can get more information, or subscribe, at www.linkedin.com/learning.

What it delivers

This course is a valuable resource for establishing or refining the craft of data storytelling. It is high-level and wide-reaching in application, yet absolutely not superficial.

“Short and unbelievably insightful course which does not give you a set of Do’s and Don’ts but gets you thinking”

Course participant Saloni Yadav, via LinkedIn

I came away having learned a bunch of new terminology and methodologies, as well as with much food for thought. For such a short and pleasant course, I was very impressed with how much rich material it covered. If I can harness the material, I am confident that my data communication work will be more effective as a result.

Bill Shander is not only an expert in data visualization and data storytelling, but a highly experienced teacher and speaker. If, like me, you’re keen to develop or refresh your professional skills in these areas, you can check out his other offerings at www.billshander.com.

The post REVIEW: “Data Visualization: Storytelling” on LinkedIn Learning appeared first on Nightingale.

Why does Kontinentalist exist?

Kontinentalist didn’t set out to be data-driven. Our director Lee Han Shih (a retired journalist with 30 years of experience) and I shared a common passion: we wanted to tell stories about Asia on its own terms. Having learned about Eurocentrism and Orientalism as a history major, I wanted to help unpack misconceptions and myths about our region’s past and present. 

We founded Kontinentalist with a simple purpose: to tell the story of changes in Asia, make quality information accessible, and keep communities updated on what’s happening around them.

Stumbling into data visualization

Our transition to data visualization started with China’s Belt and Road Initiative. Announced in 2013, the initiative seeks to reinvigorate historic trade routes, cultivating multilateral economic cooperation and connectivity with infrastructural developments. A juggernaut of a project set to roll through three continents, it was massive and controversial—but there was so little reliable information on it out there. We felt this needed to change.

Having worked in museums before this, I knew next to nothing about tech, data, and design. To tell a data-driven story about the Belt and Road Initiative, I would first have to teach myself how to collect data and tell stories with it.

So, the team signed up for lessons on datajournalism.com. I remember feeling mind-blown by Reuters’s “Connected China” project and South China Morning Post’s multimedia visual explainers. Needless to say, we were hooked. We bought data visualization books and observed the best in the business: the New York Times, The Pudding, Washington Post, and many others. 

It was an intense process of self-learning and emulation. We practised our newfound skills with our first map-scrolly piece, Understanding the Belt and Road—and the rest is history. 

Finding a community here in Asia

The deeper we got into data visualization, the more we felt challenged by how out of reach the industry’s resources and communities often were. When we “discovered” the data visualization scene, we wanted to join conferences, meet new people, and learn through on-site courses… but so much of this stuff only happened in the Western world. Even when events were digital, they were held in unearthly hours for our time zones. 

This made our learning journey quite lonely for a long while. But it also pushed us to look for companions nearby! We found the Singapore Data Viz meetup group and started befriending folks there, and this helped us feel a sense of belonging. In the past few years, the data visualization scene in Asia-Pacific has been quietly growing. We’ve found new friends such as Punch Up in Thailand, Open Development Mekong, Synthesis, Nugit, Gurman Bhatia, and many others. 

But we wanted more for Asia’s data visualization community. What else lay out there?

The Outlier 2021 conference

When the Data Visualization Society (DVS) announced their plans for their very first conference, “Outlier” (with sessions we could attend from our time zone!), we were stoked. We’d been members for a while by then, and we were—and still are—always grateful that such a wonderful ground-up organisation existed for the community. 

We wanted to contribute to the collaborative, open, and sharing culture of DVS. When it called for informal “unconference” sessions, we saw it as our chance to add something special, unique, and fun. As a shot in the dark, we decided to list “Are you an Asian snack?” as a theme, hoping that the universal love for food would help us find some common ground among conference attendees. 

Frankly, we were worried that no one would show up. As it turned out, we were dead wrong—a full 20 odd people showed up, snacks in tow and eager for some good conversation. The level of participation was mind-blowing. Although we had planned for a casual and fun session, the conversation started to get real serious. 

We spent more than an hour talking about the challenges faced by Asian dataviz practitioners and the scene in Asia. We talked about language barriers, cultural misunderstandings, and gaps. Trying to fit into an editorial world and style centred on the West. Our hesitation to promote ourselves and our work because it felt so un-Asian—or because we didn’t feel we had the right to do so. 

Many of us didn’t feel like we stood a chance at international awards in our own field. Could we start our own awards? What would that look like? Time passed, we overran our slot, and still it wasn’t enough. We left feeling like we’d barely scratched the surface—but we’d also found a community, and we wanted to do more. 

What’s happening in the Asian dataviz community?

After hosting that unconference session, we felt we needed to continue the work. We went back to the drawing board, had more deep discussions with colleagues and friends, and identified some main challenges to tackle.

Asia has a big data gap. There is plenty of data about Asia, but most of it is dispersed. Countless non-governmental and nonprofit organisations in Asia invest their money into research and data programs each year, but where does this data go, and how accessible is it? Who reads it beyond its niche audience of agents and researchers?

Most Asian countries rank low on the Global Open Data Index; there are plenty of reasons for this. Between a lack of publicly available official data and poor freedom of information legislation across the region, it’s no surprise that data visualization work is not as well-established here—there is simply less open data to work with. 

Asia’s language diversity is one of its greatest strengths, but it’s also often a stumbling block to data work. Most resources on working with data are published in English, with only a small number in other major languages. Many Asian dataviz practitioners, especially those doing work for smaller communities, cannot access this material. Their work is also less likely to gain recognition without sensitive, high-quality translation—which is often expensive and time consuming. 

These challenges extend even beyond the data itself. How might we bring an Asian perspective to frameworks, methods, and thinking in data visualization, so often curated through a Western lens? As a history enthusiast, I’m excited by the rich history of Western data visualizations—but where is this history in Asia? There are some amazing examples of data visualization from Japan (shout out to RJ Andrews), but we can’t help but wonder if there’s more out there, waiting to be discovered. 

Geographical borders, for example, did not exist as a concept in pre-colonial Southeast Asia—are there ways to visualise the region’s histories without them? How about cultures with their own units of measurement or unique ways of telling time? What Asian motifs or forms lend themselves to data visualization? 

We have witnessed this ourselves: data visualization is a creative and experimental medium, and those characteristics have allowed many Asian practitioners to add their own cultural elements to their work. We’ve seen how traditional Asian art forms can be used for data visualizations, such as henna art, embroidery patterns, calligraphy, and more. 

Our hopes and dreams for Asia’s dataviz scene

Economic observers often talk about Asia’s potential to shape the world. We agree, and not just because of our region’s rising affluence. Asia’s diversity will be—has already been—a major game changer in so many fields. Our cultures hold new perspectives, creative ideas, and fresh information—and the world is paying attention. 

As data practitioners in Asia, we have front-row tickets to this incredible transformation—even as we each bring our gifts to the world stage. Kontinentalist is a small entity, and our reach is modest, but we’re working hard with our fellow dataviz practitioners here to address the challenges we all face.  

We’re starting by partnering with as many causes as we can to bridge the gap between their data and the public. We want the NGOs, non-profits, and research groups here in Asia to realise the full potential of their data. We can all do good work with this, even if official government data remains hard to come by. 

We’ve been chipping away at this for two years now. Our latest collaboration with Médecins Sans Frontières/Doctors without Borders on the Hepatitis C epidemic, for example, is meant to help uplift causes that we believe deserve more attention. 

Championing Asia’s cultures through data is also a big part of the work that we do. People often say it’s difficult to tell stories without good data, but many don’t realize that they’re only looking at quantitative data or assume that data must be statistically tested or peer-reviewed for it to be usable. 

At Kontinentalist, we challenge this by developing stories that reveal the data all around us. Data is in our languages and cultures; we just need to take a closer look. We use our platform to spotlight aspects of our cultures that we love and celebrate. It’s also an incredible opportunity to be at the forefront of this change and to see our own cultures get the representation they deserve—delivered with proper context, nuance, and visuals that capture their spirit. 

Since Outlier 2021, we’ve been wondering how we can contribute to this newfound community and help keep its momentum going. Our new friends had so many personal tales to tell, and so many lessons to share, that we decided to dedicate our “Community” series—an ongoing series of interviews—to Asia’s data practitioners and enthusiasts. We want to amplify their work and create an affirming space where members of our community can connect with one another. 

It’s a small effort right now, but we hope to start a movement with it. Asian data practitioners should be proud of their identity and their work. We hope our platform encourages them and allows them to share their challenges. As the world comes to recognise the need for diversity and inclusion, these voices have never been more important. 

This is only the beginning. As Asians, we often joke that we’ve been brought up to achieve good grades and overachieve academically. But this shows up in the data landscape here, too. Most data visualization courses target business analytics, dashboards, or Big Data. We hope to blur the lines between business, science, and art, and to show that there is more to the industry than meets the eye. We have plans to launch data visualization workshops in Singapore for newcomers by the end of the year, with the aim of connecting them with experienced professionals so they find a community that keeps them going. We hope that with this small but close community, they may push the boundaries of imagination and create unexpected, beautiful data stories.

Asia’s dataviz scene is coming into its own. It may seem nascent, but this just means there’s greater flexibility and room for change. We want to be with it as it grows and succeeds. Many people have reached out to affirm our efforts and let us know we’re on the right track; we hope you feel the same. Join us in growing this movement—or even start your own! 

The post Asia’s data scene deserves greater attention. That’s why we are starting a movement. appeared first on Nightingale.

Embracing empathy in data and data visualization is a key dimension for people working with data to help put their work into the hands of policymakers, stakeholders, and community members who can use it to affect change. Inclusive and thoughtful data visualization that respectfully reflects the people and communities of focus can also help researchers build trust with those communities.

We think of empathy as it applies to communicating data across six main themes:

1. Put people first. First and foremost, we need to remember and communicate that the data shown reflect the lives and experiences of real people. Data communicators must help readers understand and recognize the people behind the data.

2. Use personal stories to help readers and users better connect with the material. Pairing data-driven charts with personal stories centered on individual experiences can help readers understand and identify with the people represented in the research and data visualizations. Techniques that can be used in tandem with data visualizations to help lift up personal stories include photography, illustrations, pull quotes, and oral histories.

3. Use a mix of quantitative and qualitative approaches to telling a story. Most charts and graphs are built on top of spreadsheets or databases of quantitative data. However, focusing on numbers alone without any context can overlook important aspects of a story including the “why” and the “how.”

4. Create a platform for engagement. This can take the form of interactivity in which users are able to manipulate buttons, sliders, tooltips, and other elements to make selections, filter the dataset, or create customized views of a chart. Such engagement can be leveraged as a way to allow users to find themselves in the data or discover the stories that most interest them. Another form of engagement is offering audiences a means of providing feedback about a data tool or visualization.

5. Consider how your framing of an issue can create a biased emotional response. Carefully consider how the data you visualize presents a particular perspective on the content. Take the examples ProPublica journalist Lena Groeger discusses in this post on different ways to visualize the impact of crime on local communities. Maps that show the locations of where crimes occurred versus maps that show the percentage of residents in a neighborhood who were in prisons are two different ways to visualize data related to the criminal justice system. What data we choose to focus on and what we choose to ignore can bias our audiences’ perceptions of the issues about which we are communicating.

6. Recognize the needs of your audience. Taking an empathetic view of the readers’ needs as they read or perceive information is an important step to better data communication. This kind of empathy can also be couched in terms of producing visualizations that are accessible by people with vision, physical, or intellectual impairments; reducing overly technical or jargon-laden language; and translating your work into languages most used by your target audiences.

Being empathetic to the people and communities of focus does not imply sacrificing the data and methods used in responsible, in-depth, sophisticated research. In fact, the opposite is true: high-quality research and empathy for people and communities can be complementary. Effective research necessarily means understanding someone else’s point of view nonjudgmentally and recording that perspective as accurately and truthfully as possible. Empathy underlies research and data visualizations that uphold diversity, equity, and inclusion, so data communicators should seek to find ways to help their audiences understand and connect with the people that the data represent.

 Read the full Do No Harm guide here.

The post Six Ways to Bring Empathy into your Data appeared first on Nightingale.

Last July, I came across the Criterion Collection edition of Parasite, a stylish cover design inspired by the use of Morse code in the film. Spurred by this design and the blooming fan art, I started thinking about how data visualization could contribute to Parasite fandom. Furthermore, I always wanted to visualize the relationship between architecture and films, and Parasite is a perfect use case.

The social commentary

Much has been written about the importance of architecture in films. Architecture has played a starring role and it has been used to convey underlying messages. It can reflect a journey into the depths of a character’s psyche. City mock-ups in classic musicals and elaborate lighting have been used to depict a character’s mood. And, on the flip side, who can forget the labyrinth design of the Overlook Hotel in The Shining ??

Architecture has also been used to reflect the pace of our times (e.g., Chaplin’s Modern Times or Jacques Tati’s Playtime) and, like in the case of Parasite, to portray differences in social class. A few examples include William Wyler’s Dead end, Akira Kurosawa’s High and Low, and more recently, Ben Weathley’s High Rise or Bong Joon Ho’s previous film Snowpiercer.

In few films does space play such a starring role as it does in Parasite. The Kims, a basement-dwelling family, devise a meticulous plan to infiltrate the life of the wealthy Parks. The Parks’ house sets the scene for most of the plot twists and frenetic action. In fact, 66 percent of the film takes place in the impressive Park family household. Wide rooms, endless stairs, and secret passages: a maze designed for the characters to traverse. The journey of the Kims to this top-of-the-hill household embodies their pretension to escalate their social class. The way the families coexist in this house is not casual. The vertical composition, the spaces that the characters take over, and the movement among the different floors of the house transmit a powerful class inequality message.

Director Bong Joon Ho sketched a simple floor map of the house while writing the screenplay that Production Designer Lee Ha Jun later used to build massive one piece sets.

In this video from Great Big Story, Lee Ha Jun explains how, among other sources of inspiration, he was inspired to design the house from cutting tofu!

After his Oscar nomination, Lee was interviewed by Deezen to explain his creative process. This interview inspired my data analysis plan:

“The vertical structure of the house reflects the relations between the three families, with Geunse at the bottom…That’s the essence of this film.”

“I wanted to express the increasing density both in terms of space and colour as one moves from top to bottom.”

While variation in space and color density between poor and rich areas could also inform interesting data visualizations, I preferred to focus on the characters’ movement and occupation within the house. I felt that the data gathering of this approach was more straightforward and less complex than trying to capture the palette of colors* as the film goes by.

The data

After watching the film an insane number of times, I created a dataset that cataloged the characters’ occupation and movement in the film. Movement was captured depending on how characters traversed the areas in the Parks’ house: up, down, move, static, hiding (which some characters do often).

I didn’t find many datasets that followed a film’s run-time evolution. To shape this dataset, I was inspired by Jeffrey Lancaster’s amazing effort documenting modern hits like Game of Thrones or Stranger Things.

Visualizing architecture

It was clear to me that an analysis of the space called for a floor map visualization. One of the amazing things about films and shows, along with their resulting fan art, is that everyone brings their expertise to the table. Architects are no exception. Since I am not an architect, I looked for fan art floor map designs. To my surprise, my search was unsuccessful, maybe due to the recentness of the film.

I found several 3D renderings of the house, but I came across the perfect option in a DVD store: the French Parasite Blu-ray edition with a cover created by Korean designer Jisu Choi . This 3D floor map had most of the features that I needed. It depicted most of the rooms where the action takes place in the Parks’ household and its aesthetic was more suitable for my visualization against the rendered designs.

I created an SVG layout with the different rooms in the house. Each of the paths on the SVG has a force layout representing a character in a room in a scene. In the visualization, to emphasize the vertical structure of the house, I divided the different rooms into three floors.

The data story

You can see the end result here. I designed the visualization to illustrate how the building embodies a character’s class and intentions and to demonstrate how each family, depending on their class, move in certain areas of the house. For example, the wealthy Parks only come downstairs to interact with the help, while the poorer families move up and down the most — trying to scale social levels. In the visualization, you can also track the shifts in action between areas throughout the film.

More than ever before, Parasite’s portrayal of social inequalities remains relevant. This, added to a vibrant plot and a magnetic design, made it inspirational not only for traditional fan art, but also for data storytelling. While film fan art is usually associated with graphic design, there are opportunities for other disciplines like dataviz to contribute to this phenomenon. Moreover, data storytelling can be an intersection of aesthetics and film analysis.

There are many films that deserve a data visualization treatment. So, which one is next?

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