A Hierarchy of Needs to Live Your Best #DataLife

Maslow’s Hierarchy of Human Needs provides a useful framework for understanding the drivers of behaviors. He recognized that basic needs must be met before higher level functions can happen. He also identified that once a need is met, it becomes an expectation.

Plateresca / Getty Images

Can we apply a similar framework to our life with data?

I’m far from the first person to ask this question. However, the data-oriented “Hierarchies of Need” tend to focus on the needs and capabilities of the organization. Here are a couple of examples:

https://www.kdnuggets.com/2015/08/data-hierarchy-needs.html

https://medium.com/@hugh_data_science/the-pyramid-of-data-needs-and-why-it-matters-for-your-career-b0f695c13f11

As is so common in the analytics space, the focus is on technology and capabilities. If you are the CDO or CTO, this may provide a useful roadmap. It is less so if you are a data analyst, operations manager, or student learning how to work with data.

We’ve always been more interested in the human side of data. What do people need? What are the pains and concerns when it comes to using data? That’s why we are hell-bent on making the most human-friendly creative tool for expressing with data.

This is where my “DataLife Hierarchy of Needs” fits in — it highlights the ascending needs for an individual to make use of data. We’ve talked to hundreds of people who are using data in their jobs. Similar themes come up again and again, and this structure helps explain where people get stuck.

A quick tour of the levels:

Level 1: Physiological needs, i.e. get the data

Nothing happens if you can’t get your hands on the data. And once you have the data, you need to make sure it is accurate, understood, cleaned, and structured for analysis. Like the basic needs for food and shelter, this is the place where the under-served and under-resourced people run into a barrier. The challenge of not knowing how to define data requirements is sometimes enough halt any movement up the pyramid of needs.

Level 2: Safety needs, i.e. the confidence to work with data

Long before anyone can express themselves with data, they need to develop foundational data analysis skills. These skills include: combining data sources, being able to define important metrics, and choosing the right charts to explore and express data.

Without this sense of confidence, they won’t feel safe moving forward into finding and sharing insights in a social environment.

Level 3: Love and belonging needs, i.e. engaging with the broader organization

The work of data exists in a social environment. The next step up the pyramid is when we understand how our use of data starts to impact the people around us. This is where we expand outside ourselves to start to consider the audience, the priorities of the organization, and how to best visualize data for understanding.

Social and communication skills become increasingly important as we move to higher level needs. It was 15 years ago (!) that I wrote that the problem of analytics “isn’t a technical problem, it’s a social problem.”

Level 4: Esteem, i.e. pursuing recognition for the data insights

At last, it is time to become a data author, a creator of data products. With the skills and data access, you can look for ways to express your insights through data stories, design repeatable reports, and manage who gets access to the data and how they receive it. The social capabilities from the previous level gives you direction to know what you should create from data.

Level 5: Actualization, i.e. achieving action from the data

Ultimately, the goal of using data is to guide informed actions. If your insights are effectively communicated, you have the opportunity to change minds. But you will face resistance from people who have pre-existing assumptions or incentives to push back on the results you share. Perhaps this last level is the hardest of all because it takes subtly and skill to influence others to change how they view their world.

Download a PDF version of the Data Life Hierarchy of Needs.

The Power of Story: 6 Lessons from a Master Storyteller

I had the pleasure of attending a presentation by Rick Rekedal at Belmont University. The former Chief Creative of the Global Franchise Group at DreamWorks Animation spoke about ‘The Power of Story’ using examples from his work on the How To Train Your Dragon movie franchise. His lessons are as relevant for a data storyteller as they are for an aspiring screenwriter.

Here 6 storytelling lessons from Rekedal — and the implications for your next data story:

1. Story starts with your empathy for your audience. People want to love how they feel after hearing the your story. On the other side of that coin: your audience has fears and a story can help release them from those fears. Action: Determine what your audience wants to feel from your data story. Are they looking for confidence in in a decision they are making? The thrill of finding a new opportunity? Analytical support for a problem they believe is important?

2. We are living in a “story economy.” We have a growing array of platforms for sharing stories, and people want to feel purpose and belonging. Therefore, your story needs to stand out in a crowded, noisy field. The best way to stand out is by knowing the heart of your audience. Action: Analyze what stories the audience already has heard. Determine how your data story is going to fit in, or stand out.

3. Stories are about enabling change. Stories have the power to transform how people feel, see themselves, and act. They can also reinforce and build on beliefs that people already hold. Action: Before you start authoring your story, have a clear definition of what change you want to bring about in your audience.

4. Preparation is the majority of the work. Two-thirds of the effort of storytelling should be put into understanding your audience, their needs, and the landscape of stories. Only then does the crafting of your story begin. Action: In the data world, deadlines can pressure us to race to the finish line. Step back. Do you know enough about where you are going before you dive into the analysis?

5. Stories bring clarity to difficult concepts. The best stories help give audience a language about something they struggle to express. The process of editing your story is crucial to ensure clarity and precision of ideas and language. Action: Edit your data story down to the core concepts — and articulate them with care. It is ok to cut some interesting information if it bring focus to the most important concepts.

6. Find your central theme. Of all the elements of a story that Rekedal enumerated (plot, character, thought, speech, melody, decor, spectacle), the “thought” — i.e. the central theme and message — is most important. This is about finding the heart of the story. Themes should be important and reach deep into the psyche of the audience. His examples included: “I can accept myself in my own skin”; “Unconditional friends let me share my true colors.” Action: Each data story should have one theme. Don’t try to do too much. Make more stories.

A Better Pie Chart

Pie charts are the Kenny G of visualizations: Massively popular in the face of withering criticism.

I used to spend a fair amount of time criticizing pie charts. And I wasn’t alone — everyone who cares about visualizations has spent time shaking their fists at ineffectual pie charts (take this article for example: “The Worst Chart in the World”).

The criticism stems from a basic limitation of the form: radial degrees (e.g. a pie slice) are harder to perceive than linear distance (e.g. the length of a bar).

However, pie charts have an enduring popularity. It stems from a fundamental appeal of the form. Circles are familiar. And it is conceptually simple to understand that the chart is showing parts of a whole.

But you shouldn’t put on Kenny G at a dance party, and that’s where pies charts get in trouble. They aren’t fundamentally bad as much as they are are mis-used, over-used, and poorly executed. The problems of pie charts fall into six areas:

1. It is hard to visually evaluate size based on radial degrees.

How do you make clear the relative values of the segments?

2. Pies can only be sliced so thin before they are unreadable.

How do you handle showing many items?

3. Labeling individual segments can be difficult.

How do you ensure the readability of labels and values?

4. Static pie charts provide little opportunity to answer a next level of questions.

How do you make the information shown in a pie chart more explorable?

5. Pie charts can become colorful to the point that readers feel like they need to file charges for assault-by-rainbow.

How do you ensure color contrast with appropriate restraint?

6. Pie charts can struggle to show all the information in the available space.

How do you fit in the important content without it feeling compressed?

To boil it down, a Better Pie Chart accomplishes the following:

• READABILITY even with many data elements

• COMPARABILITY for seeing differences in segment values

• INTERACT-ABILITY to enable data exploration

• SCAN-ABILITY using color, contrast, and layout

At Juice, we believe that pie charts don’t have to suck — with some clever design choices. I’m delighted to show you how our brilliant design and development team created a Better Pie Chart.

Here’s what it looks like:

Try it out for yourself in this app where we show how The Great British Bake-Off recipes break out by creator and type. When you do, you’ll notice some clever features:

1. If there are going to be a lot of segments in your pie, we automatically create an ‘Other’ category.

2. You can select one or many segments in the pie and see how the values aggregate with the display in the middle of the chart.

3. The legend and segments interact to make it easy to select items.

4. As with all our visualizations, the selections act as filters. It is easy and automatic to have one pie chart filter results in the next pie chart.

Check out this short video showing how quick it is to build your own:

Heck, if pie charts had started out like this, we could have saved ourselves a decade of complaining.

Why Great Customer Reporting Is Worth It

🥦🥦🥦

Customer or client reporting is the healthy eating of business. It is often the last thing organizations want to do because it is hard to do well and reveals hard truths.

But like eating well, it is among the most important, valuable things you can do to create alignment internally and externally. So why don’t more organizations make the effort to eat their greens?

Bad reporting is everywhere.

Reporting is the last stage of communicating your data. You need to know what’s important and what isn’t. You need to know your audience, and the message you want to convey.

Unfortunately, bad reporting is so common it is a cliche.

It feels like drudgery — a painful but required last step that is the barrier between you and the weekend. We’ve puzzled for a long time (Reporting: The Most Boring, Important Thing in Analytics) about why reporting always seems like the last thing people want to do.

The reporting interface on many SaaS platforms is the least developed, least engaging part of the tool. Take the examples from Hubspot and Intercom below. There is no way those innovative technology companies have prioritized these experiences. It feels like these enterprises are treating reporting like a 7th-grader dashing off their math homework so they can get back on the Xbox.

Hubspot Reports are literally the very last thing (last drop down, last item) you can find in the Hubspot UI.

Intercom is no better. Their reporting feels like a forgotten corner in the attic where old boxes are piled up.

And it’s certainly not just SaaS platforms. Financial reporting. Scientific research reports. Survey reports. Success with reporting seems to be measured by checking the box over any sense of quality impact.

In fact, the quality of reporting is so low that we are surprised and delighted when the occasional high-quality report shows up. A couple of the rare gems:

The Australian Tax Office sends a report to tax payers that shows how their money is being spend. The report is clear and direct, answering the basic question: Where did my tax dollar go?

Spotify’s Wrapped is an annual report sent to listeners to summarized their music habits. It is a delightful journey through your own data

We’ve always reasoned that report is last because of the flow of information — first you need to collect data, then you can present it. Collecting and managing data is hard and will take up many cycles of development — often leaving little time and energy to “bridge the last mile.”

It is not simply a capacity problem.

The true challenges of creating good reporting

Like any problem, it is the social and human challenges, that underlie why reporting is seldom well solved. Reporting asks you to do three things that are very hard:

1. Know what is important in the data. which requires you to express to your customers the value you are delivering. You need to have clarity about your value proposition.

2. Communicate it well. Data storytelling requires a bunch of skills.

3. Be open to the pushback from transparency. When customers start to engage with your reporting, they will inevitably start asking tough questions. It can be easier to obscure data than provide the transparency we see with the Australian Tax Office.

The unexpected value of good reporting

Time and again, when I work with companies, they realize that designing a dashboard, data story, or report isn’t really about the data. It is a mirror on your organization. One of those make-up mirrors that shows all your pores. Fortunately there are real benefits when you get your reporting right:

• You will open up a new kind of open dialogue with clients/customers

• You will differentiate your product or service from competitors. Their reporting sucks.

• You will open the door to more executive conversations. Good reporting shows the value you create.

• You will understand the drivers of your business better than ever.

Reporting requires managing a careful balance of conveying a message about your value with the transparency and hard-reality of data.

It is the healthy thing to do. 🥦🥦🥦

How to Apply Data Storytelling to Dashboards

Is a dashboard a data story?

It is a question we get all the time. But I don’t think it is the right question. A better question is:

How can I apply concepts of data storytelling to dashboard design?

Dashboards are a form of data communication. Data storytelling is a collection of skills and concepts that help improve data communication. They are apples and oranges.

I want to share a few fundamental data storytelling skills that will make your dashboards more effective. Before that, it is worth understanding the purpose of a dashboard.

Like a snowflake, every dashboard is unique. Yet they share a set of common goals:

1. Provide visibility into data

2. Deliver a consistent, shared view of that data for a group of people

3. Present a overview of key metrics that are important to that group of people

4. Offer timely tracking of performance on those key metrics

5. Most fundamentally, a dashboard is an expression of priorities. The choices of metrics and data to show reflects what the organization wants to focus on.

To be effective, a dashboard needs to communicate well. It needs to be clear, concise, engaging, and organized. All that meaning — the metrics, trends, business logic — needs to be conveyed in a way that makes it easily readable for the audience.

That’s where data storytelling comes in. Think of it like this: A bad dashboard is like a collage of data points (like the Spiderman image below), leaving the reader to search for meaning (also like Spiderman, ironically). A good dashboard, in contrast, should be be a guide for understanding — more like a comic strip.

How do you achieve a flow and logic that tells a story, even if that story is constantly changing?

Here are three common data storytelling skills that will make your dashboard better. If you are looking for more data storytelling design principles, check out our 12 Rules for Data Storytelling.

(1) Story structure

The three-act story structure is something that we inherently recognize (and expect) as an audience. By using these concepts in data stories, you can tap into the deeply-rooted expectations and needs of you audience. Below you can see how to map the three acts to the presentation of data. In a dashboard, it is critical to define the scope and purpose (why should someone care about your dashboard?) and help them understand what actions can be taken based on what they are seeing. Learn more from our Story Structure Lesson.

(2) Narrative

Dashboards shouldn’t only be able presenting data. You also need to act as a guide. There are four ways that you should think about providing narrative guidance:

1. Set the Context. Explain what the data story is about and why your audience should care.

2. Describe the Charts. What measures and dimensions are being shown? How should the chart be interpreted?

3. Guide the Flow. What should the reader look at next? What should they do to proceed in the story?

4. Highlight Insights and Actions. What's most valuable to take away from the data story? What should the reader do about it?

Our Narrating Data Stories Lesson.

(3) Metrics as characters

Your choice of metrics is the most important decision you will make in designing the dashboard. These metrics need to reflect the priorities of your organization and be easy for people to understand. A few things to consider:

1. “You can't manage what you can't measure.” -- Peter Drucker. The reverse is also true. What you measure is what you will manage. The metrics you choose to include in your data story will influence priorities and goals.

2. Like characters in a story, too many metrics will overwhelm your audience. There is persistent pressure to include more ways to measure performance -- but this often reflects a lack of understanding of what matters most.

3. Metrics can either serve as the thing you want to see succeed (hero metrics) or something you want to disappear (villain metrics). Understanding this will help determine how the metric is presented.

Learn more in our Metrics: Your Story Characters lesson.

Data Storytelling: The Ultimate Collection of Resources (Updated for 2022)

From Edward Tufte's Visual Explanations, a diagram based on Salman Rushdie‘s description of the Indian epid Kathasaritsagara or Ocean of the Streams of Story.

“Gartner projects that by 2025, data stories will be the most widespread way of consuming analytics” — Data Trends

Data Storytelling is the evolution beyond data visualizations. It is a recognition that well-designed charts aren’t enough to move people to action. We need to use the tools and techniques of narrative stories to engage audiences with data. While the term has been around for a while, we are still early in the discussion about what Data Storytelling means and how it should be practiced. It is fair to wonder:

• Is data storytelling more than a catchy phrase?

• Where does data storytelling fit into the broader landscape of data exploration, visualization, and presentation?

• How can the traditional tools of storytelling improve how we communicate with data?

• Is it more about story-telling or story-finding?

Many of the bright minds in the data visualization field have started to tackle these questions -- and it is something that we've been exploring at Juice in our work. Below you'll find a collection of some of the best (1) blog posts and articles, (2) presentations, (3) books and other resources; (4) Podcasts; and (5) research papers on this topic.

And while you’re here, you should sign up to try our unique data storytelling platform for building your own data stories.

1. Posts and Articles about Data Storytelling

The Next Chapter in Analytics: Data Storytelling by Beth Stackpole. “As with any good story, a data tale needs a beginning, a middle, an end, and some actionable insights. Data scientists aren’t always up to the job.”

A Data Scientist's Real Job: Storytelling by Jeff Bladt and Bob Filbin "In short, we're tasked with transforming data into directives. Good analysis parses numerical outputs into an understanding of the organization. We "humanize" the data by turning raw numbers into a story about our performance."

Coffee & Empathy: Why data without a soul is meaningless by Om Malik "The idea of combining data, emotion and empathy as part of a narrative is something every company — old, new, young and mature — has to internalize. If they don’t, they will find themselves on the wrong side of history."

Look ma, no story! by Moritz Stefaner "Tools have no stories to them. Tools can reveal stories, help us tell stories, but they are neither the story itself nor the storyteller. Portraits have no story to them either. Like a photo portrait of a person, a visualization portrait of a data set can allow you to capture many facets of a bigger whole, but there is not a single story there, either."

Discussion: Storytelling and success stories by Andy Kirk "I just wanted to share my view on the distinction I personally make between the two main types of visualisation function: exploratory and explanatory"

The secret to storytelling is in the editing by Garr Reynolds "Although it is a film about the role of editing in filmmaking, the lessons and principles are applicable to other creative work such as writing, and storytelling of all kinds, including presentations."

Visualising data: can you see stories? by Chris Twigg "Narrative can on the one hand be broken down into a set of universal laws and principles that may transcend mediums. Stories have temporality in common (they deal with time) as well as causation (they deal with cause and effect of something). On the other hand there are the more media specific narrative affordances as for example in the way that film, opera, novel and data visualisation – because of their physicality and the dimensions open to them – would be able to give a different ‘staging’ of a story."

Data Visualization as Storytelling: A Stretched Analogy by Zach Gemignani "For practitioners of the craft, connecting our work to stories feels satisfying — it is a call to raise our standards and an opportunity to enhance the influence of our field. Stories evoke images of rapt audiences, dramatic arcs, and unexpected plot twists. Unfortunately this analogy is a stretch."

Why good storytelling helps you design great products by Braden Kowitz "It’s not uncommon for designers to confuse a beautiful looking product with one that works beautifully. A great technique for creating smarter, better products is to approach them using story-centered design."

More Story References and Resources - A list of resources inside a list of resources -- so meta. Jon Schwabish details the materials he used while writing his series on data storytelling.

So What? By Cole Nussbaumer Knaflic “Everyone wants to "tell a story with data." But very often, when we use this phrase, we don't really mean story. We mean what I mentioned above—the point, the key takeaway, the so what?"

Storytelling with Data Visualization: Context is King by Nick Diakopoulos “To fully breathe life back into your data, you need to crack your knuckles and add a dose of written explanation to your visualizations as well. Text provides that vital bit of context layered over the data that helps the audience come to a valid interpretation of what it really means."

Data Storytelling: Separating Fiction from Facts by Brent Dykes “As various people step forward to provide opinions on how to tell data stories, I’ve seen misinformation creep in which—if left unaddressed—could lead aspiring data storytellers astray."

The Role of Data in Data Storytelling by Teradata “An (alarmingly) large number of comments and opinions describe in great lengths how people in technical professions are unable to explain or storytell their experiments and findings. Have we regressed that far that something as natural as stories has disappeared from our skillset? Not really."

Will You Present the Data As-Is, or Tell a Story? By Ann K. Emery “It’s not that one visualization style is better or worse than the other. They’re apples and oranges. I want you to figure out when your viewers are expecting to see each style and then learn how to switch back and forth."

Implied Stories (and Data Vis) by Lynn Cherny “Even very simple stories, whatever the discourse form, rely on the reader filling in a lot of invisible holes. Some of the interpretation we do is so 'obvious' that only sociologists or cognitive scientists can make explicit the jumps we don't notice we're wired to make."

30 Days to Data Storytelling by Juice Analytics

2. Presentations about Data Storytelling

Telling Stories with Data in 3 Steps from Harvard Business Review

Making data mean more through storytelling by Ben Wellington

Storytelling with Data with Cole Nussbaumer Knaflic

Develop Your Data-Driven Storytelling Chops with Four Simple Principles by Soleil Kelley

How to Tell Stories with Data (Really) by Edward Segal PDF version

Visualising Workflow: Findings Stories and Telling Stories by Andy Kirk

Storytelling with data visualization: Questions and challenges by Albert Cairo

Storytelling with Data by Jonathan Corum

The Secret Structure of Great Talks (TED Talk) by Nancy Duarte

Reaching Beyond Data Visualization by Zach Gemignani

Storytelling with data visualization: Questions and challenges by Alberto Cairo

Data stories - how to combine the power of storytelling with effective data visualization by Miriam Gilbert

Making data mean more through storytelling by Ben Wellington

The Age of Insight: Telling Stories with Data from Google News Lab

The science of storytelling by Prasad Setty

Storytelling and data Why? How? When? By Robert Kosara

Data Visualization and Storytelling by Alberto Cairo

3. Books and Other Resources about Data Storytelling

DataStory: Explain Data and Inspire Action Through Story by Nancy Duarte

Illuminate: Ignite Change Through Speeches, Stories, Ceremonies, and Symbols by Nancy Duarte & Patti Sanchez

Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

The Complete Collection of Interactive Data Storytelling Lessons by Juice Analytics

Hans Rosling's TED Talks "What sets Rosling apart isn't just his apt observations of broad social and economic trends, but the stunning way he presents them. Guaranteed: You've never seen data presented like this. By any logic, a presentation that tracks global health and poverty trends should be, in a word: boring. But in Rosling's hands, data sings. Trends come to life. And the big picture — usually hazy at best — snaps into sharp focus."

Nightingale, The Journal of the Data Visualization Society A collection of articles written by data visualization and storytelling experts delivered by Medium.

Telling Your Data Story by Scott Taylor. The Data Whisperer's practical guide to explaining and understanding the strategic value of data management.

Data-Driven Storytelling by Riche, Hurter, Diakopoulos, and Carpendale. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.

Robert McKee, Godfather of Storytelling (Wikipedia) Rather than simply handling "mechanical" aspects of fiction technique such as plot or dialogue taken individually, McKee examines the narrative structure of a work and what makes the story compelling or not. This could work equally as well as an analysis of any other genre or form of narrative, whether in screenplay or any other form, and could also encompass nonfiction works as long as they attempt to "tell a story".

Stories Through Data Exploring storytelling in data visualization. A collection of visualizations sorted by Chris Twigg's narrative analysis framework.

13pt Information Graphics Gallery of examples from the studio of Jonathan Corum, an information designer and science graphics editor at The New York Times.

Pixar's 22 Rules of Storytelling "Give your characters opinions. Passive/malleable might seem likable to you as you write, but it’s poison to the audience."

4. Podcasts about Data Storytelling

Stats + Stories with John Bailer and Rosemary Pennington

Present Beyond Measure with Lea Pica

Visual Storytelling w/ Alberto Cairo and Robert Kosara by Data Stories (Enrico Bertini and Moritz Stefaner)

Storytelling with Data by Cole Nussbaumer Knaflic

Adam Greco’s 5 Analytics Data Storytelling Strategies by The Present Beyond Measure Show (Lea Pica)

5. Research Papers about Data Storytelling

Visualization Rhetoric: Framing Effects in Narrative Visualization by Nick Diakopoulos (Summary, Research Paper) "We carefully analyzed 51 narrative visualizations and constructed a taxonomy of rhetorical techniques we found being used. We observed rhetorical techniques being employed at four different editorial layers of a visualization: data, visual representation, annotations, and interactivity. The five main classes of rhetoric we found being used include: information access (e.g. how data is omitted or aggregated), provenance (e.g. how data sources are explained and how uncertainty is shown), mapping (e.g. the use of visual metaphor), linguistic techniques (e.g. irony or apostrophe), and procedural rhetoric (e.g. how default views anchor interpretation)."

Narrative Visualization: Telling Stories with Data by E. Segel and J. Heer (Abstract, Research Paper) "We systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration)."

Storytelling: The Next Step for Visualization by Robert Kosara and Jack Mackinlay "Presentation and communication of data have so far played a minor role in visualization research, with most work focused on exploration and analysis. We propose that presentation, in particular using elements from storytelling, is the next logical step and should be a research focus of at least equal importance as each of the other two."

What Storytelling Can Do for Information Visualization (PDF) by Nahum Gershon and Ward Page "Effective presentations using the storytelling approach require skills like those familiar to movie directors, beyond a technical expert’s knowledge of computer engineering and science. Creating a presentation is not just a matter of being literate in visual media and storytelling but depends on a frame of mind that caters to other modes of human information processing and thinking."

The Enchanted Imagination: Storytelling's Power to Entrance Listeners "While storytelling has flourished, there has not been a concomitant surge in research of the art form. One element of storytelling has remained nearly unconsidered, and it is, perhaps, the most profound and influential characteristic of storytelling: its power to entrance those who listen."

Celebrating Women in Data Visualization

March is Women’s History Month and as a company that celebrates women, we wanted to highlight some of the most influential women in the history of data visualization! So let us introduce you to some of these incredible women who have shaped the industry we all love and are committed to pushing forward.

Florence Nightingale:

Florence Nightingale is considered to be one of the first pioneers of data visualization. While she’s best known for her advancements in nursing, she also is credited with being one of the most influential early figures to not just use data, but to show it in a way that could impact and move her readers - who were ordinary people and even Queen Victoria herself. Nightingale was known for her love of statistics. And during her time working in a military hospital, she helped to prove that hygiene and cleanliness of the hospitals were directly linked to soldier deaths in combat. She used her experience in nursing and love of statistics to take data and information that were collected and turn it into charts and graphs like the one below. However, because she was a woman in the 1800s, she isn’t adequately credited for her advances of data visualizations along with the “founding fathers" we are more familiar with.

“Diagram of the Causes of Mortality in the Army in the East”, Florence Nightingale(1858)

Emma Willard:

Emma Willard is probably best known for her visually-stunning maps, and being America’s first female map maker. Her Temple of Time visualization is one that she hand shaded and details the timeline of world history. She used a flow diagram to showcase the rise and fall of empires throughout history. Willard described her reasoning for this visualization in this way, “By putting the course of time into perspective, the disconnected parts of a vast subject are united into one, and comprehended at a glance;–the poetic idea of “the vista of departed years” is made an object of sight; and when the eye is the medium, the picture will, by frequent inspection, be formed within, and forever remain, wrought into the living texture of the mind.

Photo from Boston Rare Maps

Florence Kelley:

Florence Kelley committed her life to social reform from an early age. Born into a home with parents who were abolitionists, Kelley was a scholar and studied in the US and abroad. She is best known for her work with Hull House in Chicago where she focused on the impoverished neighborhoods of immigrants who had come to the United States to start a new life. Kelley began the Hull-House Maps and Papers Project in the late 1800s. As well as the Hull House project, she also worked on the report of the smallpox epidemic in Chicago in 1893 and helped enforce the provisions of the Illinois Factory Inspection Law (she was the first female factory inspector in America). The Chicago slums maps were the most popular among her work and consisted of two types of maps based on nationality and weekly household income. Kelley sought to show the information she collected with her small team and help others understand the complexities of socioeconomic statuses in four Chicago neighborhoods.

Hull House Chicago Maps by Florence Kelley go-wage-maps/

While much of the visualization world as we know it comes from these leading ladies listed above, we want to give a nod to the current women who are making visualization history as well.

Lea Pica:

Lea Pica is known worldwide as a data presentation guru, or as she describes herself, “Let me be your Slide Sherpa. Your Viz Vizier. Your guide on the exciting road to presentation enlightenment.” Pica used her experience in musical theatre to bring a “performance” aspect to her professional career. But try as she might, she realized that even all of the bells and whistles she thought would help her successfuly grab attention, were falling flat. She became a self-taught visualization expert and now, she’s among the ‘leading ladies’ of the data visualization and presentation world!

Lea Pica eapica.com/about-lea-pica/

Amanda Cox:

Amanda Cox is an America journalist and data visualization that is well-known for her work as the data designer at the New York Times where she rose to serve as editor of The Upshot section. She worked as a graphics editor from 2005 through 2016 at the NYT. And her desk created the infamous election monitoring needle we see from the NYT every election cycle since 2016.

Cox is known as the “Michael Phelps of infographics,” a title we are quite fond of! In her opening statement of her keynote at the OpenVis Conference in 2013 she popularly said that ultimately design isn’t about typography or whitespace, but rather empathy - it’s about creating visualizations that readers can both understand and connect to emotionally. Since Cox's tenure, the Times has "led the field of innovative information graphics" and "raised the bar of journalistic interactive visualization."

She has also served as the judge for data visualization competitions, and several of her data visualizations were selected for The Best American Infographics 2014 and The Best American Infographics 2016. It’s easy to see why we would include her in this list of influential women who are cemented into the history of women in data visualization.

Giorgia Lupi:

Girogia Lupi is an Italian information designer who needs no introduction. She is a rockstar in the visualization community and has amassed tons of notoriety and awards for her work. Lupi’s work synthesizes data and storytelling in innovative ways to create unique and singular brand expressions. In her practice, she designs engaging data-driven visual narratives across print, digital and environmental media that create new insight and appreciation of people, ideas, and organizations.

Her vibrant and inspiring design work empowers leading global organizations to achieve their mission through data-driven storytelling, and reflects her belief that “data has the capacity to make us all more human - advancing our intelligence, engagement, and delight.” Her work is part of the permanent collection of the Museum of Modern Art and of the permanent collection of the Cooper Hewitt Smithsonian Design Museum.

The Easy Button for Dashboard Design

If you’re a Tableau fan, you should be following Lee Feinberg of DecisionViz. He’s an expert dashboard designer, adjunct professor at NYU, and hosts a deep-dive podcast with industry experts (and me).

He’s also the kind of pragmatic data practitioner that I really appreciate.

Lee recently shared a spot-on list of The 10 Tableau Dashboard Fixes You Need To Be An Analytics Hero.

Lee has inadvertently gotten to the crux of why we created Juicebox: It should be easy to create data communications that are audience-ready right out of the box.

More time conveying your message; less time fiddling layouts, charts, color, and labels.

Of course this isn’t a problem created by Tableau. Long before Christian Chabot, Chris Stolte, and Pat Hanrahan made Tableau the defacto “IBM” it is today, we spent our time fixing Excel dashboards and PowerPoint slides to make them readable. The source of the problem is un-thoughtful design options, a belief that more flexibility is always better, and little consideration of the end-user audience. As a result, you get more design decisions and more “opportunities” to fix them.

Lee offers 10 checks on your dashboard design using a 1-5 performance scale, and you shouldn’t share your dashboard until you’ve gotten 40 points (out of 50).

Here’s the good news! We can get you 40 points right out of the (Juice)box. Let me show you how by reviewing each of Lee’s criteria.

“1. The information on each dashboard ties to one main idea, and the audience should be able to read the dashboard in about 30 seconds.”

Instant Juicebox Score: 2

Focusing on one main idea is ultimate up to the author.

However, we make sure to give you a nudge in this direction: When creating a report or dashboard in Juicebox, we ask you to give it a name and description. This information shows up in an automatically-generated header.

“2. Each chart uses the least amount of space needed to see the data legibly and most importantly, to communicate the insight / intended message.”

Instant Juicebox Score: 3

Visualizations in Juicebox are lovingly-designed to emphasize the data with minimal distraction.

To do this, we have made our charts automatically responsive to work beautifully on mobile devices.

We also make visual space for the text descriptions and insights that will help your readers know what the data means.

“3. Remove visual elements that don’t add clarity, such as : too many digits or decimal places, gridlines, tick marks, axis labels, field and column labels. Less is more.”

Instant Juicebox Score: 5

Our design team has done the work to remove extraneous details. We’ve taken out extraneous ‘chartjunk’ to deliver data legibility.

We even make smart choices for number formats to ensure that large numbers are presented with the level of detail that will make it easy to read.

“4. No horizontal or vertical scrolling. Explore other chart types that do not scroll or enlarge the chart to minimize scrolling.”

Instant Juicebox Score: 5

Our charts automatically size to fit on screen. One of the best examples is our bar chart. When you have a long list of items to show, a small, scrollable version of the full bar chart is displayed on the left. Now you can see the shape of the values without sacrificing readability.

It takes some clever design engineering to elegantly handle data can be big or small. We’ve got it covered.

“5. Apply color to make information stand out, not to make charts pretty. Use a color only once and be consistent, i.e. blue has the same meaning on every dashboard.”

Instant Juicebox Score: 5

Color choices can be hard. What colors go together? How do I make color choices consistent?

Not in Juicebox.

We have an industry-leading approach to theming your dashboards and reports. You can instantly try out our pre-build color themes, or add your own. The colors will be applied consistently across everything you make.

“6. Rename or alias field names to be clear and simple, especially for Quick Tableau Calcs. Don’t accept the default name the database admin created.”

Instant Juicebox Score: 4

This is an important concept: you don’t want to expose your dashboard audience to the messy data field labels that come from your spreadsheet or database.

In Juicebox, we automatically rename field names, removing that junk and even adding plurals. We also make it quick and easy to update those labels throughout your dashboard.

“7. Avoid charts that look “cool,” e.g. treemaps, starbursts, Sankey, packed bubbles. They may be unfamiliar to your audience and can get in the way of seeing insights.”

Instant Juicebox Score: 5

A great point, and a lesson I learned long ago when I had fallen in love with treemaps. “If you are explaining, you are losing.”

Fewer choices is sometimes better. In Juicebox, we include the most common and useful charts — then we make it easy to connect those charts. The result: you can present complex data in an interactive way without having to resort to complex charts.

“8. Show brief ‘operating’ instructions, especially for action, highlight, parameter, and set filters. They may be unfamiliar with Tableau; to them it’s a website with charts.”

Instant Juicebox Score: 3

You may need to do most of this on your own. But you’ll get some built-in explanations when you use Juicebox.

For example, legends come standard and we automatically include instructions on how to interact with charts.

“9. When using a dimension on color or shape, make sure the dimension has at most seven elements, else the chart can be visually overwhelming and hard to interpret.”

Instant Juicebox Score: 5

We impose limits on how many parameters you can add into charts. This can seem draconian at time — but it is for everyone’s good.

Here’s the secret: when you can automatically link together different chart types, you can still do sophisticated things with data without having to show all the data once.

Simple parts, easily connected.

“10. Place filters, parameters, and legends that affect all charts next to or below the dashboard title. Else, place in the left column and/or within the related chart(s).”

Instant Juicebox Score: 5

As Lee points out: Context is everything. You can’t understand the numbers on a dashboard without explaining how the data has been filtered.

That’s why we created our “Sticky Bar”. When you navigate through a Juicebox dashboard, you’ll always be able to see how the values and charts are being filtered.

The 7 Stages of Data Projects

Why do data projects take so long? It’s exhausting — finding data, cleaning data, identifying problems in the data, creating presentations, hitting resistance...on and on.

I’ve seen the struggle up close for over 15 years. It is my belief that the challenges of analytics have less to do with technology limitations and more to do with people challenges. The barriers often relate to Psychology, Sociology, Anthropology, and Mindsets.

We will often have clients who are energized to get started, but then disappear for months as they struggle with their data problems. I see people bounce back and forth from optimism to pessimism.

With that in mind, I wanted to offer a framework for thinking about the journey that both people and organizations go through as they tackle data projects. The framework describes the sequence of behaviors and emotions that people express. Getting stuck in these stages helps to explain why data projects can take so long:

1. Skepticism. Like anything that is new, people will start by questioning whether it is worth their time and effort.

2. Irrational Exuberance. The pendulum swings and people get (over-)excited, about what they can do with data. Reality may not match their growing expectations.

3. Confusion. Then back to Earth. When it comes time to embark on an actual data project, the uncertain grows. Where do you even start?

4. Discovery of Purpose. Getting to this step requires finding a small piece of the data potential that can be bitten-off first.

5. Doubt. Now that you’re committed to a direction, the reality of your data comes into play. Will you be able to find value and insights?

6. Denial. Even after emerging from stage 5 with progress, now you face an audience that may not be ready to change. Their skepticism is now your blocker to progress.

7. Acceptance. Finally, the data project comes to fruition, perhaps at a smaller scope than was originally imagined. Time to find the next opportunity.

I made this infographic as a visual display of this framework:

Download the infographic as a PDF.

Reporting: The Most Boring, Important Thing in Analytics

Dresner Advisory Services’ report about self-service business intelligence uncovered a surprising result. Among all the technologies and initiatives that respondents consider important, the item that topped the list was reporting.

https://dresneradvisory.com/

Let’s zoom in a bit to make that easier to read. Yep, Reporting is the most important strategic initiative to businesses in their use of data. (Shout out to #12 Data Storytelling!)

Let that sink in. Among all the hot analytics initiatives to choose from (big data, IoT, NLP, data storytelling, cognitive BI, GDPR), plain old, boring reporting is what is considered the most important strategic initiative. In fact, the top of the list is all meat-and-potatoes data needs — reporting, dashboards, data integration, data warehousing (sorry, not data lakes), and data prep.

But seriously, reporting? That has to be the most boring term in all of analytics. How can you not think of "TPS Reports"?

It makes me wonder: Is reporting the dark matter of analytics? It is everywhere, holding the data universe together, yet it manages to elude our attention and affection.

If I swap in the word “reports” to this dark matter article, the parallels are eerie.

https://stfc.ukri.org/news-events-and-publications/features/the-story-of-dark-matter/10-things-you-need-to-know-about-dark-matter/

Reporting isn't eluding everyone's attention (that’s obvious from the survey). But it does seem to elude the attention of analytics vendors who want to build lakes, predict outcomes, learn deeply, and tell stories. We’re guilty of gravitating to concepts that seem to have more curb appeal.

In the midst of all that possibility, business must go on with:
• Marketing reports explaining campaign performance
• HR survey reports
• Reporting to shareholders or investors
• Sales performance reports
• Product engagement reports

• Project impact reports.

Reporting can be for internal audiences. Reporting is also common for customers or clients. It can be simple (e.g. in an email update) or in-depth (e.g. as an interactive, exploratory website).

Your report should always have a clear purpose, convey a message, and encourage action. All of our training on data storytelling applies to report design.

If you are sitting in the position of an IT leader, you are begging for reporting to be less painful. You want to reduce the backlog of reports that “need” to be produced each month. You want to reduce the back-and-forth of requirements from business users.

It might be time that we acknowledge the centrality of reporting in our universe. When we do, we’ll start to focus on making reporting better.