What's Missing From Your Visualization Solution? Hint: It’s Not More Features


As we watch Tableau and PowerBI pile on new capabilities, it has never been more obvious that the options for visualization tools have become a pile-up of copycat, me-too solutions. It is time for something fundamentally different:

We have plenty of visual analysis tools designed for an elite group of data analysts; we need a solution for better data communication that anyone with data can use.

Data storytelling shouldn’t be a bolt-on feature or a hollow marketing promise; it should be at the heart of how we connect people with data.

We don’t need more visualization options that distract from the heart of your message; we need fewer, better options and an emphasis on getting your audience to act on the data.

It shouldn’t be restrictively expensive to share data with everyone inside (and outside) your organization; everyone should be able to join in the data conversation.

Speaking of which, we shouldn’t draw lines between those people who are allowed to present data and those who only get to view it; everyone should get a chance to create something useful.

That’s why we build Juicebox. Let us show you the difference.

The Only Recipe For A Data Story You'll Ever Need

That’s our lead designer, James (out on the town in Nashville with one of our talented developers, Jingwei).

That’s our lead designer, James (out on the town in Nashville with one of our talented developers, Jingwei).

James knows data stories. He appreciates that a data story needs to have beautiful, intuitive visualizations and people-first descriptions. But before James worries about all that, he starts by finding the structure of a story. The structure is the narrative flow that will grab a reader’s attention and carry them through the analytics to find valuable, actionable insights.

We’ve been designing data stories, dashboards, and analytical tools for over a decade. In that time, we’ve found a lot of common patterns, regardless of the industry or function. When we teach people about how to design data stories*, we emphasize that the most critical starting point is the Juice’s (patent-pending) three-part framework.

  1. Context: What does my audience need to know or choose to make the story relevant to them?

  2. Heart: How can my audience see and explore the data to reveal insights?

  3. Action: What should my audience do with their newfound knowledge?

* Contact us at info@juiceanalytics.com to find out about our popular Data Storytelling Workshops.

This pattern mirrors a three-act play: the set-up of the situation followed by complications (“messy middle”) and finally the resolution. 
I want to show you how this plays out in a thoughtfully-designed data story. Killer Heat Interactive Tool is brough to you by the Union of Concerned Scientists and built by our friends at Graphicacy.


1. The story starts off by setting the context:


2. We immediately get to the heart of the matter: the radical increase in the number of extreme heat days.


3. Finally, the story encourages the reader to take action on what they have learned.


I love this data story for its directness. It has one key question it wants you to answer and makes that question relevant to you locally (context). The answer is displayed in a few key values (heart) -- not a complex collection of trend charts and maps. And then the data story authors ask you to take action on your knowledge.

Not every data story can achieve this level of simplicity. All data stories should follow our three-part recipe.

A Well-Aged Data Story

Like a fine wine, the New York Times’ visual storytelling tends to age well (unlike many dashboards that start out like Ripple and mature into a Thunderbird).

In a recent search through the archives, I came across this gem about the number of swings Derek Jeter, long-time Yankees shortstop, took in his career. Like Jeter, this data story is a multi-tool player, demonstrating many of the skills need to bring real impact for the reader.


Let’s take a look at a few of those skills:


(1) Make data relatable. Right off the bat (sorry), the authors give you a frame of references for how many swings he’s made. As you watch an animation of him swinging, you’re informed that you’d need to watch “nonstop for more than 4 days” to see all his swings.


(2) Teach once, use often. This is another design guideline that I’ve advocated for. As you scroll through this data story, the authors introduce a data visualization model that shows tiny Jeters within a boxed in area. As you zoom further and further out, the Jeters get smaller and smaller, making clear the growing number of swings they represent.


(3) Emphasize take-aways. As the reader explores the gradual expansion of swings, the data story authors are explicit and concise in what they want you to learn from this exercise. Each step gives us another insight — shown as a callout on the side — into the massive volume of swings. The story concludes with a quote from Jeter that emphasizes perhaps the key message from the analysis: he’s been at this for a long time.

These are three of many layers of quality that the New York Times design staff brings to their data storytelling; careful use of color, fonts, layout, and responsive design are a few more. The lesson for me: If you want to refine your taste, drink the good stuff.

Make Your Data Relatable

I’m a big admirer of Nancy Duarte and her new book DataStory. One thing that Nancy knows is that data communication isn’t just about data visualization — any more than a movie is just about moving images. Audiences are moved by a movie because of so much more: storyline, characters, conflict, context, mood, specificity, meaning, and relatability.

She makes this point in a recent HBR article

The more data we collect, the more mind-boggling these figures become. Though an audience may intellectually understand the measurement, they might fail to relate or connect with it emotionally. For numbers to inspire action, they have to do more than make sense — they have to make meaning.

Connecting to people requires connecting to things they can relate to. When we’re learning a word in a new language, pointing at the picture of a biblioteca can be the easiest way to make the connection. So too in teaching the language of data.

Let’s check out some examples of ways to relate a data value to something your audience already understands.

In a simple example, we might modify as statement like “sales of hydration bottles is expected to reach $10.3 billion by 2023…” by appending a comparison “…that’s more than twice the size of the current fiction book market.” Now, your audience can relate — we’re spending twice as much on water bottles as we are on summer reading.

2. Duarte shares this excellent example from Neil deGrasse Tyson:

A site called TheTrueSize shows us how the United States, India, and China can all fit within the geography of Africa.


Choosing relatable units can make all the difference. Thus, “a banana for scale.” The New York Times expresses the amount of water used to grow produce using a) gallons of water; b) a typical serving size.


In an article by ‘Wait But Why’ From 1 to 1,000,000 they show how to use unit charts to provide an intuitive sense of scale when the raw numbers alone might fail to convey meaning.


Send me your examples and I’ll add them in.

Teach Once, Use Often

“Can’t we sprinkle in some pie charts just to change things up?”

If you’re in the information design business, you’ve probably heard some version on this request. Variety is the spice of life, right?

I say: Get your spice elsewhere. Your audience doesn’t have interest in learning a bunch of different charts — and carrying an unnecessary cognitive load. Instead, teach once, use often.

Here’s a great example from a Pudding data story that explores “How many high school stars make it in the NBA?”. The following visualization is a little complex, non-standard, and will take readers a moment to grasp. Each dot represents a top 100 high school player and the progress they made on their way to stardom in the NBA.

Why ask your readers to learn this new Plinko-style visualization?

Because once they understand it, the data story goes on to use the same structure many times. Different segments of players (e.g. top 10 high school players, straight-to-NBA, players from specific colleges) are shown in this same model, each time exposing a new facet of insight into the data. By the second or third iteration, I’d expect most readers understand the visual vocabulary and are focused on what they can learn from the data. And that’s the point of data visualization — not to create a spicy grab-bag of charts.

Toward a Data Personality Framework

With all the talk about Data Literacy (led by folks like @Ben Jones, @Jordan Morrow, @Valerie Logan) and/or Data Fluency (👀@Dalton Ruer), the time is right for a more rigorous methodology for understanding the audiences for data dialogue.

Which got me thinking: What if there was a Myers Briggs-style personality type indicator for data personalities? It could be used to predict how someone is going to respond to data when it is presented, and by extension, what are the best ways to get the desired outcome.

I’d like to share a framework for profiling Data Personalities. Like Myers Briggs, it has four dimensions on which an individual can exists on a spectrum between two extremes.


Here’s how I think about each dimension:

  1. Decision-making approach. How does this person integrate data into their decision-making process? Do they lean on data to guide their thinking or are they more likely to depend on their experience and instinct?

  2. Types of decisions. Is this person in a role where they make decisions that have a long-term and strategic perspective, balancing more uncertainty and more sources of information? Alternatively, are most of this person’s decisions more real-time, tactical, or operational in nature?

  3. Experience with data. Does this person have a high level of data literacy — comfort analyzing, communicating, and interpreting data? Or are you dealing with someone who is relatively novice in working with data and who may express discomfort with data?

  4. Role in the organization. Is this person in a role where they can make decision directly from the data they are presented? Alternatively, will this person take your data and use it influence other people?

As a data author intent on encouraging smarter decisions, there is nothing more important than understanding your audience. The Data Personality Profile (DPP) is a good place to start. Now we just need some data.

Explaining Data Teams to HR

The importance of a good team to build data solutions can’t be underemphasized. If you’ve read anything like Francois Ajenstat’s recent Forbes article or Roger Pen’s e-book on building data science effective teams you get many of the key points; however I would argue that in addition to these points you need to invest time with your Human Resources (HR) team and make them an integral part of the success. Developing their data literacy should be part of your objective to building a successful team.

The following isn’t a prescription for a single conversation, presentation or analysis for your HR team, but a way to develop their data literacy around what constitutes a great data team.

Skill Diversity

Your HR team will focus on inclusion and diversity, but may not understand diverse skills and experiences and how they contribute to creating great dashboards, models, etc. At Juice we’ve found on numerous occaisions that Zach’s experience in digital marketing has opened new insights or ways of designing valuable healthcare analytics solutions. It could be just asking a different question or offering up a solution to a similar problem in a different industry (btw, funnel visualizations in healthcare are amazing). If everyone on the team is from the same industry or has similar experiences how do we get the HR team view this as a concern or red flag? How do we convince them to find someone with complementary skills and background?

The right way to think about skills is less about an individual’s skills, but about the team’s overall skill set. Your series of HR conversations should be an understanding of what the team is good at, where are their blindspots and what skills are needed. Also, HR needs to understand how to ask questions like, “Describe to me some of what you’ve built in Python and how users were impacted” vs. “How many years of Python experience do you have?”


One of my favorite quotes I’ve read recently on building data teams is “Hire people, not experience.” It comes from this piece on Medium by Murilo Nigris. How do we get to know people? Rather than ask them to talk about the tools they’ve worked with convince them to tell you story. Its in those stories that you’ll understand their values and priorities.

For the HR team to be able to assess fit you need to decide what your team’s value are. Do you value speed, creativity, production quality code or collaboration? All of the above is not a valid answer. Give your HR team 3 to 5 values to screen for. Take the time to explain why these values matter. Include examples of how someone on the team currently exhibits these values and how they makes you successful.

Defining and describing values will sound like a lot of work; however it will be a fraction of the effort of having to let someone go because they weren’t a fit.

Data-Driven Job Descriptions

Many of the job descriptions I read for data positions are painful to read. The biggest miss in my mind is I never really know what this person will be doing exactly on Day 1 or Day 500. Your job descriptions should read more like these on the Salesloft website.


  • Build a prototype application that will be posted on our website.

  • Completely data visualization online class to bring your data literacy vocabulary in line with the team.


  • Conduct product feedback interviews to gather feedback on existing features, and speak to new features coming.

  • Successfully lead a scrum team by running planning meetings daily.

A nice benefit is that a job description written like this becomes the individual’s performance plans and goals if they are hired. Here’s a template from the Google offers a way to think about job descriptions as another example. https://hire.google.com/job-description-template/

Skills Assessment

Most new candidates have to go through some technical assessment. Make sure your HR team is involved with the assessment. Don’t let them punt involvement in the skill assessment because its “too technical”. If you can’t explain the skill assessment or if they don’t understand its desired goals then you have a problem. Use the opportunity to explain the assessment as one way to develop their data literacy. They can also see if you have any blindspots in the assessment and to make sure there isn’t bias in your assessment.

Also, make sure that they know skills assessment changes as new technologies are adopted and implemented, so it's never a static test.

Recruiting Talent

Often you are sharing the HR team with other departments. As a result, the amount of time that HR will actively recruit new candidates is limited. In my experience the HR team will send you 3 to 5 candidates or resumes and if you elect not to choose any then you’re completely dependent on whoever finds your website.

When discussing recruiting efforts with your HR team ask the following questions:

  1. What is your time commitment on this opening?

  2. What kinds of efforts will we make to find candidates that are probably already employed?

  3. What can our team do to supplement your efforts? (What are we allowed to do?)

  4. Are there any monetary incentives for us to find our own candidates?

  5. Are OPT candidates a viable option? Do they understand OPT?


After your disappointment diminishes, here are some items you can take to supplement their efforts:

  1. Have your team share the job posting link with their social networks

  2. Volunteer to present at local meetups, events, universities and conferences. Try to do at least 2 per position.

This will seem like a lot of work, but building models, visualizations and data solutions without a full team is time consuming too. Note that the lessons above are very applicable to bringing on contractors or consultants to your data team as well.

The initial HR conversations will be hard, but keep the dialogue going even when you don’t have openings.

To learn more about data culture and teams make sure to get your copy of Data Fluency, Empowering Your Organization with Effective Data Communication. If your timeline for your customer facing data project doesn’t include time to get HR on board and fluent, reach out to us to learn how the Juicebox platform can handle some of the challenges with getting the right data team in place.

The 2020 Twitter Election: Explore the 20+ Democratic Candidates

Our goal at Juice is to give everyone the ability to design and share compelling data stories. We're always inspired by the The New York Times information design group (and many other data story authors). We want to bring this kind of data communication to every organization.

However, we sometimes forget to share publicly all the cool stuff we can do. We’re going to fix that, starting with this data story, an exploration of democratic presidential candidates and their influence on Twitter. It was crafted as a passion project by our very own Susan Brake.

She set out to answer a few key questions:

How do the candidates compare in the reach of their Twitter audience?

Who has Twitter momentum?

What are the candidates saying on Twitter that is drawing the most attention?

Give it a try. I expect you’ll learn something and enjoy the journey.

If you like it, keep in mind that we work with organizations of all types — start-ups, non-profits, large enterprises, and the public sector — to help them tell the stories in their data.

2019 Data Summer Reading List

“Deep summer is when laziness finds respectability.”

Sam Keen

Now that Summer is here it’s a great time to recharge our batteries. Whether it’s a much needed vacation, a nap in the hammock, hours watching soccer games or curling up with a good book. Here are the books that made it onto the Juice Summer reading list this year. We’ve started some of them, but plan to get through the entire list by Labor Day.

Screen Shot 2019-06-16 at 09.08.44.png

After hearing Alberto speak recently in Atlanta on his book tour we added it to our list. We’re sure it will make it to the Juice reference library along with his other books.

Screen Shot 2019-06-16 at 09.08.11.png

This book was on Bill Gates Summer reading list last year and we’re finally getting around to reading it. Each chapter tells a great story about how to think about data in the context of real life.

Screen Shot 2019-06-16 at 09.56.49.png

This book has gotten a lot of interest in the data visualization community, so hard to ignore it and not make it a focal part of our Summer.

Screen Shot 2019-06-16 at 09.57.45.png

As Juicebox supports data storytelling at scale, we love to read anything we can get our hands on about stories. This one came highly recommended to us.

Screen Shot 2019-06-16 at 09.58.17.png

We’re always up for some clever humor. This book fits the bill and just skimming us made us laugh.


This book is a beautiful compilation of maps and hard to put down. Very enjoyable to skim and appreciate the illustrations on a rainy day or Summer afternoon.

Your Data Story Needs More Than Data

Data stories use the techniques of traditional storytelling — narrative flow, context, structure — to guide audiences through data and offer flexibility to find insights relevant to them. Data may be the star, but your data story won’t cohere without a mix of additional ingredients.

There are at least four things that you’ll want to incorporate into your data story that go beyond the data visualizations:

1. Context

The first step in a data story is to set the stage. You want to explain to your readers why they should care about what you’re going to tell them? This is also an opportunity to let your reader customize the data they are seeing to make it more relevant to them. A couple of good examples:

2. Educate your readers

Before plunging your audience into a complex or innovative visualization, you want to take some time and space to explain how that visualization works. Tooltips and gradual animation can help the user absorb how to read to the visualization. Try these examples out:

3. Explanation of insights, notes, help

Data stories shouldn’t create more questions than they answer. In some cases, you may want to be explicit about what meaning a reader should take from a visualization.

4. Actions and recommendations

A data story should lead to action. Make some space to explain what recommended actions your readers might take based on the results.