Data Fluency Dorks Unite

How we communicate data is broken.

There. I said it.

It may not be nice to hear, but deep down you know it's true. You can see it in the way that data gets delivered to audiences: email attachments no one wants to open, 50-page slide decks filled with never-ending complex charts, and scrolling pages of dashboards with no context around them. It's not only messy, it interrupts the ability to adequately share and communicate important information about data.

So what's the solution? How do we deliver data to audience where they can draw out conclusions and information that is going to be meaningful to them? The answer: data fluency.

Data fluency, or data literacy, is something that we at Juice have been talking about for years (we literally wrote the book on it). We recently sat down with Dalton Ruer, or as he's more familiarly known around the web, QlikDork, to discuss the details of data fluency and how to achieve it. Check out the video below to hear from Juice CEO Zach Gemignani and Global Head of Data Literacy at Qlik Jordan Morrow and learn what having data literate consumers means, how to get good at choosing visualizations and weaving them into engaging stories, what a data fluent culture looks like, and so much more.

Related reading:

• Our new book Data Fluency may not be for you
• 4 Components of the Data Fluency Framework
• Top Signs Your Audience Isn't Data Fluent

You're Invited: Data Monetization Workshop 2018

Juice is proud to announce that it will host the third annual Data Monetization Workshop on Thursday, March 29, 2018 at the Nashville Technology Council’s Tech Hill Commons. Created by local data expert Lydia Jones in 2016, the Data Monetization Workshop brings together some of the top data and analytics practitioners in the country to discuss how to deploy data monetization in a business setting.

This year’s workshop will feature speakers and panelists from companies such as BuildingFootprintUSA, Crystal Project Inc., Dawex, Digital Reasoning, and Uber. The topics covered will be: 

• The Now: What are the opportunities and business models you should consider to monetize your data? E.g. enhancing existing products, new data products, data marketplaces.

• The Future: How will emergent technologies such as IoT and AI unlock new opportunities and challenges for data monetization?

Prior to the workshop, attendees will have the option to attend a data storytelling seminar led by Juice Analytics employees. The seminar will showcase Juice’s unique method for quickly and easily creating data stories from a given data set. The workshop will conclude with an open bar networking event.

Attendees in the past have come from Florida, Texas, New York, Georgia, California, Canada, and Australia, and across industries such as healthcare, finance, retail, technology, and government. They are typically members of the C-Suite (including CEOs, CMOS, CFOs, CXOs, and CAOs) as well as data and analytics leaders, data scientists, investors, and data product development leaders, among others.

To learn more about this year's Data Monetization Workshop, visit the link below. Please be sure to register in advance as seating is limited. We hope to see you there!

What's in a Juicebox: Narrative Flow

Do you remember when Facebook first launched its News Feed? I do. I can recall complaining with friends that it was "too stalker-ish" (we were right) and that "no one was going to use it" (we were wrong).

Today it's hard to imagine Facebook without its signature News Feed, but for a long time I did not care for it at all. That's because once people get used to a certain way that things operate on the web, they don't like it when those practices are changed. Not only do they dislike change, but they come to expect certain design practices online. Take for example the horizontal navigation bar found across the tops of web pages: around 88% of websites have its main navigation panel there on every page. Nine times out of 10, that's the first place a user will look when attempting to navigate through a website.

Another piece of design experience that all users expect is vertical narrative flow. Web pages flow from top to bottom and as you want to learn more about a company, product, piece of news, etc. you scroll down.

Juicebox is not unique among BI solution to offer a design layout that flows from top to bottom. Infographics and web pages have taught us that people want to read data the way the same way that they read text. However, Juicebox has a special ability to seamlessly connect the narrative flow, dynamic textual content, and complex filters to give users an effortless experience while navigating data.

Narrative flow is essential to the experience of Juicebox and our user-centered design. As a user interacts with a Juicebox application, they are continually making decisions about what they would like to see in the data, what relationships are most important to them between segments and tables, and what details they need to make informed decisions. All of these complex interactions are done behind the scenes as Juicebox provides that infographic-type feel in a web-based format.

Can you think of a situation where you need to deliver data to an audience of users? Maybe it is customer reporting or a data model that needs a user interface for people who are not data savvy. In recent months we have made it even more easy for you to get started with Juicebox. Through our Guided Design Process you can see your data in Juicebox and give access to 10 users so that they can experience the ease of navigating through your data in a narrative flow. To learn more about Juice's Guided Design Process, check out our resources page.

You Don't Need a Slide Factory

You might be surprised to learn that one of our most popular blog posts of all time is Automated PowerPoint Generation, or Making a “Slide Factory.” Even though this post was published almost nine years ago, month after month we continue to see it rise to the top of our most visited pages. 

Whenever someone reaches out to us asking if we have a ‘Slide Factory’ solution, we tell them two things:

1. Sorry, we do not.

2. You don’t actually need a slide factory.

In fact, the need for automated presentation delivery is the genesis of our data storytelling solution, Juicebox. We are intimately familiar with the need to deliver data to customers, co-workers, stakeholders, etc., in a consistent, structured manner that communicates a message while providing each person with the data that is most relevant to him or her. Instead of attaching a 50-slide PowerPoint deck, Juicebox does that same job with an interactive reporting application. Users benefit from a guided analytical story, ability to capture insights, and features to collaborate with others.

Not only do your users benefit, but you no longer have to deal with report production and ad hoc headaches! Juicebox was designed with the data consumer in mind, meaning that the need to spoon feed your audience data and information through long-drawn-out PowerPoint slide decks is no more. 

In recent months, we have made it more affordable and simpler than ever to get started with Juicebox. Through our Guided Design Process, customers are seeing what their data looks like in a Juicebox application within days not months. We give you four weeks and ten user accounts to test Juicebox with your data, you have plenty of time to get user feedback and build a business case for using Juicebox. Pricing starts at only $6 per user (with a 50 user minimum). With tiered discounts for more than 500 users, Juicebox is a competitive option for any budget!

If you would like to test drive your data in Juicebox, fill out our Get Started form and we will be in touch ASAP.

Check out some of our Juicebox apps in action:

• Nashville Arts Grant

• Virginia Economic Development

Gift Ideas for Data and Visualization Lovers: 2017

It's that time of year again. Thanksgiving is over, and the mad dash to find the perfect gift for everyone on your holiday shopping list is on. If you're anything like us, you've got a number of data visualization enthusiasts on that list that you just know are going to be particularly difficult to buy for. Thankfully, we're back with our annual gift guide created specifically for people who love data and visualization. Read on to find out exactly what to buy for your data-loving friends and family.*

Books

Just like last year, we're kicking this gift guide off with a selection of books that we think any data lover would enjoy. While there are so many excellent books on data visualization to choose from, these are a few of our favorites that were released (or re-released) this past year, with a few old classics thrown in as well. 

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios by Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave

Semiology of Graphics: Diagrams, Networks, Maps by Jacques Bertin

Visual Journalism: Infographics from the World's Best Newsrooms and Designers by Javier Errea

Infographics: Designing and Visualizing Data by Wang Shaoqiang

Presenting Data Effectively: Communicating Your Findings for Maximum Impact by Stephanie Evergreen

Storytelling with Data by Cole Nussbaumer Knaflic 

Beautiful Evidence by Edward Tufte

Data Fluency by Zach and Chris Gemignani

Art

A few years back, Juice gave each of its employees a piece of sound wave art and it was a huge hit. One employee actually loved his painting so much that it now hangs permanently in Juice's Atlanta office. These pieces are not only custom and unique, they're absolutely beautiful visualizations of something that everyone loves: music.

For Kids

It’s never too early to start introducing the children in your life to the wonderful world of data, visualization, and technology. Instead of wandering through toy stores frantically searching for Fingerlings, consider instead one of the cuter, cuddlier, and less noisy distribution plushies from Etsy seller NausicaaDistribution. These visual guides to Star Wars and comic books make for great introductions for kids and teens to the wonderful world of visualization. And if you want to start them really young, check out the Code-A-Pillar from Fisher Price. It's a seriously cool toy that involves planning a path for the robotic caterpillar and getting it to follow that path using coding.

For the Data Lover Who Has Everything

What do you get for your data loving friends that already have everything on this list? How about the most customized visualization possible - one of their DNA! Give someone the ultimate information with either a 23andMe or AncestryDNA report that details his or her ancestry, food intolerances, and so much more! It will definitely be unlike any other gift they've ever received before.

These are just a few ideas for gifts for your data-loving friends. For more ideas and inspiration, check out our gift guides from previous years. And of course, have a very happy holiday season!

Related reading:

• Gift Ideas for Data and Visualization Lovers: 2016 Edition
• Gift Ideas for Data and Visualization Lovers

*Or for yourself. We don't judge here.

The Rise of Analytical Apps — Are We Seeing the Last Days of Dashboards and Reports?

66,038,000 years ago, a massive asteroid smashed into the earth in what is now Mexico's Yucatan Peninsula. After this massive collision, it took only 33,000 years before the dinosaurs were entirely extinct — a blink of an eye in terms of the history of the earth.

This asteroid is considered to be the "final blow" after a series of ecosystem changes (other asteroids, volcanos, etc.) created a fragile environment for the poor dinosaurs. The climate changed, the dinosaurs died out, and the mammals took over.

Incumbent solutions for delivering data —dashboard and reporting tools— are facing their own "fragile environment." The big asteroid may not have hit yet, but it is only a matter of time. Here's why.

Exhibit A:

A thoughtful answer from an experienced Tableau user to the question “Why do people still use Tableau?”

We need to consider why (and when) people might stop using Tableau. My opinion is that Tableau has failed to realise two important things about their software and that if another company can solve this problem then Tableau could really lose out:

1. Companies need to create applications, not just reports

Yes, Tableau is interactive but you cannot use Tableau to make applications that write back to a database. It has maps, yes.. But you cannot use Tableau as the basis for an app like you might with MapBox (which has multiple SDKs for different platforms) or Leaflet.js for instance. Tableau is not designed for this, so if you need apps and not reports then it is not for you. You need a developer (or dev team) instead. This is a big gap in the product that other companies are also failing to see.

2. Tableau’s software does not directly generate revenue for (the majority) of their users

For a company to run several copies of Tableau desktop costs several thousand pounds. This is without the additional costs of Tableau Server or end-user licenses that you will need if you want your customers to use your hosted visualisations and dashboards. Any business that chooses to use Tableau to deliver interactive reports to its customers would need to consider passing some of that cost (or all of it) onto its end users. But when we’re talking about interactive reports, not applications, it is hard to justify data reporting as a stand-alone or additional cost.

That’s a real user wondering whether the paradigm of visual analytics tools for analysts, dashboards for executives, and reports delivered to customers and stakeholders is going to hold up for much longer.

Exhibit B:

Analytics vendors and market analysts are using language that leans more toward delivering "apps." 

Alteryx

PwC analytical app marketplace

Infor

Gartner's IT Glossary

IBM Cognos

Is “app” more than a rebranding of a decade of data visualization tools? We think so. Here’s why we see analytical apps are on the way to taking over the BI world:

1. Apps have a purpose. A report or dashboard may carry a title, but it is less common that they have a clear and specific purpose. A well-conceived analytical app knows the problem it is trying to solve and what data is necessary to solve it. In this way they are similar to the apps on your phone — they solve a problem the same way a mapping app shows you how to get to the Chuck E. Cheese and a weather app lets you know if you need to bring an umbrella.

2. Apps make data exploration easy. I’ve spent a decade railing against poorly designed dashboards that put the burden on users to find where to start, how to traverse the data, and what actions to take. Good analytics apps willingly carry that burden. Whether we call it “data storytelling,” narrative flow, or quality user experience design, the app should deliver a useful path through the data to make smart decisions.

3. Apps are collaborative. Most business decisions are made as a group. If that weren’t the case, you’d have a lot fewer meetings on your calendar. Why should data-driven decisions be any different? Historically, reports and dashboards treat data delivery as a broadcast medium — a one-way flow of information to a broad audience. But that’s just the start: the recipients need to explore, understand, and find and share insights. They should bring their own context to a discussion, then decisions should be made. Our belief is that data analysis should be more social than solitary. It is at the heart of the “discussions" feature built into our data storytelling platform, Juicebox.

4. Apps lead to action. "What would you do if you knew that information?” That’s the question we ask again and again in working with companies that want to make data useful. Understanding the connection between data and action creates a higher expectation of your data. Analytical apps connect the dots from data to exploration to insight to action.

5. Apps are personalized and role-specific. The attitude of "one size fits all" is typically applied when creating a dashboard or report, and then it is up to individuals to find their own meaning. Analytical apps strive to deliver the right information for each person. How? By utilizing permissions for a user to only see certain data, automatically saving views of the data, and presenting content relevant to the user’s role.

The mammals took over because conditions changed, and the outdated species — with its size and sharp teeth — couldn’t adapt. Expectations are changing the analytics world. Consumers of data want an experience like they enjoy on their mobile devices. They don’t have the attention to pour over a bulky, unfocused spreadsheet, and they expect the ability collaborate with their remote peers. The climate has changed, and so too must our approach to delivering data.

If you’re still churning out reports, we can help you do better. Or if you’ve constructed a one-page dashboard, we can show you a different approach. Drop us a line at info@juiceanalytics.com or send us a message using the form below.

What's in a Juicebox: Discussions

What good is information if it cannot be shared and discussed? One of the founding pillars of Juicebox is communication; we aim to allow users, regardless of their familiarity with data analysis, the ability to easily identify and discuss important data points.

In taking on this challenge of what we call "The Last Mile of Business Intelligence", the question we must constantly ask ourselves is, "How do we make starting a conversation around data as easy as sending a text message?"

In order to solve for this, we have taken the knowledge gained from our 11+ years of designing and creating custom data applications and created an interactive data storytelling platform that helps everyday information workers make smarter decisions. Our goal for Juicebox is to reinvent the way people discuss and communicate data in the workplace and to their customers.

Our Discussions feature within Juicebox does just that by enabling those conversations around data in a method that is intuitive, quick, and effortless, especially compared to traditional processes. In the past when an insight was discovered within a spreadsheet, an analyst would have to send a report to a decision maker and ask him or her to review the finding. Not only was this process clumsy and time-consuming, the analyst and the decision maker were often on different planes in terms of data skill level. With Discussions, those conversing over the data can take a snapshot of the visual, mark it up, and download it in order to ensure the most relevant and important information is being shared. 

If you're interested in having conversations around your data, we would love to talk with you. Send us a message at info@juiceanalytics.com or click below to tell us more about what you're looking for. 

Data Storytelling Workshops, Part 2: Data Story Showcase

This is part two in a series on sharing Juice’s data storytelling method at various workshops around the United States. In part one, we talked about the highlights of teaching business professionals how to build insightful data stories in under an hour. Here we’ll showcase a data story that was built by one of our own Juicers who attended a workshop.

When creating data stories at Juice’s data storytelling workshops, we always start with a set of data from Nashville’s Open Data project. There are an infinite amount of data stories that can be created from a set of data like this that contains information on construction permits, location, cost, and type of building permit. For our data story prototype, we decided we wanted to know where to find construction projects for multi-family housing so that we could determine where to best build a cool new coffee shop.

When creating our data story, it was important to us that we first give it a title. There are many different strategies that you can utilize when coming up with a title, but we decided to start with a simple yet profound question that would ultimately be answered by the end of the data story.

We find that in order to ensure that the data story being created is coherent and focused, it’s crucial to determine how each visualization contributes to the overall goal of the story. In this example, we wanted to have a high volume of people who enjoy a good cup of coffee and would be likely to visit our coffee shop. To display this in our data story, we would zoom into the map to browse areas around Nashville that are sized by the number of multifamily home projects currently underway in a given zip code.

Once we had selected a zip code that had a business-sustainable number of multifamily projects underway, we also wanted to check to see which way the number of projects in that zip code has been trending over the past 5 years. After seeing that the volume of projects in our zip code of interest had been positively trending over the past few years, we would search through the table at the bottom to find the largest one of these projects to build our coffee shop near so that we can maximize our chances for success.

Want to know more about our data storytelling process? Send us a message! We're always happy to share our methodology or to answer any questions.

 

Data Storytelling Workshops, Part 1

The Juice team has been traveling around from conference to conference showcase our method of quickly and easily creating data stories from a data set. We got the opportunity to utilize Nashville’s Open Data project to source the data we used for the workshops. Attendees were divided into several groups and given the option to choose between several personas for whom they would build their data story. By focusing on a particular type of user’s goals, attendees were easily able to create questions that should be answered by the data. These questions or “goals” for their data story were written out on sticky notes by each group member and were shared with the entire group.

Once the goals for their stories were distilled into just 3 questions total, attendees chose metrics and dimensions that would function to best answer the questions that would achieve the goals that their personas wanted from the data story. Making decisions about building effective data stories typically take hours if not days. We were able to accomplish this in less than an hour and saw attendees leave with a full understanding of how a great data story is built for a particular audience or user.

Some workshop moments that were captured can be found in the 30-second video below:

Stay tuned for part two, in which we will showcase a data story that was created by one of our attendees. You won’t want to miss what he created in just under an hour with Juice’s guidance!

If you can't wait and want to see how you can start making your own data stories with Juice, send us a message using the button below.

Why We Prototype

At Juice, we’ve spent the last year relentlessly pushing to make it easier to build world-class interactive analytical applications, or "data stories.” This was an important change for us. In the past, like a design agency, we would create carefully-crafted user interface mock-ups with detailed descriptions of functionality and interactions. Anything we couldn’t show in a static picture we would describe in words. Now we can do something massively more effective: we can build a live, interactive prototype in the time it takes us to draw all those pictures.

Here are the most important reasons we felt it was necessary to be able to prototype with ease:

1. Non-designers don't speak the language of mock-ups

With a decade of experience designing analytical interfaces, we became adept at making the mental leap between a static mock-up and the live application it would become. Static mock-ups imply — but don’t show — interaction points. They suggest what the data may look like, but don’t try to accurately show the data. They highlight dynamic content, but can’t show it change.

Take the following visualization mock-up as an example. Can you tell:

• How the orange button will change as you interact with the visualization?
• What happens when you roll over the points?
• Why the title indicates “4 categories”?
• The image implies a lot of functionality to an experienced information design audience. That doesn’t help everyone else.

2. Uncover data difficulties early

Your data isn’t always what you think it is. It certainly isn’t as clean or complete as you might hope. By prototyping with real data, you discover some of the issues in your data that run counter to your assumptions. You may also find trends or patterns that reshape what information you want to show.

Recently we built an application for a client that delivers an assessment checklist. We expected that we’d be able to look at the average scores to see how well students were performing. But in reality, students didn’t need to submit their scores until they were complete (100%). As a result, all the scores were perfect. And perfectly lacking in insight.

Here are just a few of the common things we run into when we prototype with real data:

• Missing values where data should be
• Multiple date fields, sometimes with confusing meanings
• Averages that need to be weighted
• Unexpected behaviors captured in the data that create unexpected data results

3. Validate hypotheses about the story you want to tell

Designs are based on a lot of assumptions about users. How will users interact with the data? What data is important to them? What views will be most impactful?

Prototypes give us the opportunity to test these hypotheses.  We utilize a user experience tool called FullStory to see in detail how users interact with their data story. We can see where they get confused and where they focus their attention. We also ask pointed questions to ensure our assumptions are playing out as we expected.

4. Gather user feedback to sand-off the rough edges

User feedback isn’t only helpful for the big things. It can help you understand whether you’re on track the small, but important, details. A great data application needs to communicate the meaning of the content, including everything from the metrics to the labels to the descriptive notes. A few things to look for:

• Do users understand the meaning of the metrics accurately?
• Do the descriptions and labels convey the right meaning?
• Is the styling — color, contrast — work for users or is it distracting?

5. Build buy-in and a bandwagon

Making the transition from standard reporting to an interactive data application can be a big step for some organizations. For example, it can be scary to imagine giving your customers the ability to explore data by themselves. What will they find?

Taking this big step sometimes requires baby steps. Prototyping is an easy baby step. If you can create a real, working version of a solution to put in front of senior leadership, it will go a long way towards helping them get on board. Now people don’t need to envision what is possible, they can see it.

Interested in building a prototype with your data? Get started by sending us a message!