Our Favorite Data Products

The bar is set pretty high around here, but there are definitely some awesome data products we've come across and thought we would share.

So, what makes a great data product? For Juice a great data product or application meets the following criteria:

• Delivers valuable data to consumers or non-analytical users.
• Displays presentation quality information vs. a four quadrant dashboard.
• Offers interactivity and a way for the user to engage with the data.
• Guides users through the information and does a great job with data storytelling.
• The product uses data to drive revenue or retention.

The following are our favorite, non-Juice created, data products in the market today. Let us know at info@juiceanalytics.com of any that we're missing that meet these criteria.

Five Thirty Eight (fivethirtyeight.com)

These guys have really grown on us, especially in the recent months. They do a great job of wrapping data into a story. Their data storytelling skills are well developed. In the example below we value how they give color meaning and make labels easy to understand. Annotations and text descriptions are definitely where many data products are lacking.

True Car 

We really like the way True Car applies guidance to the user experience. It's very clear what actions the user should take. The interactivity of the histogram as well as the animations applied reveal the impact of the selected changes in a clear way. Note how the simple use of the numbers going left to right make it so that a first time user knows example how to use the application and consume the information. Also, the use of action words like See, View and Get are subtle, but give an added layer of clarity.

Payscale (www.payscale.com)

While Payscale, a salary comparison application, isn't as true(car) to the Juice Design Principles as we'd hope, there are some items they've done well. We love when charts are labeled using questions. See below how they use questions to describe what the charts are trying to answer. The user doesn't have try to interpret what is being conveyed. They know right away.

Foursquare Analytics (foursquare.com)

The Foursquare team offers this interface for customers to understand their user traffic.  There's lots of information to convey, but their focus on filters at the top and modular design really make it easy for the user to focus their attention on a specific data set.   I did crop the 3D pie chart from this display. It took away from a pretty valuable application.

To get more details on what makes a great data product, check out the case studies found throughout the solution pages but specifically at the bottom of the Healthcare page for case studies on Healthstream and Laerdal.

Video: The Juicebox Recipe

Since creating our platform Juicebox, we often get a lot of questions about what it is (and isn't). And while we could give you a bulleted list of facts about Juicebox and what makes it so special, we decided that it's better to show rather than tell. Find out why it's so important to make your data useful and engaging, and see just what it means when we say we're here to make your data delicious.

Want to learn more about how Juicebox can make your data delicious? We'd love to schedule some time to get to know you and show you more of what Juicebox can do. 

Putting People First in your Big Data Initiative

You have the resources and the data, but how do you package information so customers find it valuable? This e-book, Putting People First in your Big Data Initiative, summarizes how to make data valuable for customers. The focus is largely on those non-analytical audiences - the people that beg you for more information but won’t use your new dashboard. It also offers ideas on extending your Big Data efforts outside your organization. 

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The idea of putting people first goes way back to Zach’s Last Mile of Business Intelligence blog post in 2007, where he highlighted that the user experience is often the forgotten stage of any BI project. Fast forward to 2016 and the same can now be said for the Big Data project. The real value or monetization opportunity of these projects lies in making customers a big part of the success equation. Getting customers engaged, using your data, asking for your guidance and expertise should be the end goal of these projects. Given the investment and effort required to store, clean and analyze data, putting people first is a helpful reminder of where the finish line really is.   

You may not be ready to put people first now. There’s just too much messy data, too many tests that need to get run and too many new requirements to think beyond this week’s tasks. However, when you turn the corner and the conversation changes and you're ready to talk monetization, customer reports or data products, this document gives you the storytelling tips needed to develop an Information Experience that customers will love, so they clearly feel they were put first all along.

It's an easy read of 15 pages with plenty of tips, links and resources to help you be successful.   

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People First Ebook Blog Post

Automated Presentations (Slide Factory 2.0)

Much has changed since our original post in 2009, yet much remains the same.  There's been a variety of solutions, like Prezi, SlideRocket and even some home grown Python integrations, aimed at improving PowerPoint and presentation automation. However, its still challenging for a non-developer to produce a good-looking, effective PowerPoint deck with automatically updated charts.

The best way to tackle this challenge -- for the moment -- is to simplify the problem. While a utopian solution may not be available (sorry),  here's a way to break down the problem and get a partial win.

Think of the presentation automation challenge as one of three distinct challenges. 

1. Delivering Presentations @ Scale
2. Automating Chart Updates
3. Improving PowerPoint Chart Availability

Delivering Presentations @ Scale

When you want to deliver high-quality slides or share information as a story for a large audience, like all your customers, this is what Juice refers to as Presentations @ Scale.   It manifests itself in organizations when there are multiple dedicated resources manually producing PowerPoint slides for clients. This is because a report doesn’t provide enough contextual information and narrative structure (flow) as can be delivered through slides. Some examples where organizations deliver Presentations @ Scale are:

1. Quarterly account reviews produced by ad agencies;
2. SLA reviews by technology providers;
3. Quarterly reviews by insurance providers to human resources leadership.

While customers value the effort and details, the energy to produce these documents is expensive. Its not uncommon for Juice to hear about organizations with teams of 5 to 10 people dedicated to creating customer PowerPoint slides.

The opportunity to improve frequency and reduce the cost associated with delivering Presentations @ Scale lies in web-based solutions where customers can consume the information as an interactive web page vs. static slides. Here’s an recent example from the New York Times that offers a taste of a scrolling presentation or story.

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It offers the easy to consume format, valuable data displays with a lot of descriptive text. Juicebox, is intended to solve exactly this kind of problem. Click here to see a quick video of Juicebox in action to get a flavor of delivering slide quality information across many customers.

Automating Chart Updates

The most popular or frequent PowerPoint automation challenge is automatic chart updates. There are an increasing amount of programatic solutions for this problem; however the options below require decent technical skills to set up and maintain. It's still a surprise that no solution has come to the forefront or solved this yet. Here are some of the technical options to check out, which require VBA skills at a minimum to automate chart updates. In addition to the ones below, Lea Pica has some product and tools on her resource page worth checking out.

1.  Microsoft PowerPoint VBA - Some guidelines and tips for Office 2013 
2. PowerPoint VBA FAQs - Some helpful tips on PowerPoint VBA (a little dated).
3. PowerPoint 2010 Chart Programming - Registration required, but some good VBA answers here.

Improving PowerPoint Chart Availability

Probably the option least talked about or referred to directly are PowerPoint’s chart limitations.   Prior to 2011 the chart options were very limited. In most cases now, this represents enterprises that are still behind on their Microsoft Office upgrades and are limited by the few chart options in these earlier versions. There are some really elaborate integrations of PowerPoint using Python available now. Just search YouTube and you'll find a bunch.

Please share any other solutions that are out there in the market place that solve one or more of the presentation automation challenges. In the meantime, check out the Juicebox demo or request a personal demonstration by clicking here.   

30 Days to Data Storytelling - Updated

There continues to be interest in data storytelling. For example, consider Cole Nussbaumer’s recently-released book, Storytelling with Data.  The amount of quality content on the subject continues to grow, and that’s why we decided to do a refresh on our 30 Days to Data Storytelling document from 2013.

New look, but the basic principle is still the same. Through a series of exercises, you’ll learn some of the best techniques for delivering information in a way that people understand, absorb, and act on it. You can skip around, or follow the daily instructions - the choice is yours. Either way, in less than four weeks you’ll be telling stories like a pro.

Already completed it and ready for more? Some other thought leaders on the topic, in addition to Cole, to check out include Lynn Cherny, Robert Kosara, and Alberto Cairo.

10 Screenwriting Lessons for the Aspiring Data Author

The art of data communication is in its infancy. Fortunately we can learn from other forms. Photography, cartoons, literature, painting, poetry, graphic design -- these are all about using language (visual, aural, written, etc.) to capture attention, convey information and ideas, and move an audience in some way. (In fact, helping organizations understand the power of data communication was the goal of our book Data Fluency.)

When I came across John August’s blog post about how to write a scene, I saw parallels with dashboard and visualization design. John is an accomplished screenwriter (Big Fish, Charlie and the Chocolate Factory, Frankenweenie) and popular blogger and podcaster.

His first piece of guidance: “What needs to happen in this scene? ...The question is not, “What could happen?” or “What should happen?” It is only, “What needs to happen?”

This is the critical concept in all of information design. It isn't a question of what data can you show, it is a question of what data you need to show. How do you need to propel your users forward in their role? Give your audience data that they can use to be better at what they do.

Next, he asks the screenwriter: “What’s the worst that would happen if this scene were omitted?...One thing you learn after a few produced movies is that anything that can be cut will be cut, so put your best material into moments that will absolutely be there when it’s done."

Like a movie audience, your audience has a limited attention span (unfortunately the data presentation business has fewer built-in constraints than the movie business). What data can you remove from the report that won't leave decision-makers misguided or confused? In our work, we always ask: What action is someone going to take when they see this data? If there isn't a clear answer, then leaving it out will help the reader focus on things that are more important.

John emphasizes the importance of choosing your setting..."A father-and-son bonding moment at a slaughter house will play differently than the same dialogue at a lawn bowling tournament."

It is no different for considering how information is presented to your audience. Information designers may overlook the different ways for presenting and wrapping context around the data. A daily email report, a printed slide deck, or an interactive dashboard will have very different impacts on your target audience.

"What’s the most surprising thing that could happen in the scene?"

In other words, what options do you have for grabbing the attention of the your audience? Great data visualizations do this by making data emotionally resonant. A couple good examples include The Fallen of WWII and US Gun Deaths (both grim data stories). In a more mundane example, we designed a data app that showed the costs of training programs in hospitals. By putting a dollar figure on this everyday investment, we were able to capture attention in a new way.

"Is this a long scene or a short scene?"

Edit yourself, show less data, and say more. We all have experienced the scourge of the neverending powerpoint deck or Excel report with endless sheets. Extraneous content comes at a high cost.

"Brainstorm three different ways it could begin."

Dashboards seldom consider a beginning or an end. But your audience will, one way or another, find a starting point and explore data in a sequence. Will you help them with this path? I believe it is crucial to offer an obvious place to begin and useful end-points. It is a feature we've baked into the fundamental design of our Juicebox platform. 

"Play it on the screen in your head."

I love this advice as applied to information design. Imagine your visualizations with different amounts of data, different values, different results and insights. Pretty soon you'll find the weaknesses. This is my first critique of the pretty dashboards designed on Dribbble. The data will never look so pretty as this in real life and the design will become incomprehensible.

Finally, John ends with advice on the writing process: 1. Outline; 2. Write the full scene; 3. Repeat 200 times. He wants screenwriters to start with the bones of the story, fill in the flesh, then iterate — without fear of tearing the whole thing down if it isn’t working.

Every form of communication has its challenges. Films face constraints and audience expectations, and yet have creative breadth in what can be put on the screen. Communicating data also has an interesting challenge for data authors. It takes a rigorous, analytical mind to understand the data and its meaning, but also requires the artistic skills of a screenwriter. It is a rare combination that needs to be taught and cultivated. If you don’t fit in the slim overlap of this Venn diagram, there is more to learn.

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Reaching Beyond Data Visualization

The practice of analytics suffers from a persistent disconnect between the people who create (data authors) and those who might do something with the information (data consumers). If you've ever emailed an important analysis or shared a dashboard, and felt that your work had fallen into a void, then you know what I mean.

The gap between your data and informed-actions has to do with data authors and data consumers struggling to find common ground. Conventional wisdom has suggested that data visualization is the bridge. But after 10 years in this business, I've come to believe that better data visualization isn't enough to cross the divide. Making your analytics truly useful requires more: closer connection with your audience to help them understand the meaning; the ability to socialize the insights within an organization; and clear links between those insights and feasible actions.

This was the message that I shared -- along with my colleague Christian Oliver (VP of Data Products, HealthStream) -- at the 3rd annual Nashville Analytics Summit.

It is a humanist perspective. If we want everyday decision-makers to use data, we need more empathy for their work, the actions they can take, and how they choose to do things in a social environment. 

Beyond Data Visualization: What's next in communicating with data? from Zach Gemignani

Peter Thiel's book Zero to One hits on a similar theme:

"Today's companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don't know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst...

We have let ourselves become enchanted by big data only because we exoticize technology. We're impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contributions make them less uncanny."

In your role as a data author, you have three imperatives that go beyond well-designed visual communication:

1. Recognize that visuals are just the beginning of the journey in influencing your audience. They can start the conversation and educate, but that's not the end game.
2. Understand your audience's job. Not just in the abstract but in the details of what actions they can and cannot take.
3. Guide your audience to those actions. People are busy. Part of your responsibility is to help them quickly connect the dots between what you are saying with data and what they should do about it. This isn't dumbing things down; it is taking an extra step to make you and them successful.

Naturally enough, this is the philosophy that animates our Juicebox product design. Check it out with a personalized demo.

"Chart" new territory with your data

Amazing discoveries start with an innovative mind willing to look at things differently. Take Columbus, they said he was crazy for sailing the ocean blue in search of the “new world”. Well here’s another outrageous idea for you!  What if you could use your Big Data project as a way to make additional revenue? Here are some ideas so that you can begin to chart this unknown territory with your Big Data, and turn your discoveries into dollars.

3 ways to monetize your data

It is logical to use company data to save money and find cost savings internally. But what if you take another approach with that same data? Check this out-- U.S. News and World Report was able to make their own discovery.  They created the criteria and collected the data on college rankings for decades. And each year universities fight for the top rankings in their region or for a particular education track. They produced these ranking reports geared toward the prospective student. One day they stepped back and took another look at the rich data they had collected over the years, realizing they had another (big) market for this information. If they could package and sell it in a new way, to the colleges and universities, they could provide valuable insight and create new revenue streams!

So here are some tips to help you think outside the box with your Big Data.

1.  Make it unique

Think of ways that you can make the data unique to your audience and their needs. You have data that no one else has, and it can help users make better decisions. Think about who, outside your business, could benefit from this unique information, and how they can benefit. Then apply some additional strategies to really make your data a must have:

Mashup - combine your Big Data with a public data set

What would happen if you combined your data with a data set on data.gov or another public set? Perhaps you work in the public health sector as an executive of a health insurance company. You could overlay your Big Data with government census data to identify healthcare trends that a growing hospital needs to plan for. The hospitals could use your data product to set up their hospital for the future. Here’s a list of companies already using government data in creative ways.

Predictive Analytics - find the treasure in future trends

Can we apply an algorithm to our data to find some special meaning or make the data more helpful? Predikto is one company that has this down in the railroad industry. They have a great product to predict the breakdown of railroad track safety monitors. Their product analyzes a plethora of data from weather to train loads to provide maintenance crews critical yet simple health-check displays, so they can easily see when these monitors are likely to fail and preemptively send a crew out for repairs before any damage is done.

Composite Metrics - if you build it they will come

Sometimes a simple metric isn’t enough if it can’t fully describe a behavior or the performance of a system. That’s when you need to come up with a Franken-measure: a made-up metric that creates a comprehensive composite to capture complex concepts. Think Google’s PageRank or the NFL’s Passer Rating. PageRank combines multiple complex metrics on web traffic and trends in such a way that the end result is something we can understand and use.

2. Put your best efforts into the user experience.

By putting yourself in your user’s shoes, then you can design data products much more effectively. First, like we mentioned earlier, you need to really think about who your audience is and what your audience needs to get from the data. How does this impact the way you tell the story of the data, and how you design the product so that the users can see the value immediately?

More often than not, the heart of the designer’s message is lost among all the metrics and charts. In this flurry of enthusiasm to display tons of data, little attention is paid to the user and guiding them on how to consume the information.

Remember, your data consumers are not the experts in the data like you are.  Your users probably have responsibilities other than analyzing data. Give them the high-level path to follow, and let those users who need more info have the option to drill down into the details. Think about the delivery of data much like the way you tell a story, provide a beginning (starting point), middle (critical details) and end (decision points).

3. Start small, design one product first that solves a real problem easily.  

It’s better to prototype a data product that is ready to put in front of a user in six weeks instead of six months. This allows you to keep it simple and make adjustments quickly based on what’s working and what’s not. Think like Google. Put out a concept or idea as a beta, study the user responses and feedback and add more capabilities as you go. This kind of logic allows for a quick release, less investment in development of the product and the opportunity to grow with the consumer.

Now that you are ready to set sail and chart your own new data territory, here are more helpful leads to help you do more with your data products!

Join us in December for our webinar on Turning Data into Dollars.

Also check out DJ Patil’s (the U.S. Government’s Chief Data Scientist)  free e-book, Data Jujitsu, the art of creating a data product.

Finally, take a look at our own, Zach Gemignani’s slideshare on turning data into dollars.

For a demo of our product, Juicebox, schedule an appointment.

Statisticians & Storytellers

We decided to refresh and re-JUICE-enate a blog post that Chris wrote in 2005.  Click here to read the original.

The way the game of analytics is played today, there isn’t much competition. The statisticians are on the field, the storytellers are on the bench—or in the stands—or, sometimes, not even in the stadium.

We first expressed this sentiment almost a decade ago, but in the last ten years not much has changed. There may be a lot more players, and there is definitely a lot more data, but statisticians and storytellers still aren’t playing on the same field together.

In Chris’ earlier post we proposed that the answer to getting statisticians and storytellers to work together lay in the visualizations themselves. We still maintain that’s true, but believe that there is a bigger emphasis needed on storytelling and fostering discussions among people about data.  While visualizations were relatively new ten years ago, today every CFO around the world has already approved their first visualization purchase order. Unfortunately, there’s too much focus on charting capability vs. bringing these teams to together to solve real business problems.  As Chris stated 10 years ago, visualizations should be designed not to just reflect data, but to engage the user with a story. When visualizations are approached in this way, it provides the perfect environment for statisticians and storytellers to collaborate and succeed.

At Juice, we’re now ten years into our story, we continue to help organizations bridge the gap between statisticians and storytellers. To learn some of the recent stories check out the case studies on our travel and hospitality, digital advertising, research, and healthcare solution pages.  Give them a read and share your story in the comments below.

Stop users from walking in circles, four rules to lead them like a tour guide!

A good tour guide always enhances our experience at art galleries, museums, historic sites or production facilities. We want to be guided.  Who has the time to put the hours into researching and becoming an expert on Picasso’s Blue Period or how Sierra Nevada brews beer on a national scale? We value and need others’ expertise. Your users are no different, they want to be guided by your expertise.

Think of the best tours you've been on. Most likely, the tour guide led your group in the best direction and was available for questions when needed. Great guides teach you about the topic at hand, but also leave room for self discovery. The same holds true for users of your data.

Turn your data into a guided tour

Rule #1: Don’t leave your users hanging. They should not walk away with unanswered questions or feel that they wasted their time. Respect the fact that your users have full-time jobs- they're not experts in your data.  When designing your data solutions, keep in mind that your product is the guide that helps facilitate the information experience, discussions and decision making.

Rule #2: Every story has a beginning. As the tour guide for your company, your job is to create a starting point for your audience. Mounds of information can be overwhelming to a user. Interesting and engaging tours set the stage and create a launching point for participants to begin their experience.  Don’t leave them confused with a barrage of charts and tables which lead them in circles!

Rule #3: Gradually reveal more. This means you guide the user through data that you have designed with grouping like topics and themes to be studied one at a time. Leading the tour group through what you have prioritized as important information first. Reveal the high-level information, with the option to see more if desired.

The visual sequence of information is important in designing a data solution that not only guides the audience, but invites them to explore more and dig deeper into the details. Where you place key metrics, position charts and the amount of information you display will all make a big difference in how your audience will interact with the information.

Rule #4: Encourage exploration. The design of the tour, like your data product, should encourage your users to explore on their own as well. When developing your data product keep in mind that the user will want to see the big picture and then be able to engage with the information at a more detailed level. All filters you use to create the data product should provide immediate follow-up to provide continuity for the user.

As your tour concludes, you can see the results of the user’s experience.  But what makes data powerful is creating an experience for the user that informs, instructs, and leads to smart discussions, decisions and actions. Turning your data solutions into guided tours will not only help keep your audience’s confidence and attention, but will maximize your effectiveness in the market with users that feel they got the full value from your tour.

Find out more about effective data visualization, check out our full list of design principles. Also check out our book, Data Fluency.

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.