Gift Ideas for Data and Visualization Lovers

It's that time of year again. Thanksgiving is just a few days away and soon you'll have to answer that question you dread every year - what to get for your data-loving friends for Christmas? Clever gift giving is not easy so we're here to help with some great suggestions. 


A great option for someone who loves data and loves to read. Also a great choice if there's a book you'd really like to read. Get it for them and when they're done, you can borrow it. Win-win. Here are a few books we love:

You can find lots of neat visual gifts on Etsy, from infographics on Zombies, to periodic tables of Game of Thrones, the perfect cup of coffee art, to world map canvas art.

These prints from would also make a great gift.

Visual Family Tree

My Tree and Me - a fun and unique way to visualize your family tree. Just in case you need to answer that timeless question, “how are we related to him again?"


Do you have any great gift suggestions we missed? Leave them in the comments below! Happy shopping!

Make reports better, not just prettier

We hear from people contacting us as well as from other designers that often the design role in the dashboard project is to “just make it look pretty.”    

Well, pretty only gets you so far. Users may pause and stay longer than the typical 15 seconds on the page if pretty, but did they walk away with or accomplish what they wanted?  Do you want users or customers to say it was pretty or useful?

When delivering an information experience™ there are more important goals that outweigh pretty every time.  Here are just a few to consider when designing an information experience.

1. Be purposeful with design choices

Be really intentional on how you incorporate visual elements into your design.  When used with intent it tells the user to take notice of what you’re sharing.  

Use Color Intentionally - Color has meaning. It can communicate emotion, feeling. It can also draw your attention to certain things. To make sure you draw attention to the right things, it’s important to limit the amount of color you use. For example, see the example below.  While mostly grayscale, your eyes are drawn to the red.

                                             Image source: Information Dashboard Design, by Stephen Few

                                             Image source: Information Dashboard Design, by Stephen Few

Avoid information overload - Gradual reveal can be used to guide people through information, while still allowing them to explore.

Simple is best - Use the simplest appropriate visualization for the data you are presenting. Consider what question you are trying to answer and communicate that as quickly as you can with a simple visual that’s easy to understand.

2. Design for Action

Ideo in their September 2015 HBR article, Design for Action, highlight many examples of designing for action.  Much of what they cite is relevant to information displays as well. Some other things to keep in mind:

Integration with workflow - People need to work quickly and efficiently and if it takes too long to get to the information they need, they will move on. Think through your user or customers workflow and how your design can best integrate.

Provide next steps - Keep your users end goal in mind and help them get there. Give them meaningful next steps at appropriate times. In the example below, LinkedIn helps you with setting up your profile by letting you know how much has been completed and which items are still left to complete so you know what’s next.

There is certainly a place for beauty in your dashboard design.  As Chris G. notes in our frequently downloaded “Guide to Creating Dashboards People Love to Use”, “Modern web design has moved on to seek a union of utility, usability and beauty. We must find a similar union when displaying data in business.”  Note how beauty is equally partnered with utility and usability.  There should be a balance.

When designing reports or dashboards, strive for useful, helpful and understandable.  “Pretty” simply isn’t enough.

Offer Self-serve, not Self-solve

Imagine this: after months of waiting for the new dashboard with promises of “actionable insight” and “democratized data” you click on the link and silence.  A numb feeling takes over as you stare  at the buttons and drop-downs as if they were from a commercial airline cockpit and wonder, what do you do with these fancy things do?

As the pace of business quickens, customers need data solutions that are truly self service and not self solve. Organizations continue to deliver solutions masquerading as self service, which offer extreme levels of flexibility and put the burden of solving the problem on the user. There is no service reflected in these solutions. A data product or solution should make the value of the data and how to answer a user’s questions readily apparent to truly be self service.  

Much of what we see in the consumer marketplace isn’t self-serve, rather it is self-solve. Take for example Trunk Club, an online men’s clothing retailer, where it asks customers a few questions about lifestyle, work-life, budget and sizes. Then, Trunk Club becomes their personal shopper and puts together wardrobe options, mailing them directly to the customer each month. This is a self-serve approach.


A self-solve experience, in contrast, will require more of your time.  The self-solve approach to clothing shopping is to turn buyers loose at the mall or on Ebay, where there is very little direction or guidance given to the shopper’s specific needs. Self-solve requires you to figure out the process yourself.   Self-solve involves an instruction manual or many rounds of trial and error.  

How do you know if your solution is self-solve or self-serve? Here are four clear points to help you distinguish the difference.

1. No Instruction Manual or Training Required. Remember the point of self-serve is to make life easier. Most users are not looking to invest more time, but less. Embed your training within the dashboard or application at key points.

2. Built to Answer Specific Questions. A self service solution is intended to answer specific questions. It’s not a means of just dumping information on someone.

3. Encourage vs. discourages exploration. While a long series of drop down menus may feel like it offers lots of exploration, it really doesn’t. Think of the paradox of choice. Offer a few options with interactivity, so the user sees immediately the fruits (or juice) of their efforts and wants to try more.

4. Make Steps Sequential. In the web analytics world you often want to see paths or steps the users took to make sure they’re guided down the intended flows. The same can be said for your dashboard. Make the flow or steps sequential and easy to follow.

What makes YOUR data powerful is creating an experience for the user that informs, instructs, and leads to smart discussions. Keep your audience engaged and deliver real value with a REAL self-serve solution. Don’t make them figure it out, because chances are, they won’t.

Review our design principles for a helpful guide and review examples of effective self-service models.

Schedule a demo to see how Juicebox can transform the information experience that your audience needs and values.


Building Customer Loyalty through Reporting

Over the past few months we’ve heard numerous stories, like the SEO reporting example highlighted here, where organizations are losing customers or renewals are in jeopardy because of poor reporting.   It hasn’t been specific to one industry either.  We’ve heard this story in healthcare services, software maintenance renewals, ad agencies, etc.

What we’ve noticed as a common thread across these cases is that reporting is viewed as a compliance activity or requirement, not an opportunity to connect with customers.  Reporting or the sharing of insights, is rarely thought of as a means of educating customers, sharing expertise or part of the overall customer experience.   

While there are many opportunities to build customer loyalty using data, i.e. using predictive analytics, to personalize offerings, we’ve created a short, no registration, e-book, Building Customer Loyalty Through Great Reporting, to articulate how a valuable Information Experience TM can enhance the overall customer experience you deliver and round out all your touch points.

The e-book is an easy late night Kindle read or lunch time scan.  Please check it out and let us know what you think about the relationship between reporting and your customer experience.  You can download it by clicking here.  

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


"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 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.

Not Knowing Where To Start

Books, movies and music all have a beginning. Data, when presented or shared, often does not have an intuitive starting point.  The challenge of not having a clear beginning is that when you see a dashboard littered with a dozen competing charts it’s easy to disengage. Tables of raw data can be even worse. Dashboards or reports are often designed to deliver everything and the kitchen sink.         

Here are a couple of examples of dashboards that miss the mark in terms of telling their audience where to start.  In both of these cases the user has to be familiar with the data and know how to read the information correctly.  Beginner or infrequent users will struggle to understand the value of this data.  Without guiding them, the users can lose interest and choose to avoid using the information altogether.





Good dashboards or reports start with a high-level summary and then let users progressively and logically drill into more complex details and context. They are also simple and uncluttered. They use white space and have a clear visual hierarchy.   Here are a few of alternative examples to get the wheels turning.                        

Even this more advanced interactive visualization, called a TreeMap, offers clarity on where to start and how to use it.

To have your audience follow your story it’s important to get them started on the right path.  Think Steven Covey’s, Begin with the End in Mind.  Just like a story your audience is along for the ride.  Carry them from initial explanation to a new, shared understanding.   Only then will they begin to value the effort you put into assembling and presenting the information you’ve given them.

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

Find out more on effective data visualization from our book, Data Fluency. Excerpted here 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.

4 must-do’s to get the most out of your benchmarks

We all joke about “keeping up with the Joneses”. This timeless expression is synonymous with the human tendency to compare ourselves to others. Following the rise in suburbia, neighbors continue to “one-up” each other as a way to establish  socioeconomic dominance. This neighborly competition plays out in categories like who has the best kept lawn, the newest car, and whose kid won first place in the spelling bee.

Just like we compare ourselves to our neighbors, businesses benchmark to see how they stack against the competition. In most cases a benchmark is an industry average, peer group or an index (like the DJI or NYSE). Displaying these benchmarks clearly helps your business identify areas for improvement and goal setting.

For example, how does Volkswagen stock compare to the BMW and DJI over the last 3 months?


Motivate your audience

More importantly, benchmarking is used to inspire action. The social pressure to be better (much like the Joneses) catalyzes when you see how you stack against your peers. When your company can see where you are in the standings, everyone works harder to get that one-up.  

Are you using benchmarks effectively in your business?  Here are some must-do’s to get you started.

Item #1: Start with the right metrics

Choose a relevant metric and the right benchmark. Users want benchmarks that are relevant to them. They want to compare themselves to the most similar set. Provide flexibility to define relevant comparisons for the user.

Depending on the action you want to inspire or the goals to set, select the metric that aligns. Then decide whether to compare to an industry, a region or a peer group.

Item #2: Make it easy to find yourself and your peers

When designing the display, think about making it as easy as possible for the audience. Will they be able to see within seconds where they stand? Users love benchmarks because it puts their performance in context. Can they easily identify where their peers are? Don’t make them work to figure it out, or the benchmark becomes ineffective even if the data is accurate.


Item #3: Clearly label your benchmarks

Benchmarks are often not well defined. It can refer to the average of all entities in the data. It can refer to top performers only. Label clearly. If you aren’t sure about your labels, ask a group of users to interpret what they see. You might be surprised at how different all the answers are.

Item #4: Include a large enough benchmark group

Sometimes you need to be careful about cutting your comparison set too thin.  Now’s your chance to share your BIG Data and everything you know about the industry and customers. It’s not enough to say you're 5% below the national average, but to show your audience that it’s based on 10 years worth of data, or millions of survey responses, etc.. Show the depth of your data!

Need more help? Check out the benchmarking functionality within Juicebox where there are several visualizations focused on benchmarking and comparisons.  

Click here to schedule a 30 minute demonstration.

Happy benchmarking!

The Last Mile of Business Intelligence (Revisited)

Here’s another re-juice-inated blog post from years ago. Unfortunately, or perhaps fortunately for us, even after almost eight years, we’re still in the last mile. Even today, with Tableau’s huge success and so many data analytics startups, we still see organizations struggle to turn information into insights for their every day decision makers.

Here is Zach’s post from November 2007:


“The last mile” is a term that often is applied in the telecom industry in reference to “the final leg of delivering connectivity from a communications provider to a customer.” It is an expensive and complex step due to the challenge of pushing information from centralized, high capacity channels to many diverse end-points where information is ultimately used.

We think there is a “last mile” problem in business intelligence too. This critical bridge between data warehouses and communication of insights to decision-makers is often weak or missing. Your investments and meticulous efforts to create a central infrastructure can become worthless without effective delivery to end-users. “But how about my reporting interface?” you wonder. That’s a creaky and narrow bridge to rely on for the last mile of business intelligence.

Listening to our clients, we are confident the last mile is a real problem. The ultimate source of this failure is less clear. Here are a few of theories:

1. The engineers who built the data warehouse build the interface. No offense to the talented individuals who can push around, clean, normalize, and integrate data—but they may not be ideally suited to designing a user interface for non-technical users. A designer wouldn’t create charts that look like this (our favorite example of chart-based encryption):

In the worst case, developers are dismissive of user experience. I’ve met with IT folks who felt confident that providing a massive data table would provide a suitable solution for delivering information to users. “Hey, they’re getting their data. Is there a problem?”

2. Reporting is considered the fundamental mechanism for working with data. Here’s a framework we’ve started to consider in thinking through the multiple approaches for getting value from data:

  • Reporting lets you monitor things that are well-understood and relatively predictable.
  • Exporation or analysis helps you understand new processes and erratic and shifting behaviors.
  • Presentation is about communicating insights and understanding, often building on both reporting and analysis.

Many people assume that a reporting tool is sufficient to do in-depth analysis and communicate results. That’s like trying to build a deck with a screwdriver.

3. Poor fundamentals in information display. Despite the efforts of folks like Edward Tufte and Stephen Few, general literacy in this area is still low. Shiny, 3D pie charts are still acceptable, even desirable in some places. Particularly disturbing is the persistence and pervasiveness of this problem in Excel where there still remains some confusion as to why this is bad information display:

You don’t have to go any further than the Dashboard Spy to find examples of the visual muck that is commonplace.

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.

Screen Shot 2015-09-17 at 11.33.24 AM.png

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.

The End of the One-Page Dashboard

The one-page dashboard is a relic. Its form makes little sense in an era of touch screens, on-demand data, and interfaces crafted for interaction and user experience. It is the single purpose, brick-sized mobile phone compared to your smart phone.

One-page dashboards came from the best of intentions (not all of them, but critique of poorly-designed dashboards is besides the point). The notion was to provide an audience with a single view that showed all the key information together. In this way, the viewer could monitor important data and see where performance was good or bad, all at a glance with the necessary context.

A lot has changed since this type of dashboard was considered the peak of dashboard design (no offense to Jason Lockwood who did a great job within the confines of this exercise):

The admirable use of color and layout cannot overcome the misguided one-page constraint and disconnect from the needs of the viewer.

Can I see all the important information at a glance? While there is a lot of information, not all the useful detail finds a place (axis scales, for one thing). Worse, the volume of information shown is difficult to absorb with the exception of the person who is experienced with the data.

Can you quickly spot the issue areas? The red dots are a start. But they skim the surface of the concerns that could be highlighted. And what if my definition of “concerns” changed based on the viewer's perspective? Furthermore, the viewer gets no guidance as to why certain items are highlighted and what they might do about it.

There is a broken assumption for one-page “monitoring” dashboards: they assume that seeing a problem (with whatever data can be fit on the page) is enough for the viewer. It seldom is. Any viewer with a passing interest will (or should) want to know more, drill deeper, and ask “why?”. A dashboard must not pass on this inherent responsibility to help the viewer. Identifying problems isn’t enough. A good dashboard attempts to help solve those problems.

Jerome Cukier describes the goal of purpose of dashboards: “It’s about putting the needs of your users first...What is something that your users would try to accomplish that could be supported by data and insights?” 

The one-page dashboard is “a man without a country.” It tries to do too much for an executive who would much rather get an alert for the two problem areas...or at least more guidance about the meaning and relevance of what they are seeing. For someone who wants to engage more deeply with the data, the one-pager offers far too little. If done well, it only starts the conversation.

Changes in technology also undermine the premise of single-page dashboards. Trends in how we interact with information also makes this information design form a thing of the past:

  1. The scrolling myth. A decade ago, asking users to scroll was nearly a sin. That’s no longer the case. Touch screens, mouse-scroll wheels, and gestures have made it easy and natural to move vertically on a screen. These interaction models have elongated what user experience designers consider a single screen. Many modern marketing sites are entirely navigated through vertical scrolling. Scrolling acts as a form of guided gradual reveal.
  2. The power of dynamic interfaces. It was once a fair assumption that a dashboard would be a static snapshot of data, lacking the ability for users to interact with the content. Excel was the tool of choice and it took advanced Excel skills to make it interactive. Today there are dozens of dashboard building tools, many of which offer features for connecting key metrics to details that help explain reasons behind changes or outliers. 
  3. The limits of attention. The information age has morphed into the (limited) attention age. Mobile apps, smart watches, and voice-activated interfaces recognize the need to deliver only the most critical information at the right time, and let the user ask for more. The person provides context and desires; the computer provides notifications and answers. This new model of information exchange is entirely at odds with the one-page dashboard. It is unreasonable and suboptimal to expect someone to stare deeply into the densely packed digits and sparklines of a one-page dashboard. There are better ways.

The goals of the one-page dashboard remain: How to show viewers the big picture and understand it in context? How to encourage people to connect the dots across different data points? Modern interfaces have brought us better means to these ends.

No longer is there any meaningful distinction between dashboards to monitor and dashboards to understand. Monitoring highlights problems — and should flow seamlessly into analysis of root cause. The best dashboards do even more: they guide viewers to details that are actionable, tell viewers what actions can be taken, and enable discussions between colleagues. All this doesn’t happen in a single page.