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.

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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.

The Consequence of Using Visualization Incorrectly

Data visualization can be a powerful tool when leveraged effectively, but what really happens when it’s done poorly? Well, how about a confused user? What about unnecessary support calls or just more meetings to explain what you’re trying to say?

Correct visuals help others understand the data and what it's telling them. When done well it’s like a whole new set of vocabulary for conveying your message. The reasons to avoid poorly presented information and investing in the time to do it right are as follows:

1. It can not only mislead, but lead people to the wrong decisions.

Misleading data can be completely unintentional, but no less damaging. When important data is left out of the visual, it leaves you open to making the wrong decision.

One thing to be aware of is whether or not certain data exists. There are times that specific data isn’t available and it’s important to know if you can get access to it or not. Missing data can lead to misunderstanding.

To make sure that you don’t mislead, start by thinking about your goals. What information is needed to make an accurate decision? Once you know what information you need and what data is available, think about the relationships between data points. Are those vital to understanding? If so, consider visuals that accurately display those relationships so that informed decisions can be made.

Also be sure that whatever visual you use has the right scale. Scale can easily distort information. It can also hide outliers that could actually be important data points to consider as context.

2. It can delay decision making, i.e. “not sure what this says, will look at it later.”

One of the biggest culprits of this problem is choosing the wrong visual. If you are trying to show relationships between data points, but use a bar chart instead of a scatterplot, it’ll take a lot of work to figure out where the relationships exist and what effect they have.

Also be sure to consider your audience. If they are more well versed in visualization, they may benefit from the use of more advanced visuals. But if not, keep it simple rather than alienating people.

The other culprit can be displaying too much or too little information. With too little information, your audience may be left wondering what the main point is and feeling like they need more information to form an opinion. With too much information, data can be overly complicated or cluttered and make it difficult to focus on the real story.

3. It can disengage some of your audience.

When visuals are confusing or difficult to follow or understand, users will trail off. They may decide to come back when they have more time to spend to “figure it out” or they may give up entirely. Neither is going to give you the results you want and can result in delayed, or misinformed, decisions. Some reasons that your audience might disengage are similar to issues above - a confusing visual, too little or too much information.

Ultimately you need to make sure your story is clear and easy to follow. Using visualizations incorrectly can cause you to lose your audience, lose the value in your data and ultimately lead to poor decision making.

For help with effective design and visualization, be sure to check out our design principles, particularly the sections on “guidance” and “audience-centric”.

Emotional Dashboards - Moving from confused to happy customers

Just like hearing that popular song from high school can elicit certain emotions, so too can a dashboard. Intuitively, the words “emotional” and “dashboards” don’t appear to go together. However, it's not difficult to imagine some four letter words being thrown around because “the numbers don’t make sense” or because your audience is frustrated that they can’t understand what you’re trying to say.

Sound familiar? When dashboards do not connect for your audience this is the sad intersection of lost clients, wasted time and dollars.

Today’s dashboards have the power to do more and be more than their predecessors. Just as modern web/application design marry utility, usability, mobility and beauty, so the same can be said for our information displays. Your organization’s data, when presented correctly, should command attention, start a conversation, compel action AND strike the right emotion.

How do you trigger the right emotions with a dashboard?

Frustration #1: Which information is most important!

Unfortunately, more often than not the heart of the designer’s message is lost among all the metrics and chart. In this flurry of enthusiasm to get something done, little attention is paid to guiding the user on how to consume the information, so they get what they need. Take a second look at your dashboard and ask what should be the first thing they see? Will they know it’s the most important.

Frustration #2: There’s too much detail!

Don't get caught in the myth that more is better. Your users probably have other responsibilities other than looking at your work all day. 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 which can be collapsed under the high-level data point, or linked to an appendix, or included in separate report.

Frustration #3: The dashboard looks like Gaudi made it!

Overzealous graphics, too much color, images, etc can cause more harm then good.  Don’t get us wrong, Gaudi was an amazing artist, but, when displaying valuable analyses save your modernism impressions for some other endeavor. Be purposeful in how you use these elements.

There is no need to let those emotions go unchecked when it comes to displaying your dashboard results. To ensure you are triggering the right emotions and ensuring your dashboards are delicious, Download the emotion-filled Juice white paper “Designing Dashboards People Love”. Just updated to reflect the latest in Data Fluency.


The Myth of Flexible Reporting

Flexibility is essential, no doubt, to navigate life and work. Yet, flexibility is not always what is needed. Take for instance this NY Times Article, Too Many Choices: A Problem That Can Paralyze, which states that when we are faced with more options we tend to shut down or make decisions against our best interest. Often times, especially when dealing with data or making decisions, more flexibility is less helpful. It actually works against us in terms of work efficiency and cognitive load. When displaying information to the everyday decision maker, offering them the single best raisin bran choice vs. a list of choices is always best.

So what does this mean for your information sharing and reporting? Does your audience really want more reports or additional download options?

The answer is no. The myth in the reporting and dashboard world is that customers need or want more analytical flexibility and data. Sure there’s always some analyst that REALLY needs it, but more likely your buyer or end user wants your guidance on how to consume the information.   More choices always feels like a good idea on the surface until you begin to see the breakdown that occurs with users when they are drowning in reports or giving your interface that deer in the headlights stare. More reporting flexibility equals more support calls, more report requests, etc.

Here are a few reasons why your users might ask for more flexible reporting options. Be aware this is a HUGE opportunity for you to demonstrate your expertise with the data and guide them down the right path.

#1 Uncertainty - Your users aren’t really sure what they plan to do with the data, don’t know the problem they plan to solve or their business direction changes so often so they figure they need flexibility. This is an opportunity to show them the value of what’s in your data and the types of problems that can be solved. Provide certainty through instruction and guidance vs. feeding the uncertainty. This is a perfect opportunity to be a hero for customers like these.

#2 - Only one Shot -  Whether real or perceived, your customer perceives they only have one shot because of project timelines or budget reasons to solve for everything they need for the next 1+ years. In this case you need to teach them to be gourmets about the data and not think of it as a Vegas buffet. Getting every last bit of data won’t solve any problems. Help them solve a specific business problem with the data, in a timely manner (weeks not months). They will then have the credibility and leverage to ask for more resources. They’ll also have you to thank for it.

#3 - Do it myself - Do it Yourself (DIY) is fine. If they’re experts on your data and you have confidence in their capabilities and ability to come up with the right interpretation then let them go. However, be careful on this one. Often times it means that they don’t trust you to explain, guide or provide them the data they need or have asked for. Be sure to probe on this.  Has it happened right after you delivered a new set of reports? There are risks here of them coming up with the wrong interpretations or worse yet, you’re just a commodity provider of data.  

The place you want to be is like a personal shopper. You recommend, guide and instruct your users to the best path for them. If you just give them the cereal aisle, you’ll only be a cashier.  

When you guide them they know you care and are taking a REAL stake in helping them. Avoid the myth of flexible reporting. Check out Juice’s design principles, specifically the ones on guidance to offer up some ideas on how to implement guidance. As always, feel free to drop us a note with your thoughts or questions at info@juiceanalytics.com or tweet us @juiceanalytics.com.


More Apps; Less Reports

If you needed a phone number, would you use a printed phone book or an application on your iphone? As technology evolves we rely more often on apps (applications) to solve problems like finding a phone number.   The same (r)evolution needs to happen with reporting.  Reports are static and old-school like phone books.   Very rarely does a report solve a problem.  To ensure value for customers and users we need to make the transition from reports to apps.

Reports have their place.  Think annual report or some other regulatory obligation.  Your customers however really aren’t looking for more reports, despite their requests.  What they mean to ask for are apps that answer their questions and solve problems.  

We need to provide them a better, more modern way to stay informed and discuss data with others.  The everyday decision maker understands the notion of an app because they use them every day.   Much better than some dashboard or report you might create.  The idea of delivering apps vs. reports isn’t some unique idea.  

Gartner says that by 2017, 25 percent of enterprises will have an enterprise app store.   As apps take over enterprise solutions why shouldn’t reporting solutions follow suit. Probably closer to home is Google Analytics.  While it’s a dashboard and a way to generate reports, at its core it’s a series of apps.

  • A few other reasons we’re biased towards delivering data as apps vs. reports:
  • Creates a mindset of delivering big data in bite size portions.   
  • Demonstrates your expertise or knowledge of the data since it’s more discrete.
  • Forces you to think mobile-friendly.
  • Allows you to use proven web design best practices
  • Permits a new way to talk about the data with your customers, i.e. solving problems vs. displaying information

If you want to further your fluency in  “App thinking”, check out some of our other content which will offer some practical tips, especially our data product checklist.

"Gartner Says That by 2017, 25 Percent of Enterprises Will ..." 2013. 10 Aug. 2015 <http://www.gartner.com/newsroom/id/2334015>

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.

Guide Users to Understanding With Common Structures

When was the last time you vacationed in a new city? Remember that feeling of being lost until you had a couple landmarks under your belt or had to pull out your smartphone for guidance? Well it’s also important to provide that same guidance for users of your data products so they aren’t lost when trying to utilize it. For successful navigation, use structure to guide users on a path through your data product.

Laying out information is often undervalued, so we end up seeing a lot of visuals that are haphazardly placed on the screen. Sure, all the information is there for you - but can you understand it? Not every user will be an expert in the data, so that’s where guidance is important. Can people understand how the data points relate? If the way the data is presented doesn’t help the user’s understanding move forward, then the product has failed.

When deciding how to structure your information, consider the general structure of the underlying data. Related items should be near each other, there should be a clear entry point to reading the information, and important items should be more prominent. All these things can help move someone through the information and affect the way they approach the business problem.

Here are 3 Common Structures Used in Data Products:

The first structure is flow. This emphasizes your business’s sequence of events or actions across time. Generally, a flow structure will be based on an underlying process with a beginning and an end. Think about that vacation, you decide what new cities to visit, dates to stay at each location and create an itinerary or flow. All this data informs where your vacation will take you and when.

Relationships are another common structure for data. With information design, you  can emphasize relationships by using connective lines and descriptive labels so the user can understand how things are connected.  A common illustration of relationships are found in things like the metro or subway maps that you rely on as a traveler to get around the city.

Finally, grouping as a last resort for structure. Grouping data categorizes information and creates hierarchy. By grouping similar things you can help bring order and logic to otherwise haphazard information. While traveling, figuring out where to get your next meal may help you understand grouping. You can use your smartphone to check out venues by categories like type of food, ethnicity, neighborhood, price or reviewer ratings.

By keeping these structures in mind, you ensure that your users are guided down a path. This leads to better understanding and ultimately, action.

Find out more about effective data visualization from our book, Data Fluency.

Get a free excerpt from the book!  (enter code: FLUENCY-EXCERPT)

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.