Data Presentation

What's in a Juicebox: Connected Visuals

The ability of an excel novice (i.e. me) to use a pivot table is basically naught. My ability to manipulate data does not exist, and yet I work for one of the most forward-thinking data presentation companies! Nevermind why I was hired, I quickly learned how to use a Juicebox application because Juicebox is designed with the everyday end user in mind. We have tackled the problem of data delivery to both analytical and non-analytical groups. In this post, I want to chat about one of the features that make that possible: connected slices. What is a slice? A slice is a Juice term for a data visualization within a section of Juicebox application.

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I have mentioned before that Narrative Flow is important to Juicebox. Our applications are web-based and users expect to move and navigate from top to bottom, like when interacting with a webpage. Part of that movement from top to bottom in Juicebox means that as the user is making selections within the application, those selections should not only carry down the page but that they should also inform the visuals that follow.

We strive to be the world's best platform for telling data stories and because of that connecting our visuals together is vital. When someone makes a selection in the topmost slice, it places a filter on the data and the selection they make. This filter helps the user narrow down their selection and drill into the data.

Much of the problems with static reports and dashboards is that they only give the user a top-level view of his or her data. Traditional solutions do not provide the ability to drill further to discover what factors could be driving the data. In essence, today's charts, dashboards, reports, and BI solutions give the user a snapshot and not the whole story. 

Curious to see what else is included in Juicebox? Check out some of these posts highlighting other unique features:

Getting unstuck: Give your data a jumpstart

It’s a predicament that we’ve seen many times over: your data is stuck. You’ve tried some reporting through some Excel pivot tables, or you’ve messed around with a Tableau trial, but felt like there wasn’t enough engaging content to get your users excited. Rationalizing why you can’t get your data to be impactful for your business, you think things like, “maybe my users are talking about the data but I just don’t know about it” or “maybe the data isn’t structured in a way that allows for valuable insights to be extracted from it."

If you’re sitting there thinking that your mind is being read by our artificial intelligence, you’re wrong. It's because at Juice we have seen this scenario played out too many times and we’ve made it our mission to make these issues a thing of the past. What you need to do is give your data a jumpstart.

Here’s our suggested plan of action for getting your data unstuck and giving it the jumpstart it needs:

1. Get your data into a readable structure.

  • The first row of your data should always represent the column’s title
  • Columns should contain the same type of values, respectively

  • Each row should represent a case or a single instance within the data and should contain a date of when that data was collected. This means that two different rows in the data can represent the same entity with data collected for it at different points in time.

  • As a consequence of the rule above, the data should include a row identifier column that can be repeated to indicate that different rows of data are representing the same entities.

  • Make yourself a metadata sheet (also commonly known as “data definitions”) that you and other users of the data can refer to.

Here are some simple example data & metadata using the principles above.  

2. Present your data in hierarchical manner catered to specific audiences.

  • Give your audience a call-to-action, let them know why the data is important and why they should care.

  • Begin with presenting high-level key metrics. Think about what the most important numbers are you to your intended audience(s).

  • Give your audience the option to select a few different categories in which to segment and parse-out those important numbers. Doing this will allow your audience to drill-down in the data to get from a high-level to a granular level.

  • Allow your audience to take the data they have drilled down to with them. This could be one row of data out of the thousands they started with at the high-level.  

Here’s an example of this data presentation flow.

3. Engaging your audience in data discussions

  • This one is self-explanatory: talking about the data with others is the best way to squeeze the value from it.

Here’s an example of effective data discussions.

Sounds like a good plan of action, right? If you're still not sure what your next steps should be, we’re here to help.

We’ll work with you to get your data in a structure that makes it valuable, or even create data for you. We’ll build you a data story with that data that helps you and your users understand the data so that you can turn data insights into business actions. We’ll get your users engaged in data discussions and app design feedback so that you know they’re engaged with the data and you know how valuable they perceive the app to be. So drop us a line, we’re here to help.

Lessons from More Than Insights: Beyond Exploratory Data Viz

Last month a group of Juicers attended a lecture at Georgia Tech entitled “More Than Insights: Beyond Exploratory Data Visualization” given by Hanspeter Pfister, Professor of Computer Science and Director of the Institute for Applied Computational Science at Harvard University.

Pfister cited the rise of the infographic, as well as an increased general interest in subjects like data storytelling and data journalism as evidence that more and more people are becoming interested in using visualization to communicate and explore information. But what comes after information is shared?

“After insight comes the message,” Pfister explained. “The information is the ‘what’, the message is the ‘so what’ - the ‘why should I care?’”

Being able to address the “so what” brings a whole new set of challenges to data communication, Pfister told the audience. He explained that we’ve only just begun to scratch the surface of what is possible, that we actually don’t know as much as we think we do about these subjects, and that much more research is needed to even begin to understand these intricacies. To illustrate his point, he used examples from three different subject areas: data visualization, data storytelling, and data tools.

Data Visualization

Pfister cited a study that he had participated in along with Michelle Borkin on what makes a visualization memorable. In the study, participants were shown a string of various visualizations and told to respond if they remembered having seen it previously.

So what did the researchers find made a visualization memorable? The charts were found to be more memorable if they contained human recognizable objects (such as dinosaurs or faces), if it was colorful, visually dense, or had a title, labels, and/or paragraphs.

Are these descriptions setting off alarm bells and making you scream internally? It’s probably because these characteristics are the exact design elements we’re taught to avoid. To further prove this point, Pfister shared that the least memorable visualizations were what we’d think of as more “Tufte-compliant.”

So the question on everyone’s minds: do we toss out the old guidelines in favor of brighter, busier visualizations? Not necessarily. Pfister shared that he believes the answer may lie in “something beyond [Tufte] that we haven’t explored that much.”

Data Storytelling

Pfister then brought up the ultra-new method of using comics to communicate data. Ultra-new because, as Pfister pointed out, there are few actually using comics to communicate data, there is no real definition of what a data comic actually is, and there are no real tools to create data comics.

A data comic, he explained, is communicating data in a way that comic books typically communicate stories. He explained that the four essentials for data comics were visualization flow, narration, words, and pictures, and demonstrated how all of these work together by displaying a data comic that showed the various power struggles that contributed to World War I.

It’s hard to do the comic justice by just talking about it, but to give you some idea of the effect it had on the audience, I would like to use one audience member’s own words: “It’s like a punch to the brain.”

Viewing the information in the form of a data comic was a faster and clearer way to communicate the information than any textbook could have done. It was evident from this example that data comics are more likely to play a larger role in the future, but, Pfister questioned, how will it fit into data storytelling overall?

Data Tools

The last subject Pfister hit on was data tools. He explained how the majority of popular data tools are relatively easy to use, but lack ability to customize visualizations easily. On the other side of the spectrum, however, are tools that are more expressive but lack ability to add insight. He argued that data scientists not only want but deserve better tools, and because of this there should be a product that falls somewhere in between Excel and InDesign.

The answer that Pfister and a team of individuals, in collaboration with Adobe, came up with was a program in which the user puts data into a spreadsheet, then uses guides that constrain the data to create a visualization. It was an interesting way of displaying data, but will it satisfy data scientists’ quest for the perfect tool? Only time will tell.

 

It was clear from Pfister’s lecture that more research needs to be done in all of these areas before we can truly say for sure what the best methods of communicating data are. It’s an exciting time to be in visualization, and we’re excited to see what the future brings. In the meantime though, check out our design principles for what we’ve found to be some pretty effective strategies for communicating data.

Want to build a data product? Don't take inspiration from an El Camino

You’ve connected your data highways, built the bridges, and now it’s time to take a ride. Do you have the right vehicle? Do you even have a vehicle?

Hopefully you’ve put some plan in place to extract value out of your information highway. If not, may God have mercy on your soul and the executive who decided to fund your bridge to nowhere. Most likely you know what it is you hope to get out of your “Big Data” investment, but there are a lot of unanswered questions.  

At this point you’re faced with what I like to call the “Chinese Buffet” of data analytics vendors. Do you really want to eat pizza, chicken tenders, and Kung Pao chicken all in one sitting? This infographic looks a lot like a “Big Data” buffet and proves how overwhelming the vendor selection can be:

   https://www.capgemini.com/blog/capping-it-off/2012/09/big-data-vendors-and-technologies-the-list

https://www.capgemini.com/blog/capping-it-off/2012/09/big-data-vendors-and-technologies-the-list

It’s no surprise why it’s so tempting to try to whittle the selection down to one vendor that does it all. You’ve convinced yourself that if you select one vendor, this decision will save money and eliminate the potential indigestion of integrating with multiple vendors from the Big Data buffet.

This can prove to be a fatal decision, especially when the solution you’re trying to build has a revenue target pinned to it. Some of you may already be familiar with the infamous Chevy El Camino. For those of you who aren’t, it’s the truck/car hybrid that’s about as appealing to look at as the mutant puppy/monkey/baby courtesy of last year’s Super Bowl commercials:

The El Camino was the ultimate utility play for people who wanted something that could haul like a truck yet still ride like a sedan. It was a one-size-fits-all solution for motorists, but unfortunately it was neither a great sedan nor a good truck.  

Let’s imagine for a minute that you’re in the construction industry and competing for a bid to deliver construction materials. The job is 1 mile off-road in the mountains and the prospective client asks what kind of vehicle you’ll be using to deliver the materials. You tell the client “I’ve got an El Camino, it’s a car with a truck bed!” Your competitor submits a bid and tells the client they’ll be using a F350 Super Duty V8 4x4. Who do you think wins that bid?

How does this relate to your problem? Let’s imagine now that you’re building a data product. Many vendors in the data analytics space bill their products as a one size fits all solution, like the El Camino.  Picking one vendor to do everything can leave you with an undersized and underperforming platform.  

For example, your client may have asked for both an executive dashboard and unbridled access to your data so their analysts can perform ad hoc analysis. You go out and find the most whiz bang drag and drop analyst friendly chart builder. It has 80+ visualizations (half of them are 2-D and the other half are 3-D) so your client can dig in and make all the 3-D pie charts they ever wanted. The vendor also claims to have an awesome dashboard solution.  You go to build your executive dashboard and it looks something like this:

   https://blog.rise.global/2015/10/28/the-5-big-design-decisions-you-need-to-make-when-creating-a-personal-dashboard/

https://blog.rise.global/2015/10/28/the-5-big-design-decisions-you-need-to-make-when-creating-a-personal-dashboard/

The vendor you selected gave you a great ad-hoc tool, but their data presentation/communication platform is seriously lacking. Your potential client takes one look at your platform and decides they only want to pay for access to your data at a fraction of what you hoped to charge for your product. You’re stuck in the mud with an El Camino full of data bricks.  

It’s worth noting that the executive dashboard is for executives and the ad-hoc tool is for analysts.  Last time I checked, executives were the ones who were responsible for cutting checks.  

It’s always important to pick the right vendors for any job. Don’t expect to find a one-size-fits-all tool in the Big Data space.  When building a data product, remember that the presentation of the meaning, flow, and story of your data is more important than any ad-hoc capabilities. If you fall short on effectively communicating the value of your solution, you may soon find yourself standing alone on that bridge to nowhere.

Need help finding the solution that best solves your data problems? Check out Juice's new tool, the Buyer's Guide to Analytics Solutions. 

Battery Meters and the Goldilocks Problem

"Actionable data." It is a phrase well on its way to becoming a cliché. But clichés are often founded in truth, and it's true that the essential quest in analytics is finding data that will guide people to useful actions.

Apple’s battery meter offers a lesson in the challenges in delivering such actionable data.

The battery meter on Apple's new Macbook Pro included an indicator of the estimated battery life remaining. If you’re sitting on an airplane hoping to watch a movie or finish your blog post, time remaining is a critical measure and a source of stress. But Apple faced a problem with presenting the time remaining value. According to The Verge, “it fluctuated wildly on Apple’s newest laptops...the ability of modern processors to ramp power up and down in response to different tasks made it harder to generate specific, steady estimates.”

Marco Arment put it in simpler terms: "Apple said the percentage is accurate, but because of the dynamic ways we use the computer, the time remaining indicator couldn’t accurately keep up with what users were doing. Everything we do on the MacBook affects battery life in different ways and not having an accurate indicator is confusing.” 

It's an issue of excess precision. Users want to know a precise time-remaining answer, but the fundamental nature of the machine results in a great deal of variance. I first heard about this problem from the excellent Accidental Tech Podcast. During the discussion, John Siracusa suggests an alternative to the problem: a burn-down chart like the kind used in agile software development. Android phones offer something that looks a lot like what he describes.

Siracusa admits that a more detailed visualization of this nature probably isn’t for everyone. It may work for him (and I like it a lot), but not everyone spends their days visualizing data.

It's a classic Goldilocks problem. Too little detail (and too much precision) can be deceptive and difficult for users to understand when the number jumps around. The lonely key metric without context can be inscrutable.

Too much detail, such as in the form of a full-fledged chart, may be more information than the average user wants to know. The predominant feature of the chart, the slope of the trend, isn’t fundamentally what the casual user cares about. They want to know if the battery is going to still have life when they are getting to the exciting final scene in their movie. Data visualizations should not be engineers serving engineers (as I noted when Logi Analytics asked that Fitbit embed a self-service business intelligence dashboard in their apps).

There is a third option available -- a "porridge that's just right." The alternative is to jump straight to solving the user’s problem while still using data. The data or metric itself isn’t the point; the user’s goal is the point. A better solution for Apple might look like this:

When it comes down to it, the problems Apple faces with its battery life estimates aren't so different from the problems we all face in delivering actionable data. The solution can be boiled down to a simple formula: Use the data to solve the problem. Keep the user informed. Give them a smart choice. 

And always have your charger handy, just in case.

Thirsty for more? Check out these related blog posts:

Book Review: "Bringing Numbers to Life"

"The effort was none other than to do the hard work of bridging numbers and people, and building that work into products that entice, even seduce, skeptical users to invest in an unfamiliar activity."

In his new book "Bringing Numbers to Life",  John Armitage sets out to create a design framework for analytical apps. It is a worthy subject and one that is close to home for our Juice team.

Mr. Armitage’s book has been published online and free-of-charge by the Interaction Design Foundation. The Foundation’s founder, Mads Soegaard, shared their vision: "We offer complete, unrestricted and free access to our chapters/books/textbooks in online versions. Everything we do is a labour of love and not a business model. We walk the talk of altruism.” The IDF hosts a wealth of free materials and textbooks, with contributions from top academics and professionals.

The book is a worthy subject delivered by a worthy organization. I was happy to share my thoughts.

"Bringing Numbers to Life" describes the how, what, and why of a “design-led innovation in visual analytics. The author is a lead designer at SAP (update: John Armitage is now head of UX design for Host Analytics), the software behemoth and vendor for such business intelligence products as BusinessObjects, Lumira, Roambi, and Crystal Reports. He was charged with creating a unified design model across these products to make them more effective in the communication of data. In his words:

“The result of this effort was a number of prototype projects that led to LAVA, a design language for visual analytic environments intended for broad application across the SAP product suite. The key driver behind LAVA was simplicity and low cost, which translated into some fundamental innovations that, with the backing of a large company like SAP, stand to improve the clarity and reach of visual analytic consumption in the workplace and beyond."

The analyst’s office is filled with books by Edward Tufte, Stephen Few, and Alberto Cairo providing guidance on how to visualize data. But what about the designer or developer of analytical solutions? Their challenge is in many ways more complex.

Armitage makes an important distinction between “Artisanal" and “Production" solutions. It is one thing to craft a one-off visualization solution for a specific purpose. In these cases, the author knows the data, the audience, and the specific message they want to convey. His challenge is to develop a system that can repeatedly deliver high quality data visualization and analytical tools. It is the difference between the carpenter who can craft a single table and the engineer who can create a factory that delivers a thousand tables. The requirements, skills, and frameworks are very different.

Armitage has researched this topic thoroughly. Over many years, he worked with internal SAP teams and consultants to define and refine his LAVA (Lightweight Applied Visual Analytics) framework. His framework describes important pieces for any analytical solution, and how these pieces should fit together. While he defines a specific nomenclature, the elements are universal building blocks. A few examples:

  • Charts can exist at different levels of detail and fidelity. When we want to represent a concept, it may appear as a single number or sparkling (“micro chart”) or a fully-labeled trend chart (“chart”) or as part of a multi-component, interactive visualization (“meta chart”).
  • Analytical tools need a hierarchy of components that allow a user to consider a broad concept, drill into more detailed topics, and explore specific data.
  • Modern analytical applications need to offer much more than the visualization of data. Features for sharing, collecting insights, and personalization are necessary to deliver a complete analytical tool.
  • The traditional single-page dashboard design is antiquated. "Scrolling effectively increases the virtual size of your display outside the borders of your window or device, and is a basic convention for digital content consumption that has been ignored in traditional dashboard design."

Armitage is also a critic of the increasingly complex and bloated analytical solutions — something I hear more and more from companies frustrated with visual analytics tools like Tableau.

"Currently, however, most visual analytic solutions reflect previous efforts to serve large high-paying enterprise customers, and are thus bloated with features designed for highly trained – and high-paying – specialists. As Clayton Christenson’s principle, and associated book titled The Innovator’s Dilemma tells us, companies in such high-margin businesses are beholden to serving their large customers, and thus leave the low-end of the business exposed to inroads by newcomers to the market. Visual analytic market leaders are facing such a dilemma today."

Despite my fondness for the topic and appreciation of Armitage’s evident research, the book has issues that I found hard to overlook.

Armitage indulges in lengthy tangents, personal biography, and an obsession with the specifics of the SAP landscape and politics — providing us with sentences like this:

“I even produced a farcical off-site video on the theme of multinational collaboration, and a comedy routine – based on the famous “Who’s on First?” from Abbot and Costello – to poke fun at working in multinational teams. I performed the latter live with Jay Xiong at our 2012 office holiday party in Shanghai."

He also appears to be fighting a public battle with the leadership of SAP to adopt his LAVA model in the pages of his book:

"Although LAVA, in particular the Lattice, pointed to similar effects for quantitative data, it was difficult for some people who were particularly close to BI to acknowledge its potential. The definitive objection from this faction was that LAVA “does not match Our metaphor”, which was of course precisely the point. LAVA is a new metaphor, and one that’s necessary for achieving SAP’s product aspirations."

I have a deeper concern with some of the recommendations for visual solutions. One of the features concepts is a "Lattice chart”. I found the example provided to be confusing and complex. Designing visualizations is an exercise in finding simplicity and accessibility for your audience. The image below is crammed full of information, but lacks the legend or space for a typical user to know what it all means.

Perhaps my biggest concern is with Armitage’s long-term vision. He expresses a desire to create an analytics world where human intervention isn’t necessary to communicate data effectively.

With this scalable framework in place, we can start to dream of efficiencies on a large scale, with entire Board Sets generated automatically from adequately-provisioned data warehouses. Filters, Panels, and Lattices can be determined with a rules engine automatically from combinations of Measures and Dimensions. Galleries can be populated by algorithm-generated charts derived from the most relevant Lattice Layers. Points can be created from data set indexing and data mining, and populated with major category representations and outliers. Multiple data sets can be organized into groups and presented as Board Sets, or one giant data set can be subdivided into Boards according to individual or sets of Measures or Dimensions, with these Boards further subdivided into Categories and Lattice stacks according to the depth and complexity of the data. These efficiencies will allow more people to use data to make decisions, and require fewer people to support them.

In my experience, data communication needs to start with an intimate knowledge of the business context, an appreciation for your audience, and an understanding of what it takes to make them more effective in their jobs. These aren’t things that can be scraped from a database.

Philosophical issues aside, this is a book about Armitage’s journey in trying to change how an organization delivers analytics, his process and research, and the quite-useful framework that came from his journey. As someone who has tackled the same design challenges in creating a new kind of tool for visualizing data, I appreciated being able to get an up-close view of how another professional wrestled with common challenges.

In describing his conclusions, John Armitage notes, "During LAVA’s development, I found myself surprised that nobody had before arrived at our fairly simple and basic conclusions."

Don’t worry, John, you’re not alone.

Communicating Economic Progress with Visual Analytics

Earlier this year, we worked with The Virginia Chamber of Commerce to help bring their vision for a new data application focusing on economic performance in Virginia to life. They wanted something that would fit within their overall website and serve reliable, timely data that’s easy for everyone to use and easy to share. With insights around performance, transportation and trade, innovation and entrepreneurship and more, their new application allows everyone to find useful and actionable insights within the data. A couple weeks ago, we had a chance to speak with Barry Duval, President and CEO of the Virginia Chamber of Commerce, to get his insight on the project, how it went and what the feedback has been so far. 

1. Tell us a little about the Virginia Chamber’s mission and how you communicate with constituents.

Put simply, we advocate and we communicate. We are the voice of Virginia business. The Virginia Chamber is the largest business association in the Commonwealth, with more than 25,000 members. Our mission is to be the leading non-partisan business advocacy organization that works in the legislative, regulatory, civic and judicial arenas at the state and federal level to be a force for long-term economic growth in the Commonwealth. We communicate with public policy makers, members of the media, and the general public through personal relationships developed over years, traditional earned media, social media, paid media, and widely attended events focused on issues of importance to the business community.

2. What was the problem you were trying to address when you set out to create this application?

Whenever you are advocating for a policy change or demonstrating a problem that needs to be addressed, you need to have trustworthy data to support your position. There are many metrics to measure Virginia’s progress in various policy areas that contribute to economic growth, but public policy makers and business leaders alike have asked us for a single place they can go for reliable, timely data that’s easy to digest and compare with other states and between jurisdictions in Virginia.

3. What were you doing previously to solve that problem? What parts of that approach were not working?

We give presentations around Virginia that visualize certain important economic metrics, and often have requests to receive the PowerPoint presentation for future use and adaptation to a group’s or individual’s needs. We communicate issues such as Virginia’s recent drops in national business and legal climate rankings through op eds, press releases, and traditional media interviews. However, none of those efforts give interested individuals easy access to use the data that our staff is able to compile for their own purposes

4. What appealed to you about working with Juice and Juicebox as a solution?

We saw the work that Juice and Juicebox did for the US Chamber of Commerce on their dashboard, and use it as a frequent resource in the course of our work. They came highly recommended for their responsiveness and expertise in this this area. We wanted to work with a company who not only had the visual and graphic design expertise to create our dashboard, but also the economic understanding to understand how to present complex data in a way that’s easily understandable and simple to navigate.

5. What kind of feedback have you gotten on the application so far and from whom? How has it impacted your conversations with members and stakeholders?

We have not yet launched the dashboard, but when presenting screenshots of the application to executives from other chambers of commerce in Virginia and to the members of our board of directors, the response has been overwhelmingly positive. Local and regional chambers have been particularly interested in the ability to compare certain metrics at the regional level. We anticipate a public rollout and demonstration December 2nd at our annual economic summit in Williamsburg.

6. How do you feel this application represents the Virginia Chamber compared to the sites that other chambers have?

We launched a new website last winter to make it easier for our members to access Chamber resources and to reflect a more contemporary feel. The application produced by Juice fits hand in glove with the goal of our website to be useful, intuitive, and attractive for members, potential future members, and for public policy makers to find valuable, trusted information.

7. Have you learned anything interesting through exploring the new application?

The feedback from Juice as we have gone through this process has led us to rethink the way we use some of our data and led us to include new metrics that we otherwise may have overlooked. It has not been simply a process of beautifying the data we were using, but has given us new insight into how we measure progress in the Virginia economy, which benefits our members and adds value to all of the other communication outreach that we do as a Chamber. 

 

To learn more about data products and visual analytics solutions, get in touch! We'd love to chat!

10 Differences between Customer Reporting and Data Products

We’ve been talking a lot about how innovative companies are realizing the need to enhance their solutions with more customer-facing data products. For example, GoToMeeting launched a new feature called “Insights” where they send you engagement summary information from your meetings. Here is one from a recent Juice Lunch & Learn:

DJ Patil (U.S. Chief Data Scientist) defines data products as "a product that facilitates an end goal through the use of data.” I’ve described data products as turning analytics inside-out to deliver value to your customers. But the question we most often get is: How are data products different from the customer reporting we already provide? 

Here are ten important differences between customer reporting and data products: 

1. Instead of summarizing data, solve a problem. Most reporting simply regurgitates data in a semi-aggregated format. A data product starts with customer’s pain points and asks how data can bring insight and better decisions.

2. Instead of starting from the data, start from the customer. Report writers will often look at the data available to them and ask "How can we deliver all this information?" That’s what gets you to self-service analytics tools sitting on top of data. Not good. Data products need to start by asking how you can make your customers smarter and more effective in their job.

3. Instead of stopping at showing the data, guide users to specific actions. Customer reporting may be satisfied with making data accessible. Data products need to do more — they need to move people to take action. Start from the end point: what kinds of things do you want your users to do? How will you give them the right information?

4. Instead of focusing on metric values, deliver context for decisions. Key metrics are only as good as the context you put around them. Data products wrap context around metrics with goals, benchmarks, comparison, and trends. Then your users will know how they should react to the numbers they are seeing.

5. Instead of passive objectivity, bake-in best practices, predictive models, and/or recommendations. “Let the data speak for itself” — that’s like a chef saying: let the diners enjoy the raw ingredients. Bring your expertise to the data product. Your customer knows their pain — but you know the data and what can be done with it.

6. Instead of trying to show more data, reduced to only the data needed. When it comes to presenting information, more data is seldom better. Customer reporting only expands — into dozens of dashboards or reports. Data products should strive for less.

7. Instead of putting the burden on users to figure it out, strive to reduce burden. Customer reporting tosses responsibility to the customer, effectively saying "you figure out what’s important to you.” Data products recognize that few people inherently enjoy messing with data; most people just want to be better at what they do. The data can facilitate that goal.

8. Instead of being designed for analysts, data products are designed for decision makers. Many customer reporting solutions assume the end-user wants to dig in and analyze the data. Data products are for a different audience: front-line decision-makers. These people are busy with their regular job and have little interest in learning something new.

9. Instead of “show me the data”, strive to make the data invisible. The best data products of the future will make the data invisible. Consider how Google Search tries to predict your need and point you to the best answer at the top of your search results. Google wants to hide the data (search results) and jump straight to the answer.

10. Instead of a cost-center for your business, become a profit center and differentiator. Customer reporting is considered a necessary evil for many companies. For example, we’ve had dozens of conversations with advertising agencies who feel compelled to provide reporting, but clearly don’t relish the task. In contrast, companies that view their customer data as an asset recognize that they can create new revenue streams.

That’s where Juicebox comes in — it is the quickest, best path to turn your data into differentiated, revenue-generating data products.

More Analytics Isn’t Always Better

LogiAnalytics sells a self-service business intelligence solution, and they’d probably like to see Fitbit using it. Their sales pitch comes in the form of a blog post ("Why Fitbit Product Managers should be focused on Analytics") critiquing Fitbit for not giving its users a more full-featured, self-service analytics dashboard as part of the fitness product.

The post starts by lauding Fitbit as an innovator, but quickly pivots to suggest Fitbit hasn’t “kept pace” in the realm of self-service data capabilities. Fitbit’s simple dashboard "may have been acceptable some years ago but not anymore."

The evidence: someone pulled data out of Fitbit and visualized it in a different dashboard tool. Never mind that the someone is a demonstration project by another dashboard tool. The LogiAnalytics author concludes that this is proof that "Fitbit doesn’t meet user needs anymore and provides a workaround for customers to export data to another platform for improved self-service analysis.” The post goes on to paint a doomsday scenario for Fitbit: "Instead of instantiating itself into the daily lives of users, it is separating itself out and losing the user by becoming a device that measures steps (and other things)."

It’s quite a leap based on some questionable assumptions. My guess is that more self-service BI in a consumer device probably isn’t what Fitbit product managers should be concerned with. Here’s an alternative set of lessons for companies looking to integrate analytics into their products: 

  1. The plural of anecdote isn’t data. When you find one example of an analysis approach by your customers, don’t feel compelled to add it to your next product release. A better approach: find out what is the pain that is motivating the customer, then talk to other customers to see whether that pain is common.
  2. Know your user segments. I’d love to hear from Fitbit about how they’ve thought about user segments and meeting analytical needs. Given their success, I expect they’ve done their research. At Juice we recently segmented our Juicebox app users into five analytical user segments: Explorers, Light Explorers, Number Checkers, Table Downloaders, and Freshman.
  3. Apps, not busy dashboards. Fitbit has clearly emphasized their mobile app as the primary mechanism for interacting with fitness data. The app interface shows how they’ve thought about making data useful. The data is delivered in service to different user needs: weight loss, competition with friends, step activity, sleep tracking. I wrote about the post-dashboard world here.
  4. Don’t make consumers feel like they are at work. The idea of baking a self-service BI solution into Fitbit feels like a mismatch in expectations. As a consumer, I want simple, easy, fun, and direct. I wouldn't use those words for any self-service business intelligence solutions, regardless of how important they might be in a particular business context.
  5. Hackers want hacking tools. Fitbit created a well-documented API that gives technically-savvy users the ability to pull their data out, combine it with other data sources, and present it any way they like. That’s the ultimate flexibility — not a dashboard with extra nobs or trend lines.

To be fair, Logi offers some legitimate points:

  • "Through our extensive work in BI and analytics, Logi has found that when users get access to data, over time they begin to want access to more data and want to be able to do more with it."
  • "...for smart companies that seek to keep an ongoing relationship with the user know the philosophy of 'don’t lose the user' is critical to maintaining hardware revenue..."

To give LogiAnalytics some credit, they do point out that once users get access to data, the users will always ask more and better questions. Another important message: analytics on top of a device is a good way to enhance and solidify customer relationships. But choosing the right way to deliver that data is the challenge, one that Fitbit seems to have taken head-on.

Data Discussion Etiquette from Brad Pitt

Before Matt Damon impersonates an investigator in Ocean’s Eleven, Brad Pitt’s character delivers a little pep talk. 

Watch the 40 sec clip:

Rusty Ryan (Brad Pitt) explains the rules of undercover conversation to Linus (Matt Damon). From: Ocean's Eleven (2001)

Now watch it again, but this time imagine yourself giving a pep talk to the next email, powerpoint slide, or dashboard finding that you are about to send out. 

Presumably your data is not meant to distort, yet we can mine the advice here for a few practical communication tips to improve data-informed discussions.  

Let’s break down the key moments.

Be natural.

[Damon takes an unnatural, stiff stance] “No good. Don’t touch your tie. Look at me.”

 

 

His first posture is fidgety and self-conscious with an overly professional stance. 

First impressions are holistic and endure when it comes to perceived levels of interest and credibility. Most of us have an uncanny ability to sniff out a fake, and how data enters discussion is no exception. We’re not computers, so we don’t enjoy an overwhelming data dump of facts, findings, and insights. Two paragraphs and 15 slides in everyone wonders, “Where is this going? What’s the point?” Messages must be clear and focused, but should aim to jettison the unnatural, mechanical chart headings and the unnecessarily encrypted statistical speak. 

Be honest.

“I ask you a question. You have to think of the answer. Where do you look? No good. You look down; they know you’re lying. And up; they know you don’t know the truth.”

 

Be honest with what you do and do not know and what data you do and do not have. Your audience expects to have certain questions answered in order to take your information seriously. Your audience wants to both hear and understand answers to questions like these:

  • How do I know I can trust this data? How was it collected and who was involved?
  • How exactly is this metric calculated?
  • I see the number is X, but how do I know whether that is good or bad? 
  • What’s the history of this number and the frequency of its collection? How quickly does this number usually change? How long does it typically take to influence it in the future? 
  • How does this compare to other locations with similar attributes?
  • Why is this useful for me to know? How will it change what I care about?

These questions aren’t novel. They follow the 5W’s basics. Yet they are often either left out or overcomplicated in most data discussions. The goal here is to acknowledge these needs in the simplest, most useful way.

Start with a (very) short story.

“Don’t use 7 words when 4 will do.”

 

 

 

With data, as with words, precision is as much an art as a science. Still, helpful tools exist. Ann Gibson wrote a relevant post and I highly recommend reading the article for all the details, but here’s the magical excerpt:

Once upon a time, there was a [main character] living in [this situation] who [had this problem]. [Some person] knows of this need and sends the [main character] out to [complete these steps]. They [do things] but it’s really hard because [insert challenges]. They overcome [list of challenges], and everyone lives happily ever after.

The beauty of this frame narrative is that it provides a structure for those who are too long-winded to focus on the essence of their own message, and it helps others whose ideas tend to dart all over the place to preserve a sequential flow.

Each of these [placeholders] are candidates for data context that help satisfy the previous "Be Honest" section. I mocked up a quick scenario that demonstrates a short story with useful data context:

Set your mark.

“Don’t shift your weight. Look always at your mark but don’t stare.”

 

 

You’ve likely heard of S.M.A.R.T. goals before, but are your charts smart? Something as simple as a target value by a specific date on a chart can work wonders at moving towards something tangible. People crave purpose, so set and communicate your goals. But don’t be that presenter who stares incessantly at your metrics and goals. 

Be enjoyably useful.

“Be specific, but not memorable. Be funny, but don’t make him laugh. He’s got to like you; then forget you the moment he’s left your sight.”

 

 

Jazz it up,” “Make it shine,” and “Make it pretty” are all phrases you’ve either heard or used yourself. Few situations are more disappointing then when a company tries to overcompensate with their insufficient, irrelevant data by lathering on the “wow factor.” Don’t succumb to making your data memorable for the wrong reasons. For business the goal isn’t memorable chart-junk, but that does not mean your data should be lifeless and shallow.

Don’t leave people hanging.

And for God’s sake whatever you do, don’t, under any circumstances…”

 

 

The worst move you can make is to omit the call to action. End with clear next steps, key questions posed, or an action button that allows your audience to engage with immediacy, while your solid ideas are fresh and ripe for action.

Thirsty for more? Check out these related blog posts: