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.

Finding Insights on Turnover in HR Data

Turnover is a major problem in healthcare, and the costs associated with re-hiring and training new staff can be in the tens of thousands of dollars per person, depending on the position. Turnover costs hospitals and hospital systems millions of dollars each year. Usually each hospital or group of hospitals has an idea what their turnover percentage is, but they may not be drilling into the data to find insights into why turnover is occurring.

We found some commonalities in the reasons for this lack of connecting the dots in regards to turnover. First, there are often multiple dashboards with conflicting information. This causes problems across an organization as managers are working from conflicting numbers. Second, the data is living in different places and this costs time and money as leaders have to go to multiple sources to get their information. Lastly is the lack of ability to tie the turnover back to specific departments and supervisors with certainty.

What we discovered is that many HR professionals in healthcare are looking to their HRIS systems to provide answers to their turnover problems. The problem is that these systems are not designed with that purpose in mind. They are a place to store data and access pieces of it when needed, but they are not designed to connect the dots between the different pieces in the system.

In a recent conversation with the Chief Human Resources Officer (CHRO) of a health system, I was struck by the way he took responsibility for the turnover issues he was facing in his facilities. Turnover is something personal for him - he looks at his 4,500 employees as his direct responsibility, and as such takes it upon himself to ensure that he is hiring and retaining a workforce that knows they are valued.

My CHRO friend is one of the first in his field to take the data in their HRIS system and work to do something creative with it. They have been piloting Juice’s new product, Blueprint, for the last 10 weeks and have begun to find a correlation between turnover and supervisors in their organization. He told us that he has never before had the ability to, in one place, find turnover per supervisor and per department in such an accessible manner.

Blueprint is built like a funnel: you choose a metric at the top level that summarizes some information across all of your staff allowing for an enterprise view of that metric. You are then able to follow a path as you drill down to an actionable list. For instance this CHRO was interested in understanding why a certain subset of his 4,500 staff members have remained there for longer than 10 years. He was able to turn a list of 4,500 staff into a list of 40. Previously he would have needed to go through two or three layers to get this data, and now he can find it in a matter of minutes. Now he has the information he needs to figure out what causes employee turnover in his organization and can take actionable steps to decrease turnover in the future.

As this organization works to get a handle on their turnover problem, Blueprint has proven to be an invaluable asset. It has allowed the organization to quickly and efficiently find the necessary data and take action on it. If you're interested in learning more about Blueprint, send us an email at or set up some time to talk-one-on-one.


Panel: Data is the Bacon of Business

Last month we attended the Nashville Analytics Summit, where our CEO Zach Gemignani made the claim that "Data is the bacon of business" and presented on the subject "Launching Data Products for Fun and Profit". After receiving an outpouring of questions about the presentation and creating data products, we've put together a happy hour and panel discussion for data product (and bacon) enthusiasts to get together and learn more.

Join us on Wednesday, September 21 at the Tech Tavern in Nashville for a happy hour and panel discussion on turning your data into profitable products. It'll be a great opportunity to go more in-depth on the world of data products, as well as a chance to ask questions and discuss the challenges facing organizations building data products today. Zach will be joined on our panel of experts by:

Damian Mingle, Chief Data Scientist at WPC Healthcare
Christian Oliver, Vice President of Data Products and Product Management at HealthStream
Chris Crenshaw, Vice President of Strategic Development at STR

We hope that you'll be able to join us for what promises to be an exciting evening of appetizers, drinks and good discussion. If you're interested in attending, check out our Eventbrite page and make sure to register (so that we know how many pounds of bacon to order). 



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:

Data is the Bacon of Business: Lessons on Launching Data Products

Last week was the 4th annual Nashville Analytics Summit. The event has grown from 150 participants three years ago to 470 this year. I took the opportunity within this friendly analytics community to share our latest thinking at Juice. Last year I spoke about "Beyond Data Visualization: What's Next in Communicating with Data”. This year my talk was entitled “Launching Data Products for Fun & Profit”. I started with a simple premise: Data is the bacon of business. I’ll let Jim Gaffigan explain:

His logic works for data, too.

We've had a front-row seat as our clients have transformed their data assets into revenue-generating data businesses. But launching successful data products isn't simple. And it is a far cry from your typical reporting or self-serve BI solutions — the insight-free data delivery vehicles of the past. I’ve posted the slides from my talk here:

Here are a few highlights:

  • Data products are happening now. Big technology companies are making massive investments in pursuit of better data sources for their products. IBM spent billions for The Weather Channel to enhance Watson Analytics. Google bought Waze for crowd-sourced traffic data. Microsoft wanted LinkedIn’s “economic graph” so badly they spent $26 billion.
  • The best data product stories start with a visionary leader. Our clients aren’t just thinking about fancier visualizations. They want to transform their businesses by making their customers smarter and more successful through data.
  • My friend Oli Hayward of Hall & Partners provided some valuable lessons from launching a world-class market research analysis portal. He explained the need to start by selling to internal audiences and targeting only the most innovative clients (we’re in the same boat there).
  • Data is an imperfect reflection of reality. When you present data to customers, prepare to discover exactly how imperfect it is. Which led me to this joke...

If you’d like to hear more about our lessons learned from dozens of data product launches, send us a note at

The Jury's In: Findings from User Research

We made it our goal this summer to hear back from prospective users of our research application about how they would use the app to address various hypothetical issues in their day-to-day workflow. After asking a couple thousand departmental leaders to put themselves in situations that would lead them to use our app to address a need, we presented them with three different scenarios, ranging from grant proposal preparation to tenure decisions. We got some very interesting responses that we believe are applicable to how people use all different types of data products and reporting solutions. Here are our findings.

Benchmarks and Discussions - Specific to the research app, we found that when department heads go to write a grant proposal, they prefer to communicate with peers and use their peers' previously successful grant proposals as a benchmark of the quality that a particular sponsor expects from a proposal. 

Similarly, users of our Healthcare app also connect with their coworkers about training assessment and work performance. They too use their peers' experiences and expertise as a barometer for their own performance in training and in their work. Our chat feature that's built into Juicebox applications does a great job of facilitating discussions right in the app, so you can highlight metrics, share them, and start a conversation

Our chat feature in action

Our chat feature in action

Performance Measurement - Specific to the research app, we found that department heads take their faculty's research activity very seriously. In fact, they consider a faculty member's research activity to have a greater influence on their promotion and tenure decision than teaching evaluations, service, and the opinions of other faculty members in their department.

At Juice, we are no stranger to performance metrics. Managers in all types of industries use our apps to measure the performance of their employees for promotion decisions and general review purposes. We take measuring performance to the next level by giving our users seemingly unlimited ways to filter the data.

An example of research performance measurement

An example of research performance measurement

By listening to the needs and preferences of our users, we've created our apps to enable users to analyze peer performance within their institution and communicate with each other seamlessly. This takes the guesswork out of with whom to consult and what to seek from those data-enabled conversations. To get a taste of how you can get rich insights out of Juicebox, check out a quick demonstration of our research application or schedule a demo.  

Office of Research Application Preview

Imagine you're a researcher at a top university. In addition to conducting innovative projects, it's your job to work with research administrators to create proposals and receive funding. But how do you go about finding sponsors?

Our Juicebox Office of Research Applications removes the guesswork and makes it easy for researchers and administrators to communicate and successfully find sponsors and create grant proposals. Watch the video below for a quick taste of exactly how it works - from quickly sorting through information and making selections, to communicating with co-workers within the app.

Thirsty for more information? Send us your questions at or for a more in-depth look schedule a personalized demonstration.

A look at our latest visualization

At Juice, we recognize the importance of design and visualization in making you successful with your data. In fact, it's the design and functionality of visualizations that bring your data to life so we are always working on new and exciting ways for people to explore data and gain deeper insights. 

A common desire when examining data is an eagerness to dive deeper. Simply knowing the answer to a question isn't always enough - sometimes you want to know the ins and outs of "why". Take a metric for example. Knowing your sales number is great, but context is equally as important. Is that number higher or lower than last month? Where did the sales come from? Is there potential for growth with new customers? 

For example: when I go to Google Maps, I am usually looking for a good place to grab a meal, find a friend’s house, or maybe a local park to take my daughter to. Once I have located where I want to go, I usually zoom in to see what area of town it is in. After I get an idea for where it is generally located I’ll usually want to go deeper to see if I am familiar with that area of town. Lastly, and this may just be me, I switch to street view so that I can see what the area looks like, occasionally you will see individual people walking on the street, running, or maybe eating on a patio somewhere. The idea behind Google Maps is that you can see clearly from any level; from 20,000 all the way down to 20 feet.

At Juice, we wanted to mimic the behavior of diving deeper with our new visualization. It's appropriately named "Bubbles" and is a visual way to get an enterprise view of a large set of data - staffing data, in this case. If you are a leader of a large organization, we have created a way for you to - like a Google Map - get an enterprise view of your organization with the unique ability to drill into different departments, supervisors and individual employees. Interested in understanding the reporting relationships at a deeper level in your organization? This visualization can walk you through these relationships to discover hotspots where your organization can optimize the workforce.

We are passionate about helping businesses discover new insights in their data in creative ways and this is just one of the latest features. For more on our product and all that it offers, get in touch with us. We'd love to have a conversation about how to help you move your business forward.

Choosing the Right Proposal Measure

Folks in the research administration community are talking more and more about data management and reporting at their respective universities. When we talk about data, we also need to talk about metrics. Tracey Robertson, the Director of Sponsored Research Accounting at Princeton University tells us that choosing the correct metric can:

  1. Change behavior
  2. Drive performance
  3. Support investments

Failing to choose the right metric to present research activity data will not only confuse people, but will also lead to missed opportunities and a failure to answer important questions that researchers and campus leaders may have.

A couple of years ago we wrote an article about using the right metric for your data presentations, and people really loved it. It’s summarized by this diagram:

However, we wanted to make it “real” for our research community so we decided to give some more insight on how we used these concepts to design our office of research reporting application. Here’s what we came up with.


To make a metric actionable, start by making sure it accurately addresses a real question or need. If your goal is to create a report on how successful a college or department is in getting funding for their proposals, your report would be lacking if you only included number of awards received in this performance metric. Why? Because this metric alone does not adequately capture proposal success. Including the number of proposals submitted as a reference to the number of awards granted captures the performance metric and accurately addresses the need. Here are the metrics we selected:

To enhance the actionability of these metrics, we also added the change from the previous month for each metric. In this example, for instance, the number of proposals was down 157 from the prior month. This gives the users some insight into context and hotspots for follow up action.

Additionally, when a user selects a metric, other information on the page (such as trend over time, or breakout by sponsor) is updated to reflect more detail on that selection. Interesting detail means action.

Common Interpretation

Your metric should be one that everyone can easily understand without much thought. Keep in mind that some (if not most) of the people to whom you are reporting your school’s funding data are not analytical experts. Think layman's terms here.

In the Research app, we made sure the labels of the metrics were simple, common and easily understandable. The labels “Proposals” and “Proposal Dollars” clearly represent what they mean and are common to the lexicon of our targeted users.

Additionally, we wanted to make sure there is a delineation between proposal and award metics by separating the key metrics into two representative rows, using the gestalt rules of association to connect the related metrics.

Accessible, Credible Data

A good metric is one that should be easily accessible and tenable. Many schools run into the issue of being able to track down and organize the data for their grant funding activity reporting.  

The platform that we used to create our research application (i.e., Juicebox™) is based on the premise of accessibility. But the credibility factor is tied to the data. Make sure that the data that you use to calculate your metrics is well understood and comes from a respected source. A good litmus test is to ask the question to your users: “If you wanted to know the number of awards, where would you look to figure that out?” Your data source selection means more if people confirm your source as one they’re already trusting for their work.

Transparent, Simple Calculation

When an administrator, dean, or professor looks at the reported metrics, they should be able to recognize how your team reached that value and what it represents. If they cannot decipher how it was calculated you lose credibility and gain confusion.

The metrics we selected for our research application are what we call “simple metrics” in that they are not complex assemblies of multiple metrics (otherwise known as composites, indexes, or franken-measures.) But to make sure the selected metrics are as simple as possible we narrowed them down to core concepts that people understand: the number of proposals and awards, and the dollars associated with proposals, awards and expenses — concepts most anyone in the research world readily understand.

Want to see more about useful proposal metrics?

We’ve taken the principles illustrated in this article and beyond and have applied them to our own product that can deliver accessible and actionable data insights to anyone who uses it. Check out the demo video.

Video: Turning Data into Dollars

When someone says the words "company data", what comes to mind? If you're like most people, you probably picture endless spreadsheets, stacks of reports, and multiple analyses all used to make decisions within your organization. What if you were able to take that same data and reach external audiences, while generating profit as you did?

Data monetization, or the process of turning your data into dollars, is doing just that. It can be difficult knowing where to start, so we've laid out some of the best practices on building data products and monetizing your data. Watch the video below (or download the slides) to learn the four main steps to data monetization, how to communicate with different audiences, familiar examples of data products, and much more.

Want to know more? Check out some of the success Juice has had in the past building data products, or send any questions to us at