Driving Healthcare Data Culture Forward

Last week, Juice Analytics participated in the Health 2.0 Atlanta panel, a co-hosted event by the Data Science and BI Society of Atlanta and Health 2.0 Atlanta. The focus was on analytics and healthcare and it was a great event. There was so much interest, they had to move the event to a larger venue! That tells me two things - (1) people want to get more out of their data and (2) Healthcare is behind and they really want to catch up. Two of my favorite “tweetables” of the night, said by Jason Williams, VP of Analytics and Strategy at McKesson, backed up those assumptions.

Getting more out of your data

The first “tweetable”  was something we see at Juice all the time:, “Nobody wants analytics, people want answers.” This relates back to people wanting more out of their data. Right now many people simply have data - and that’s it. But people want more than just a bunch of charts and numbers on a screen, they want insight. They want to be told where the problem is and given insight into how to fix it. If you’re simply delivering data either in a spreadsheet or just a series of charts, you’ve missed the mark. And for the record, this problem isn’t specific to healthcare. It’s all over.

Catching up in Healthcare and the path forward

My other favorite “tweetable”, originally said by W. Edwards Deming, was “In God we trust; all others bring data.” To get buy-in on a problem and solution, you need the data to support your position. The problem is that not everyone is ready to embrace data. As the quote alludes to, it’s all fine and well to think or believe you know the answer, but data helps you actually know the answer. Sure there can be a human element involved, but being informed with data to back up decisions is useful and important. In order to move data in healthcare forward, there needs to be a culture around data. It needs to be ingrained in an organization as useful and be included in everyday conversation.  

Embracing a data culture in healthcare will become even more important as we move into the future of what healthcare could look like. Much like Google Maps on your phone adjusts your course based on a wrong turn or an accident on the highway, it was said that healthcare will begin to use data in much the same way. Healthcare data should and will move in the direction of being event driven and using data to adjust as things are happening, rather than being reactionary. I don’t know about you, but that sounds exciting and full of promise! But to get there, you first need a good data culture.

The event was not only a great success, it was insightful - which is what we love! It would seem that to begin to move your healthcare organization forward, there are two things to focus on. One would be providing insight, not just data. The other is to promote a culture of data that is widely adopted within the organization. Without that, having insight won’t matter since nobody will want to use it.

To learn more about creating a data culture in your organization, check out Data Fluency: Empowering Your Organization with Effective Data Communication, written by Juice Analytics founders Zach and Chris Gemignani.

To learn more about how we help our healthcare clients provide data insights and succeed, check out our case studies or get in touch.


How to Build Better Data Products, Part 2: Development

This is the second in a multi-part series on launching successful data products. At Juice, we’ve helped our clients launch dozens of data products that generate new revenue streams, differentiate their solutions in the market, and build stronger customer relationships. Along the way, we’ve learned a lot about what works and doesn’t. In this series I’ll take you through what you need to know to design, build, launch, sell, and support a data product. Read Part 1 of the series here.

If “Data is the Bacon of Business” (TM), then customer reporting is the Wendy’s Baconator. Sure it contains bacon, but nobody is particularly happy with themselves after eating it.

In a recent blog post, we described the differences between customer reporting and data products. Those differences result in some very different functionality requirements. In particular, data products require more C.L.I.C. D.R.A.G.

  • Context — Benchmarks, comparisons, trends, and/or goals that encourage decision making.
  • Learn — Help and support features to train users to get value from the information.
  • Integration — Connections with other software systems to integrate with data and enable operational actions.
  • Collaboration — The ability to save insights and communicate them with other people. Decisions aren’t made on an island.
  • Documentation — Because data products live on and touch many people within your organization.
  • Reporting — To track usage of the data product.
  • Administration — Features to manage users and control permissions.
  • Guidance — To point users to the most effective ways to explore and understand the data.

This collection of capabilities gives some indication of the gap between your standard customer-facing reporting and a complete data product. To accomplish all of these, you’ll need more than a talented BI report writer and access to your database. In our experience, the recipe for building a successful data product is dependent on a number of specialized roles.

Product Manager

The Product Manager sets the vision of the product. He gathers the necessary resources to make the team successful, and builds organizational support for the product.


UI/UX Designer

The UI/UX Designer understands the user’s workflow and how to best guide the user to decisions. She crafts the interface and interactions to make the data intuitive. She's also in charge of design application styling and all visual elements.

Business Analyst

The Business Analyst translates application design into technical and data requirements. She's responsible for documenting business logic as product decisions are made.


Front-end Application Developer

The Front-end Application Developer's role is all about building interface elements, interactions, and data visualizations.

Back-end Application Developer

Data Guru

The Back-end Application Developer does everything the Front-end Developer does, only backwards. Just kidding! But he does build the application server environment an define data queries to support UI interactions.

Data Scientist

In addition to having the coolest title, he provides access to raw data sources. He understands and communicates the meaning of data fields and calculations to the development team.

Technical Architect

The Data Scientist defines the questions that will help end-users make better decisions. She enhances data through predictive modeling and other advanced data analytics techniques.

Quality Assurance Engineer

He's the general technical architecture of the product, responsible for figuring out how the application connects to data sources and integrates into other systems.

The Quality Assurance Engineer evaluates whether the data product meets the need and requirements set out in the design process. He also tests data accuracy and product functionality.

It's a big load. That’s why you might want some help before going at it alone. At Juice, we've built a technology solution and an expert team that fills out many of these requirements. We have a set of visualization components and interactive features that ensures your application is a first-class user experience. Combined with our experienced design and implementation teams, we’ve got many of the resources covered. Our clients bring the product vision; we make it happen.

Our goal at Juice is to streamline the data product launch process so you can launch innovative data products in weeks, not months. Want to know more? Let us give you a demo.

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.

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 info@juiceanalytics.com 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 info@juiceanalytics.com.

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.