Data-Driven Decisions

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:

Leveraging Data to Generate Value

Lydia Jones is a business and legal data monetization strategist and adjunct professor of law at Vanderbilt Law School and Boston University School of Law. Lydia and her consultancy firm InSage LLC are the 2017 producers of The Data Monetization Workshop, an annual event that brings together industry leaders and data monetization innovators to discuss data-centric opportunities, address perceived challenges, and transform the C-Suite conversation.  Learn more and register for the Workshop here.

You have defined data monetization as "leveraging data to generate value." I'd like to explore more about what you've learned as you talk to companies looking to get value from their data.

The role of Chief Analytics Officer or Chief Data Officer has become increasingly common as organizations try to focus their efforts on data monetization. From your experience, what kinds of companies have pursued this strategy and established this data leadership role? That is, what conditions need to be in place for a company to pursue this type of innovation?
I look at the data-centric ecosystems that exist in the private sector as broadly as possible, and that includes looking at how private companies are working with local governments to leverage data and to generate value for public goals as well. So to start, companies willing to learn from,  or collaborate with, entities outside of their core industry is one condition. Another condition includes a willingness to view data as a business opportunity rather than as just a cost item in the CTO’s budget. Once data and data-centric revenue are seen as crucial parts of business operations, focusing on data opportunities analysis is key, and having the right skill set for that task is even more so. The shift from data as an anchor to data as an opportunity typically moves a company to consider whether, and when, to create a Chief Analytics Officer or Chief Data Officer position to address opportunities, to leverage data, and to generate value for the company, its partners, and its customers.

What kinds of distractions have you seen that may give a company pause in either creating a data leadership role or pursuing innovative data-centric monetization thinking?
A change in perspective about opportunities that may arise from data collection, data sharing, and data analytics, or from the creation of a new position such as a Chief Analytics Officer of Chief Data Officer, is not enough to fully leverage data monetization initiatives. A company must also change the internal conversation about perceived risks concerning data value and data monetization. Many companies misperceive risks, such as privacy risks, when valuing data and when assessing whether to engage in innovative data monetization initiatives. Companies that have opportunities to leverage data must resist being swayed by misperceptions about privacy. For example, many companies have a blanket rule against monetizing personally identifiable information. While this may be the prudent choice under some circumstances, many companies adopt this position as an automatic universal rule because of a perceived, but oftentimes unfounded, risk concerning privacy. For example, data monetization opportunities arising from the collection and sharing of personally identifiable wellness data are routinely rejected under the perception that privacy rules – such as those found in the HIPAA Privacy Rule applicable to personally identifiable health data – prevent the monetization of wellness data when in fact those rules usually don’t.

This kind of thinking based on misperceived privacy risks undermines innovation. One need only look at the popularity of mobile applications through which consumers pay to give real-time generated personal data –  from retail purchases to geolocation data to biometric data – to companies in exchange for data analytics and insights. When consumers are demanding highly personalized products and experiences, it becomes a necessity for a company to consider, or to reconsider, how to best generate value from personal data. Yet for those companies that misperceive the risks associated with data monetization, these opportunities are left to competitors.

Can you share a few examples of the kinds of entities that you think are doing innovative things with data and data monetization?
Innovative data-centered projects span across the corporate sector, the nonprofit sector, and the local government sector.  Big Data Quality recently published Big Data 50 -- Companies Driving Innovation
, which is worth checking out here. And in the nonprofit sector, data scientists are moving into the role of the chief data officer. DoSomething.org is an example of a nonprofit that hired a data analyst for the dual role of data scientist and chief data officer nearly three years ago. Finally, in the local government sector, we are seeing innovative leaders creating the chief data officer position to support data-driven strategies aiming to serve public goals such as increasing efficiency in public education, fire safety, and public health. In fact, Nashville just joined the short list of innovative cities – including Boston, Chicago, New York and Los Angeles – when it hired its first Chief Data Officer, Dr. Robyn Mace, in 2016.

Looking forward, 2017 holds significant promise for companies willing to engage in proactive data monetization discussions that redefine data monetization as “leveraging data to generate value,” that thoughtfully consider and assess perceived privacy risks, and that consider data as a corporate asset to expand business opportunity and competitive advantage.

 

A Blueprint to Insight

It's often easy for me to take for granted the insights I regularly receive from data. Whether that be from tracking a run with my Fitbit, looking at likes and views on social media, or using Google Maps to help me avoid traffic. Working for a company that has the word "Analytics” in its name means that I spend a lot of time in data, and working for a company as creative as Juice means I get the opportunity to truly enjoy navigating data as a visual experience.

I have mentioned our new product Blueprint a couple of times already, but I wanted to share some of the insights we've been finding in hospital and health system data and how that data affects internal decisions. According to a study from Becker’s Hospital Review, a hospital's workforce accounts for 54.2% of a hospital's overall operating costs. These people are a huge investment, and so the hospital needs to make sure that it's hiring and retaining the best people. It can be quite the undertaking to make sure that the Emergency Department has staff with enough experience to adequately do the job, or that hospital supervisors are retaining top talent.

As we have been digging into some of these different facilities' workforce data, we have started to come across varying insights that have turned out to be valuable to hospital leadership. For instance, Blueprint can show a Chief Operating Officer where she has the opportunity to consolidate. There are often multiple people who are spread out across a facility which could be consolidated into one or two units, reducing overhead. For a larger hospital system with multiple facilities, Blueprint can allow a leader to compare facilities across their enterprise. With this information they are able to compare by important metrics and ratios like staff-to-supervisor ratio, or staff to provider ratio across all their facilities.

Turnover, often a primary concern for HR, is another hot spot with which Blueprint helps provide insight. Recently, Blueprint was able to help a pilot customer that manages over 40 senior living communities locate the departments and managers with the most turnover. As we have continued to discuss Blueprint with HR leaders, they have expressed the need to be able to tie turnover to root causes like compensation or employee engagement. Since Blueprint is designed to take a large number of staff and find further subsets, it can act as a funnel to get you to the group of people you are looking to take action on. I was talking to an HR Director at a hospital yesterday who was saying that she has a hard time tracking the number of interns they have at any given time and what departments they are in. Using Blueprint, we were able to find that information within a matter of a couple of clicks.

By taking the HRIS data of a hospital and health system and categorizing it in a meaningful way, we feel as if we have stumbled onto something truly valuable. The finish line being helping hospitals and health systems build a successful Blueprint for their organization.

Want to know more about Blueprint and how it could help your organization? Drop us an email. We'd love to hear from you.

 

 

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.

 

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.

 

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 info@juiceanalytics.com or for a more in-depth look schedule a personalized demonstration.

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 info@juiceanalytics.com.

Success Story: Predikto Is Right on Track

Ever been in this situation? Your organization generates massive amounts of data critical to its success and you share it across your organization, but it's not being used successfully. The visuals showing the insights aren't clear (or worse - they're buried in a spreadsheet), people can’t make heads or tails of it and as a result you don't hit your business goals.

If that sounds familiar, you’re not alone. Predictive analytics company Predikto found themselves facing the same problem. Their data product was being used to anticipate when railroad hot box detectors (or HBDs - monitors that detect train failure) would malfunction. The goal was to be able to get a maintenance crew out to fix an HBD before it could malfunction and stop any trains, costing Predikto's client big bucks. But with multiple tracks throughout the country and massive amounts of data being generated, crews weren’t able to make sense of the data and get to the problematic HBDs in time.

After evaluating their different options, Predikto chose to implement Juicebox to visualize the information in a simple and actionable way. Using the data product, maintenance crews are now connected to HBD health-check displays, making it easy to identify potentially problematic HBDs and fix them before they can breakdown. Juicebox provided Predikto with the tools to save time, money, and most importantly, their workers’ sanity.

Want to know more about Predikto and their data visualization challenges? Download the official case study below. Or if you'd like to know more about how Juicebox can help you communicate with your end users, drop us a line at info@juiceanalytics.com.

Research Admin Survey Says...

A few weeks back, we surveyed university research administrators to get a better feel for their reporting practices and the types of tools they that use to communicate. Take a look at the results, and share in the comments below what surprised you most about the findings.

The survey results offer a glimpse into the Office of Sponsored Research's reporting process, effort and current tools. The survey results are from 84 different U.S. universities and 2 private research facilities compiled in the first quarter of 2016. They are a mix of 40% Public and 60% Private institutions.