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 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.  

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.

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.

Actionable

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 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.

Creating Annual Reports People Love to Read

It's no secret: annual reports are typically a pain to create and dull to read. They're one of the best opportunities we have to share everything we've done in the past year with people, so why is it that so often they fall flat?

We've found that there are a few things that can really make or break annual reports. Design, layout, and voice are just some of the things that all go into making annual reports that are not only easily understandable, but that people enjoy reading. A few weeks ago, we hosted a webinar (link to webinar at the bottom of the post) with our nine-and-a-half steps to making your data delicious and how to take your annual reports from "yuck" to "yum." Throughout the webinar, we surveyed attendees to get a better idea of their annual report practices and pains. Here's a breakdown of what we asked and the answers we received. They shed some light on current practices, and help to figure out what the future holds for annal reports.

Question 1: Does your annual report allow people to understand and act on the data?
We found that most people are dissatisfied to some extent with the clarity in their reports. It's not a new finding: confusion created by data has been discussed in multiple business and tech journal articles, and demonstrates the need for clear, concise, and direct communication in annual reports (skip to 6:22 in the video for more on using language effectively in reports).

Question 2: Is color used effectively?
If you're a long-time reader of the Juice blog, you'll know that color has meaning and is essential when sharing information. We found that most people use color in their annual reports, but realize that it's an important tool and want to know more about how best to utilize it. For more on the subject check out Juice's collection of design principles, many of which focus on color use in reporting.

Question 3: Do you see utility in using an online, interactive annual report?
The results of this question were overwhelming: attendees preferred online, interactive reporting over more traditional methods such as Excel or PowerPoint and printed reports. While there are different pros and cons to making the switch to online annual reports, it's important to note that in a few years online annual reports could be the standard (see more on the subject by skipping to 29:20).

If you'd like to talk more about annual reports, or data reporting in general, we're always around to chat. Take a look at your schedule and set up a time that works for you, or send us a message at info@juiceanalytics.com. Happy reporting!

Q&A with David Schweidel

We recently interviewed David Schweidel, a professor of marketing at Emory University's Goizueta Business School and a thought leader in the sphere of analytics and customer relationship management. Read on to find out more about his book, how to succeed in the data economy, what the future holds for data and data sharing, and more.

What is your current role at Emory University?
I am an associate professor of marketing at Emory University's Goizueta Business School. My research focuses on customer relationship management and social media analytics as a source of marketing insights. I teach undergraduate and graduate courses in marketing analytics.

Can you describe your book, Profiting from the Data Economy, and why you wrote it?
The book looks at three different players: consumers, innovators, and regulators. Through our daily activities, consumers produce large amounts of data, from purchase records and financial transactions to social media posts and detailed location records. A number of businesses have been built at least in part on the data that consumers produce. For example, targeted advertising and product recommendations are based on information that consumers have provided. One question that is looked at is, "what do consumers get in exchange for the data they provide?" From the standpoint of innovators, it examines what can be done using consumer data. The key in this relationship is the value that consumers are provided in exchange for their data. Lastly, what is the role that regulators should play with regards to protecting consumers and encouraging businesses built with consumer data?

What are some examples of organizations that are succeeding from the data economy?
We're familiar with many companies that are successfully leveraging consumer-generated data. Netflix, Amazon, Facebook, and Google are just some of the companies that benefit from the data that consumers generate. It's a win-win situation, as consumers also benefit from these companies putting insights based on consumer data to use. We also see examples of government making use of consumer-generated data, such as to inform police departments of potential crime hot spots or to identify the locations of potholes that need to be filled.

What does an organization need to do to get started?
Obviously data is part of the equation. But beyond the data that organizations may collect, there should be a strategy about how data collected will be put to use and how those providing the data will benefit from sharing it. Once that strategy is developed, then a number of questions still need to be answered, including "how do we communicate the benefits to consumers?" and "how do we secure the data that we are asking consumers to provide?"

What role does the customer play in whether an organization achieves benefits from their data?
The notion that organizations can benefit from consumer data is predicated on consumers being willing to provide that data. There needs to be a sufficient incentive for consumers to provide data, whether it is actively provided or collected through a passive means. The onus is on the organization to make its case to consumers to share their data.

Are you seeing a growing interest in data products and solutions organizations develop for customers? If so, what kinds of products are you hearing about?
There's a substantial interest on the part of the organizations to monetize their data assets. They already have the data, so building new products and services based on what's already been collected to produce a new revenue stream is a wise move. From a marketing standpoint, we're seeing companies become more data-driven in their decision making. Companies such as Cardlytics facilitate targeting based on past consumer activity. We can also look to social media platforms as new sources of data being provided by consumers, offering insights into the brands they prefer and how persuasive other marketing actions are. Location data is another source that is becoming increasingly popular for decisions such as site selection and marketing.

How important is data sharing and collaboration as part of the success equation?
Within an organization, data sharing across units is key. Multiple teams are going to be involved in collecting data, preparing it for analysis, and developing products and solutions based on the analysis. Collaboration is key to successfully developing and deploying data products and solutions.

What developments in 2016 relating to data and data products are you most excited about?
One continuing development that we need to pay attention to are shifting preferences about data privacy and the role that government is going to play in this space. Mobile devices have tremendous potential as data collection devices. We're getting closer to being able to connect mobile and online activity to consumers' offline actions.

Find more resources on data products here.

 

Learn more about how Juicebox can help with your data product: