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.

How to Build Better Data Products: Getting Started

This is the first 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.

Part 1: Getting Started

The first step in building a great data product is to pinpoint a customer need and determine how your unique capabilities will solve for that need.

A successful data product lies at the intersection of the three circles in the following Venn diagram:

  1. Your customer’s pain point, an urgent problem they want to solve;
  2. The characteristics of your data which can be brought together to solve that problem;
  3. The capabilities you have to enhance the value of the data to make it as useful as possible. 

Take the Academic Insights data product we designed and built for US News and World Report as an example of finding this intersection. (1) Their customers, university administrators, needed to understand how they compare to peer institutions and where they could best invest to improve their performance and stoke student demand. (2) US News was sitting on decades of detailed survey data and rankings to compare universities of all types. This data was unique in its breadth and historical coverage. However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about.

Let’s dive a little deeper into those three elements:

1. Pain Points

We’ve noticed a temptation with data products to forget the cardinal rule of any product: it needs to solve a specific problem. Without this focus, a data product comes in the form of a massive 100-page PowerPoint deck or a collection of raw data tables. There may be value in the data, but it is clear the product manager hasn’t thought deeply about their customers and what the data can do to solve their problems. I spoke to a credit card executive recently who mentioned how his bank spent huge sums of money on benchmarking reports. Despite his deep experience, he was unable to make sense of the reports he was sent. These are lost opportunities to deliver powerful data products.

“Your users are your guidepost. And the way you stay on the right path in the early stages of a startup is to build stuff and talk to users. And nothing else.” -- Jessica Livingston, co-founder of Y Combinator

With data products the core question of your user is: What information or insights will let you make better decisions and perform better in your job? 

Look for those unique situations where indecision, ignorance, or lack of information are blocking smart actions. Rather than solving your user’s pain, you need to enable them to solve their own pain. Physician, heal thyself.

2. What’s unique about your data?

The foundation of your product should be data that is somehow unique, differentiating, and valuable. In our experience, the right raw materials can come in a few different forms:

Breadth: Do you have visibility across an entire industry? Or population segment? Breadth allows you to provide benchmarks and comparisons that aren’t otherwise visible to your customers. One of our clients has data on the learning activities of more than 60% of all healthcare workers.

Depth: Can you explore deeply the behaviors of individual people, companies, or processes? By drilling into these activities, you may have the power to predict future behaviors or find correlations that aren’t visible to others. Fitbit tracks massive amounts of personal activity data from each individual user.

Multiple data perspectives: Are you in a position to combine data sources across industries or connect disparate data sources? By bringing together different perspectives on your subject, you may be able to answer new types of questions or explain behaviors through a multi-faceted perspective.

Naturally, having breadth, depth and multiple perspectives is best of all. Companies like Google, Apple and Amazon have profound data assets because they can both see human behaviors across a large audience and they know a lot about each individual.

3. Your value-added data package

It is seldom enough to create a data product that is simply a pile of data. That isn’t to say we haven’t seen many companies that believe that a massive data extract represents a useful solution to their customers.

People don’t want data, they want solutions.

How are you going to turn that data into a solution? There are many paths to consider:

  • Visual representations that reveal patterns in the data and make it more human readable.
  • Predictive models to take descriptive data and attempt to tell the future.
  • Industry expertise to bring understanding of best practices, presentation of the best metrics, analysis of the data, and thoughtful recommendations. Bake your knowledge of the problem and the data into a problem-solving application.
  • Enhancing the data through segmentation, pattern recognition, and other data science tools. For example, comments on a survey can be enhanced with semantic pattern recognition to identify important themes.
  • Enabling users with features and capabilities to make them better in their job. The user's ability to analyze, present and communicate insights can be a value-add to the raw data.

If you can determine the right recipe of customer need, data and value add, then you've gone a long way toward defining the foundation of your data product. But before getting down to designing the data product, you'll want to get the right people in place.

4. The right product manager

We’ve helped launch data products in many industries including healthcare, education, insurance, advertising and market research. The most important factor in turning a concept into a business is a quality product manager. The best product managers have a vision for the product, understand the target customers, communicate well, are definitive in their decisions and recognize the reality of technical trade-offs. For a more complete list of general product manager skills, check out this Quora answer.

For data products, we’d emphasize a few more skills. The product manager needs to understand the data, what it represents and the business rules behind it. It helps if she is a subject matter expert, but if not, she should know when to bring in more expertise. Finally, she needs to understand the technical challenges involved with building a data product and be able to weight the impact of changes (which are often necessary as you learn more) against the benefits of launching sooner and gathering customer feedback.

5. Get stakeholder buy-in early

Kevin Smith of NextWave Business Intelligence (a consultancy focused on data products) warns: “Get the critical stakeholders involved and in agreement early or you’ll end up reciting the history of the project and why key decisions were made many times for many people.”

Launching data products is a journey that doesn’t end at the product launch. It also can push your organization into new and uncomfortable ground. These realities highlight the need to build broad support early in your process. Ask yourself:

  • Is IT on board to provide development support, data access and data security resources and sign-off?
  • Is the COO ready to provide resources after launch to support and maintain the product?
  • Is your legal team confident that the data you’ve been collecting and incorporating into your data product is available for this new purpose?
  • Is the marketing team ready to support a product launch that includes all the resources, collateral and creativity required of any new product?
  • Is the sales team in place to understand the product, the target audience and establish the sales framework for pushing the product?

“The secret to getting ahead is getting started.” 
― Mark Twain

For data products, this means finding your sweet spot at the intersection of customer needs, your data, and data product value add. And then getting the right people lined up to make your product a success.

Next up: A design approach that leads to high-impact, high-value data products.

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:

Reporting Solutions: A Guide to Buy vs. Build When Design Matters

You did it! You finally pushed through your plan to improve your company's reporting. After months spent reaching consensus, gaining approvals, and nailing down a budget, all the hard work is behind you, right? If only it were that easy! Now you face a pivotal decision: whether to build or buy your reporting solution.

Whether you're buying or building a solution, each comes with its own set of misconceptions, hidden expenses, and value. We've outlined all of these in our new ebook, Reporting Solutions: A Guide to Build vs. Buy When Design Matters. It'll walk you through the pros and cons of each, and help you ensure that the best possible long-term solution is implemented. It even comes complete with a worksheet that will help you to set up your evaluation and score your options.

It's a quick but informative read, designed to take a little pressure off of the decision-making process so that you can focus on what's most important: delivering reports that people love to use.

Create Healthcare Data Products for Your Customers

Take a look around these days, and you'll notice that we're surrounded by data products. There's an emerging market for them, and consequently consumer-focused companies are delivering new data products everyday. With the explosion of data as infrastructure in the field of healthcare, it's an especially ripe territory for data products.

Here's a webinar that Juice CEO and founder Zach Gemignani recently held on creating healthcare data products. Watch and learn the three key factors to data monetization success, the most important parts of data product design, examples of healthcare companies that have successfully launched their own data products, and more.

Like what you see and want to know more? We want to hear from you. Schedule some time to talk with us about data product opportunities, or send us an email at info@juiceanalytics.com. 

Our Favorite Data Products

The bar is set pretty high around here, but there are definitely some awesome data products we've come across and thought we would share.

So, what makes a great data product? For Juice a great data product or application meets the following criteria:

  • Delivers valuable data to consumers or non-analytical users.
  • Displays presentation quality information vs. a four quadrant dashboard.
  • Offers interactivity and a way for the user to engage with the data.
  • Guides users through the information and does a great job with data storytelling.
  • The product uses data to drive revenue or retention.

The following are our favorite, non-Juice created, data products in the market today. Let us know at info@juiceanalytics.com of any that we're missing that meet these criteria.

Five Thirty Eight (fivethirtyeight.com)

These guys have really grown on us, especially in the recent months. They do a great job of wrapping data into a story. Their data storytelling skills are well developed. In the example below we value how they give color meaning and make labels easy to understand. Annotations and text descriptions are definitely where many data products are lacking.

True Car 

We really like the way True Car applies guidance to the user experience. It's very clear what actions the user should take. The interactivity of the histogram as well as the animations applied reveal the impact of the selected changes in a clear way. Note how the simple use of the numbers going left to right make it so that a first time user knows example how to use the application and consume the information. Also, the use of action words like See, View and Get are subtle, but give an added layer of clarity.

Payscale (www.payscale.com)

While Payscale, a salary comparison application, isn't as true(car) to the Juice Design Principles as we'd hope, there are some items they've done well. We love when charts are labeled using questions. See below how they use questions to describe what the charts are trying to answer. The user doesn't have try to interpret what is being conveyed. They know right away.

Foursquare Analytics (foursquare.com)

The Foursquare team offers this interface for customers to understand their user traffic.  There's lots of information to convey, but their focus on filters at the top and modular design really make it easy for the user to focus their attention on a specific data set.   I did crop the 3D pie chart from this display. It took away from a pretty valuable application.

To get more details on what makes a great data product, check out the case studies found throughout the solution pages but specifically at the bottom of the Healthcare page for case studies on Healthstream and Laerdal.

Video: The Juicebox Recipe

Since creating our platform Juicebox, we often get a lot of questions about what it is (and isn't). And while we could give you a bulleted list of facts about Juicebox and what makes it so special, we decided that it's better to show rather than tell. Find out why it's so important to make your data useful and engaging, and see just what it means when we say we're here to make your data delicious.

Want to learn more about how Juicebox can make your data delicious? We'd love to schedule some time to get to know you and show you more of what Juicebox can do.