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: How to Build Better Data Products, Part 2 - Development

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 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 

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 of any that we're missing that meet these criteria.

Five Thirty Eight (

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 (

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 (

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. 

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.

5 Differences between Data Exploration and Data Presentation

Your toolbox for data exploration tools is flush with technology solutions such as Tableau, PowerBI, Qlik, Spotfire, and ClearStory. "Visual analytics" tools give analysts a super-powered version of Excel for dicing data to facilitate the search for valuable insights. Flexibility and breadth of features is critical; the user needs to handle lots of data sources and doesn’t know in which direction she will go with the analysis.

Data presentation is a different class of problem with distinct use cases, goals, and audience needs. Think about the incredible data stories delivered by the The Upshot, Fivethirtyeight, and Bloomberg. These data journalists often demonstrate data presentation at its finest, complete with guided storytelling, compelling visuals, and thoughtful text descriptions. When compared to these examples, it becomes obvious that the best efforts by a data exploration tool cannot deliver high-quality data presentation.

Data exploration tools generally try to cram all the information on a single page; data presentation needs better flow and explanation to tell the story properly.

Data exploration tools generally try to cram all the information on a single page; data presentation needs better flow and explanation to tell the story properly.

You need a specialized solution if you really want to communicate data in ways that engage your audience. To understand the differences between data exploration and data presentation tools, let me offer five key ways that the activities are fundamentally different.

1. Audience — Who is the data for?

For data exploration, the primary audience is the data analyst herself. She is the person who is both manipulating the data and seeing the results. She needs to work with tight feedback cycles of defining hypotheses, analyzing data, and visualizing results.

For data presentation, the audience is a separate group of end-users, not the author of the analysis. These end-users are often non-analytical, on the front-lines of business decision-making, and have difficulty connecting the dots between an analysis and the implications for their job.

The needs and interests of a non-analytical manager will be wildly different from the analyst who speaks the language of data.

The needs and interests of a non-analytical manager will be wildly different from the analyst who speaks the language of data.

2. Message — What do you want to say?

Data exploration is about the journey to find a message in your data. The analyst is trying to put together the pieces of a puzzle.

Data presentation is about sharing the solved puzzle with people who can take action on the insights. Authors of data presentations need to guide an audience through the content with a purpose and point of view.

Data exploration is a journey to find truth; data presentation should guide your audience to focus on the most important data and insights.

Data exploration is a journey to find truth; data presentation should guide your audience to focus on the most important data and insights.

3. Explanation — What does the data mean?

For the analysts using data exploration tools, the meaning of their analysis can be self-evident. A 1% jump in your conversion metric may represent a big change that changes your marketing tactics. The important challenge for the analysts is to answer why is this happening.

Data presentations carry a heavier burden in explaining the results of analysis. When the audience isn’t as familiar with the data, the data presentation author needs to start with more basic descriptions and context. How do we measure the conversion metric? Is a 1% change a big deal or not? What is the business impact of this change?

Fivethiryeight provides explanation surrounding their visualization to ensure readers understand what they are looking at.

Fivethiryeight provides explanation surrounding their visualization to ensure readers understand what they are looking at.

4. Visualizations — How do I show the data?

The visualizations for data exploration need to be easy to create and may often show multiple dimensions to unearth complex patterns.

For data presentation, it is important that visualizations be simple and intuitive. The audience doesn’t have the patience to decipher the meaning of a chart. I used to love presenting data in treemaps but found that as a visualization it could seldom stand-alone without a two-minute tutorial to teach new users how to read the content.

My love for treemaps has been replaced by visualizations (like the leaderboard) that are more immediately intuitive to users.

My love for treemaps has been replaced by visualizations (like the leaderboard) that are more immediately intuitive to users.

5. Goal — What should I do about the insights?

The goal of data exploration is often to ask a better question. The process of finding better questions gets to new insights and a better understanding of how your business works.

Data presentations are about guiding decision-makers to make smarter choices. Much of the learning (through data exploration) should be done, leaving the equally difficult task of communicating the insights and the actions that should result.

In all these ways, data exploration and data presentation are different beasts. This is why we’ve chosen to focus on building the best possible data presentation tool, Juicebox.