data visualization

2019 Data Summer Reading List

“Deep summer is when laziness finds respectability.”

Sam Keen

Now that Summer is here it’s a great time to recharge our batteries. Whether it’s a much needed vacation, a nap in the hammock, hours watching soccer games or curling up with a good book. Here are the books that made it onto the Juice Summer reading list this year. We’ve started some of them, but plan to get through the entire list by Labor Day.

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After hearing Alberto speak recently in Atlanta on his book tour we added it to our list. We’re sure it will make it to the Juice reference library along with his other books.

 
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This book was on Bill Gates Summer reading list last year and we’re finally getting around to reading it. Each chapter tells a great story about how to think about data in the context of real life.

 
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This book has gotten a lot of interest in the data visualization community, so hard to ignore it and not make it a focal part of our Summer.

 
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As Juicebox supports data storytelling at scale, we love to read anything we can get our hands on about stories. This one came highly recommended to us.

 
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We’re always up for some clever humor. This book fits the bill and just skimming us made us laugh.

 
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This book is a beautiful compilation of maps and hard to put down. Very enjoyable to skim and appreciate the illustrations on a rainy day or Summer afternoon.

10 Visualizations of Juicebox

Christmas is a special time of year. We all have our favorite aspects of the season. In the spirit of Christmas and the Christmas carol, the 12 days of Christmas, here are the Juice team’s 10 favorite visualizations.

You will recognize some of these as your own favorites, but some are exclusive to the Juicebox platform. To learn more about the visualizations exclusive to Juicebox and Juice design schedule some time with us.

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Leaderboard

An exclusive Juicebox visualization. Leaderboards are a great way to look at a dimension or group across multiple rankings. Who really is best on the team? Its never one metric and a leaderboard lets you compare across multiple metrics. Here’s a video showing a leaderboard in action from a few years ago.

 
Flower

Flower

A very engaging way to compare performance across locations, such as hospitals or schools. Each entity is represented as a flower and every metric is represented by a petal. We were inspired by the work of Moritz Stefaner.

 
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Ranked List

Its not just a horizontal bar chart, but an interactive way to see a top ranking as well as a way to explore a long list. The Juicebox way of letting a user explore a long list is unique. Easy to understand because of its familiarity while delivering a lot of interactivity for exploration. Here’s an example from our Notre Dame application.

 
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Sankey

This visualization isn’t exclusive to Juicebox, but well-loved by clients because of its easy way to explore changes over time among groups. Our version is much easier with more options than the Tableau version with its dynamic generated polygons.

 
Distribution

Distribution

This is exclusive to Juicebox despite its less than creative name. The data is binned to show distribution of values while also emphasizing the individual items (by showing details on roll-over) that make up the “bars.” A really easy, yet powerful way for users to explore their data.

 
Orbit

Orbit

A variation on the bubble chart that shows relationships. This breaks some data visualization rules, but is helpful for exploring hierarchy and avoiding too much overlap. Like a bubble chart it uses size and color to convey information.

 
Scatter

Scatter Plot

The scatter plot is a common visualization for data exploration. Juicebox adds panels and panel colors to better help the user understand the values that are good or bad. By clicking on a panel, the user can focus on a specific group of items for action.

 
Map

Map

We love using maps as a filter for other visualizations. Additional encoding with dynamic labels further adds to a user’s understanding of the information.

 
Treemap

Treemap

We’ve been doing TreeMaps since 2009 so they hold a special place for us. This is still one of the best ways to show hierarchical data that has values that can be aggregated.

 
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Key Metric

While not quite a visualization, almost every data story starts with a quick summary of metrics, often with a comparison to goals or benchmarks. A key metric visual sets the foundation for what a user will get in their dashboard or application.

 
Lollipop

Lollipop

The Lollipop is another good way to show comparisons among groups. Lollipop is our preferred way of sharing metrics when the metrics can be compared along a common scale. This is a good alternative to a bullet chart.

2018 Data and Visualization Gift Ideas

We’re continuing our tradition of the annual data gift guide. These are some of our favorite books and gift ideas for the data scientist, designer or analyst in your life.

While you’re here take a look at the Juicebox product page to see what it looks like unwrapped.

Happy Holidays!

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New Books We Love

Books we read in 2018

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Classic Data Books

We’re a little biased in this category, but these are the books on our desks that we refer to all the time.

Data Fluency - Thinking about changing how your team or organization works with data?This is the book for you.

Storytelling with Data - This one already feels like a classic. It provides simple, clear guidance on chart usage and storytelling. Hard not to reference it in the midst of a project.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - This is the book that keeps us grounded. Despite how much we think data is delicious and fun its serious too.

The Man Who Lied to His Laptop: What We Can Learn About Ourselves from Our Machines - A seminal read on learning about interactions between humans and machines.

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics - Nathan Yau’s book that teaches us something new every time we pick it up.

The Truthful Art: Data, Charts, and Maps for Communication - We love all of Alberto’s books, but this one is our favorite. Wonderful examples throughout the book.

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Art & Posters

Infographics, Maps, Data Art & More

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Data Nerds

This is a term of affection during the holidays.

Trust the Process

My son and I are really excited about the new NBA season. We are Atlanta Hawks fans, so we’re not too optimistic about this year. We know the team is young and has decided to undertake a rebuilding process. Our mantra for this season is the now familiar “trust the process”.

If you’re not aware of the phrase “Trust the Process” comes from the Philadelphia Sixers rebuilding efforts over the past couple of years. What’s most interesting to me is that the formula for team success is much broader now. It is no longer just about having great players, but free agency positioning, analytical prowess, superior facilities and developing long-term successful franchises.

It's all about the process now.

I find the same can be said for delivering customer data and dashboard solutions.

Much of the historical focus when deploying data applications (customer dashboards, embedded analytics, etc.) has been on selecting the right tool. However, despite so many more great tools and increased investment in the BI space, successful implementation rates have not improved.

In a research piece by Dresner Advisory Services from May of this year, they highlight the fact that successful BI implementations are most often tied to having a Chief Data Officer (CDO). This makes a lot of sense because the CDO is just like an NBA team’s general manager. They bring accountability and experience as well as a process to make customer data solutions successful.

Here are some elements that make process so valuable to delivering data applications and solutions.

  • Launch Dates - A process is the best way to mitigate against missing the launch date. The existence of checklists, status updates, and documentation offer a means to anticipate risks that cause delays. Remember that delays to the product launch or release directly impact revenue and reputation. Missing product launch dates is not something that goes unnoticed.

  • Customer Credibility - When delivery dates are missed, requirements miss the mark or dashboard designs don’t serve their audiences product confidence is lost. Its not only the customer’s confidence that we need to be concerned about, but also the sales and marketing teams. Once we lose the trust of these audiences it takes time to regain it, not unlike sports teams who fail to deliver winning teams over many years (see: New York Knicks).

  • User Engagement - When there is no process that means there’s no planned effort to understand the audience and deliver the dashboard design. If users can’t understand the data you’re sharing with them, a cancelled subscription is a near certainty.

  • Applications, not Dashboards - The best dashboards are purpose-driven applications. Tools don’t deliver purpose. The process undertaken to understand and solve a real problem delivers a purposeful solution.

  • A Complete Platform - A dashboard solution is only a means of displaying data. A process defines ALL the requirements. Having a process recognizes that a complete solution is needed which includes security, user administration and application performance optimization.

Much like NBA success, a successful customer dashboard implementation isn’t about picking a product (player), but sustained success over many years of increased subscription (tickets) revenue, fan engagement and loyalty. The path forward for distributing and delivering on valuable data applications is all about your process.

In the event that you don’t have a process or a CDO leading your efforts, click here to learn about the Juicebox Methodologies. It's our way to design and deliver successful, on-time applications as well as wildly loyal fans. Trust the process. It works.

The Future Belongs to Purpose-Built Apps. We're Betting On It.

“Purpose-built apps”

“Low-code app development”

“hpaPaaS”

“Citizen Data Scientists”

“Data monetization”

Witness the cloud of new buzzwords floating in the air. Let me see if I can knit these concepts together to shed light on their meaning and implications for the future of analytics.

Collectively, these phrases are a reaction to the long-standing challenge of getting more data into more hands. “Democratization of data” can seem perpetually right around the corner (if you’re listening to vendor marketing) or a distant illusion (if you are in most organizations).

At Juice we have a picture that we call ‘The Downhill of Uselessness’. It shows how the usefulness of data seems to decline as you try to reach more users. On the far left, the most sophisticated data analysts and data scientists are happily extracting value from your data. But as you extend to the outer edges of your organization, data becomes distracting noise, TPS reports, and little-used business intelligence tools.

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Three barriers to democratizing data

The struggle of getting data to more people in more useful ways boils down to a few unsolved problems.

First, general purpose platforms and tools (data lakes, enterprise data warehouses, Tableau) can be a foundation, but they don’t deliver end-user solutions.

"Vendors and often analysts express the idea that you can master big data through one approach. They claim if you just use Hadoop or Splunk or SAP HANA or Pervasive Rush Analyzer, you can “solve” your big data problem. This is not the case.”

— Dan Woods, Why Purpose Built Applications Are the Key to Big Data Success

Second, reporting and dashboards deliver information, but often lack impact. In our experience, most data delivery mechanisms lack: 1) a point of view as to what is important; 2) an ability to link data insights to actions in a users’ workflow.

Third, the people who truly understand the problems that need to be solved don't have the technical capacity to craft re-usable solutions. We all have that elaborate spreadsheet that is indispensable to running your business and, frighteningly, only understood by a single person.

A better path forward

Finally, there is a realization that these problems aren’t going away. There needs to be better approach. It will come in two parts:

  1. Focus on creating targeted solutions (applications) that solve specific problems. Apps can integrate into how people work and the systems where actions occur. They attempt to let people solve a problem rather than simply highlighting a problem. And applications are better than general purpose tools because they can bake in complex business rules, context, and data structures that are unique to a given domain.

  2. Give greater impact and influence to the people best know the problems. It has always been unfair to ask technologists to create solutions for domains that they don’t deeply understand.

This direction aligns with Thomas Davenport’s view of Analytics 3.0 (from way back in 2013). He postulated that the next generation of analytics would be driven by purposeful data products designed by the teams who understand customers and business problems. (No offense, Tom, but we were griping about ivory tower analytics back in 2007.)

And so emerges a new model and new collection of buzzwords...

Purpose-Built Applications

Solutions that start with the problem and craft an impactful answer. Their success is measured by fixing a problem rather than in terabytes of data stored.

…built using a high-productivity Application Platform as a Service (hpaPaaS)

Cloud-based development environments requiring little coding ability (‘low-code’) — but requiring knowledge about the domain and the problem to be solved.

…to be used by Citizen Data Scientists (CDS).

the people who know the problems most intimately.

At Juice, we may have backed into this trend or cleverly anticipated it. Either way, now I can say that Juicebox is a low-code hpaPaaS designed for CDS to create purpose-built apps. Better yet, we are now fully buzzword compliant.

Is Your Data Product Ready for Launch?

Looking to transform your data into a valuable, customer-facing data product?

From concept to design and launch, we've worked with dozens of companies to create successful data products. Our checklist provides seven evaluation criteria to see if your data product has what it takes to succeed.

Does your data product...

  1. Solve a distinct problem?

  2. Meet users where they work?

  3. Guide users to insights and actions?

  4. Make users feel safe and in control?

  5. Bring credibility to your data?

  6. Have the ability to operationalize the solution?

  7. Support customers for success?

Download the PDF here.

Education Leaders Embrace Data Storytelling

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The Data Storytelling Revolution is coming to the K-12 Education world -- in its own unique way. Two days at the annual National Center for Education Statistics STATS DC Data Conference in Washington DC gave me an up-close view of how education leaders were using data to drive policy and understanding school performance. This insiders view was thanks to an invitation by our partners at the Public Consulting Group, one of the leading education consulting practices in the country.

After attending a handful of presentations and hanging out with industry experts, here are a few of my impressions:

Education leaders have a fresh energy about data visualization and data storytelling.

To start with, the conference was subtitled: “Visualizing the Future of Education through Data”. To back this up, the program featured more than a dozen presentations about how to present data to make an impact. There was good-natured laughing and self-flagellation about poor visualizations, and oooh's and aaah's at good visualizations. There was also a genuine appreciation for how important it is to “bridge the last mile” of data to reach important audiences.

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Unsurprisingly, Educators understand the need to reach and teach their data audiences.

For many of the attendees, their most important data audiences (teachers, parents, school administrators) are relative novices when it comes to interpreting data. There was a general appreciation that finding better ways to communicate of their data was paramount. The old ways of delivering long reports and clunky dashboards wasn’t going to suffice. The presenters emphasized “less is more” and the value of well-written explanations. I even ran into a solution vendor committed to building data fluency among teachers.  This sincere sensitivity to the needs of the audience isn’t always so prevalent in other industries.

Data technologies and tools take a backseat to process, people, and politics.

On August 20th and 21st, I’ll see you at the Nashville Analytics Summit. When I do, I bet we’ll be surrounded by vendors and wide-eyed attendees talking about big data, machine learning, and artificial intelligence. Not in the Education world. After the lessons of No Child Left Behind and years of stalled and misguided data initiatives, Education knows that successful use of data starts with:

  1. Getting people to buy-in to the meaning, purpose, and value of the data;

  2. Establishing consistent processes for collecting reliable data;

  3. Navigating the political landmines required to move their projects forward.

The Education industry is more focused on building confidence in data, than in performing high-wire analytical acts.

Education has not yet found the balance between directed data stories and flexible guidance.

I sat in on a presentation by the Education Department where they shared a journalism-style data story that revealed insights about English Learners. There website was the first in a series of public explorations of their treasure-trove of data.

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On the other extreme, the NCES shared a reporting-building engine for navigating another important data set. On one extreme, a one-off static data story; on the other, a self-service report generation tool. The future is in the middle — purposeful, guided analysis complemented by customization to serve each individual viewer. The Education industry is still finding their way toward this balance.

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Every industry needs to find its own path to better use of data. It was enlightening for me to see how a portion of the K12 Education industry is evolving on this journey.

4 Steps to getting started with data products

Over the years, we’ve had the pleasure to work with many great individuals and companies and through our work have gained the ability to sympathize with their experiences of what we like to call “going from 0 to 100."

No, we’re not endorsing excessive speeding in your car. We’re talking about going from having nothing but hopes and dreams about delivering engaging analytics (0) to having an interactive data story that your users don’t want to put down (100).

Because we’ve focused our efforts on taking clients from 0 to 100, commonalities or trends for best practices in the data and design experience (read: everything between 1 and 99) have become increasingly clear. Use these four tips to make your introduction to data products a better, more frictionless experience.

1. Know your audience

  • What do the end users you have in mind for the product look like? What questions will users ask of the data? What actions will they take with the answers to these questions? These are all things you should know before beginning to work on data products.

  • Be specific about for whom you are creating a data product. If you try to provide insights for too many types of business roles you run the risk of making it too broad for any role to gather insights from the data.

2. Gather the right data

When putting together the data to be used in your product, it’s important to discern the difference between “more data” and “more records."

  • More data: It’s not always in your best interest to gather the most “data” possible. By doing this, you run the risk of gathering data that you may not use and wasting money in the process.

  • More Records: Gathering “more records” (read: rows of data) is a better strategy as you prepare for your data product. Doing so can alleviate the effects of outliers and unearth trends in the data.

3. If you’re new to the data, begin with an MVP (Minimum Viable Product) and let your users determine what features should be included

Building out all the bells and whistles you think you might need at the beginning the data product’s life can be expensive. Starting with an MVP that is put in the hands of actual end users will help determine what data is actually needed and what design aspects are best for your purposes.

  • Helps with data: Starting with an MVP helps determine the shape and caveats that exist within your data, and allows your users to make decisions about what data is most important to them.

  • Helps with design: By starting with an MVP, all of the questions that you and your users have for the data are answered by the design. Additional features can then be added from that point on in a more cost-effective manner.

4. Be open-minded about visualizations

  • We won’t get into data visualization principles in this section because that warrants a totally separate article, but a simple point here: just because you saw similar data in a pie chart once doesn’t mean that is the only (or best) way to visualize your data.

  • Because your users are the ultimate consumers of the data, let them be the judges of what visualizations will be most effective for them.

Easy peasy, right? We think so, but maybe that’s only because we’ve helped so many customers get from 0 to 100. If you're still not sure what your next steps should be, we’re here to help. Learn more about our 0 to 100 process by checking out the document below.

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

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.