15 Best Data Storytelling Tools (updated for 2023)

Data storytelling is quickly becoming a popular mode for presenting data. It combines text and graphics with data visualizations to guide an audience. Traditionally, people have used tools like PowerPoint and Excel, as well as traditional dashboard and business intelligence platforms, to communicate in this way. But these solutions are limited in their ability to balance the explanatory and exploratory elements of an effective data story.

We are seeing a new category of tool emerge: the data storytelling platform. It emphasizes features such as human-friendly visualizations, integration of text and visuals, narrative flow, connected stories, easy-to-learn authoring, and effortless sharing.

The demand for better data storytelling is being met by a growing collection of data storytelling tools. We evaluated tools that resembled the description above, leaving out more technical tools, visualization libraries, and old-school dashboard/report tools. In the end, we identified four unique categories:

• Guided Analytics

• Stand-alone Visualizations

• Data Storytelling as a Feature

• Design over Data

• Stories with Words

Guided Analytics

These solutions combine exploratory data visualization with explanatory text and graphical elements. The interactive data storytelling applications created by these platforms are intended as an alternative to traditional dashboards and reports.

Juicebox

Juicebox combines modern, data journalism style with exploratory visualizations that are automatically connected to enable analysis. A focus on easy authoring makes Juicebox the only tool in this category that is accessible to non-technical or non-analyst users.

In their words: Deliver more “Aha!” moments in every data presentation. Present like the pros with custom graphics & interactive data.

Strengths: Lightweight, easy editing, professional web design, automatically connected visualizations.

Cost: 14-day free trial. Starter plan is $45/month.

Toucan Toco

Toucan Toco is one of the earliest solutions for data storytelling. This platform targets enterprise buyers and has a unique approach to presenting data stories. Sharing, annotation, and drill-in story views give you a chance to communicate a comprehensive overview of a topic.

In their words: Communicate actionable insights at scale using Toucan’s built-in no-code framework for storytelling.

Strengths: Dashboard-style layout; user management features; sharing via presentation-mode for sharing.

Cost: Annual subscription. Reach out for a quote.

Nugit

Nugit has flown under-the-radar for some customers but represents one of the most complete data storytelling solutions on the market. Attractive design combined with powerful text features make this a solution worth watching.

In their words: A better way to share data with colleagues and customers. Automated tools for creating data stories on web and email.

Strengths: Live API integrations, report/email automation, automated natural language generation, infographic-style graphics.

Cost: Not available.

Flow Immersive

A unique approach to visualizations and storytelling. This platform is focused on eye-catching 3D visual outputs embedded with videos or Prezi-like presentations.

In their words: Author, present, and share immersive, interactive Flow data stories through the web, a recorded video, or in a meeting.

Strengths: Multi-dimensional visuals for showing many points positioned in 3-dimensional space.

Cost: $99/month. 30 day trial.

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Stand-alone Visualization

These visualization solutions offer flexibility and beautiful design to build individual visualizations. The end-product is generally intended to be embedded in a webpage, often as part of an online article.

Flourish (acquired by Canva)

Flourish has built a loyal customer base by delivering creative and beautifully-designed visualizations. They are well-known for their racing bar-chart, but have many other visual options.

In their words: Easily turn your data into stunning charts, maps and interactive stories.

Strengths: Animated visualization, easy embedding, fine-grain configuration of visualiations.

Cost: Free tier. Paid plans start at $69/mo.

RAWGraphs

RAWGraphs is one of the quickest, easiest ways to create advanced visualizations. An open source project with a long history, this tool provides a simple step-by-step process to create downloadable images for embedding in webpages.

In their words: The missing link between spreadsheets and data visualization.

Strengths: Open source, lightweight editing, advanced visualizations, data doesn’t leave your browser.

Cost: Free.

Datawrapper

Datawrapper is a popular tool for data journalist around the world. With a collection of attractive visualizations and advanced maps, Datawrapper gives you the configuration flexibility to craft the precise visual you need.

In their words: Serving charts and maps for millions of readers, every day. Datawrapper helps some of the world’s best teams to tell their stories with data.

Strengths: Maps, chart configuration options, labeling features, scaling for millions of views.

Cost: Free plan. Pro plan $599/month.

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Data Storytelling as a Feature

This set of solutions are comprehensive business intelligence and visual analytics platforms. Data storytelling is presented as a feature or technique that can be accomplished within the larger platform.

Tableau Story Points

Tableau, a leader in visual analytics, saw the potential for data storytelling early on. They released a feature called ‘Story Points’ in 2014. The feature has not achieved wide-adoption among their customer base, and Tableau appears to be focusing on PowerPoint export options instead.

In their words: Story Points is a way to build a narrative from data. People tend to understand and remember concepts through stories. Story Points gives anyone the tools to create a narrative with data.

Strengths: Wide-adoption of Tableau; powerful data manipulation and visualization tools.

Cost: $70/editor/month

ArcGis StoryMaps

Built on the deeply established mapping platform, the StoryMaps feature allows for creating narrative descriptions to help readers navigate a geographically-focused story.

In their words: A story can effect change, influence opinion, and create awareness—and maps are an integral part of storytelling. ArcGIS StoryMaps can give your narrative a stronger sense of place, illustrate spatial relationships, and add visual appeal and credibility to your ideas.

Strengths: Mapping capabilities (ArcGIS is a market leader)

Cost: $500/creator/year

Qlik Sense Stories 

Qlik Sense is a well-established analytics platform with strong visualization capabilities. While it gets less press than its competitors Tableau and PowerBI, Qlik understands the need to reach broader audiences in the enterprise through data storytelling.

In their words: The purpose of data storytelling is to turn data discoveries into a story. Emphasizing important elements helps create convincing stories and supports stakeholders in decision-making.

Strengths: Powerful querying technology enables rapid analysis.

Cost: $30/user/month

PowerBI

PowerBI is Microsoft’s answer to the success of visual analytics powerhouse Tableau. Like the other solutions in this category, PowerBI provides guidance, features, and instruction around data storytelling without providing a focused solution for users.

In their words: The job of a data analyst is not just technical. It entails more than just transforming data into information. It is also about clearly communicating the key messages derived from this data.

Strengths: Comprehensive BI platform; integrations with deeply-adopted technologies.

Cost: Pro starts at $10/user/month.

Observable

Observable is a collaboration-focused data exploration and analysis platform. Users can explain the data, workflow, and insights alongside visualizations.

In their words: Explore, analyze, and explain data. As a team. Uncover new insights, answer more questions, and make better decisions.

Strengths: Flexibility of visualizations; focus on narrative and descriptions alongside the data.

Cost: $12/editor/month (annual subscription required)

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Design over Data

These solutions for designers are focused on creating infographics and presentations that may include charts and graphs as part of the document. The data is one of many media elements that tell the story.

Infogram

Infogram is a flexible design platform that includes capabilities for adding lightweight charts. It offers an array of formats for presenting information, including everything from dashboards and reports to social media posts and posters.

In their words: Create engaging infographics and reports in minutes

Strengths: Consistent branding, pre-defined templates, animations, output formats

Cost: Free plan. Pro starts at $25/month/user

Visme

Whether you want to create infographics, posters, social media graphics, or even videos, Visme is a designer’s toolbox. Like the other design-first tools, charts are intended to show a few data points rather than to enable analysis.

In their words: Create visual brand experiences for your business whether you are a seasoned designer or a total novice.

Strengths: A vast collection of icons and widgets; 1,000s of templates.

Cost: Free plan. Pro starts at $25/month/user

Piktochart

Piktochart is a design tool for building infographics, posters, flyers, social media graphics, and presentations. Data seems to be mostly an afterthought for a solution that focuses on brand, styling, and templates.

In their words: Improve your internal and external communication with Piktochart. Quickly turn any text- or data-heavy content into a visual story that your audience will love.

Strengths: Colors and branding, video stories.

Cost: Free plan. Pro starts at $29/month/user

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Stories with Words

These solutions focus on using words as the primary way to convey the story in the data. Their algorithms identify insights in the data and present those insights in sentences and bullet points.

SiSense Narratives

SiSense is a traditional business intelligence and dashboard solution that has added narrative capabilities.

In their words: With Sisense Narratives, we use natural language generation (NLG) to automatically present you with calculations and insights in plain, easy to understand language based on what the engine recognizes as interesting.

Strengths: Integrated as part of a complete BI solution.

Cost: Not available.

ChatGPT

After Tableau’s acquisition of Narrative Science, one has to wonder whether similar capabilities will become freely, or inexpensively, available through Artificial Intelligence tools like ChatGPT.

In their words: We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

Strengths: Users are exploring using ChatGPT with data sets to see how it summarizes and extracts highlights. With the ability to recognize context and answer follow up questions, we can expect many more advanced solutions in this domain.

Cost: Free for now.

Phrazor

A focused solution for Natural Language Generation analytics

In their words: Explore, analyze and get meaningful insights to make data-driven decisions at the speed of thought.

Strengths: Conversational querying tool 'Ask Phrazor' even recognizes industry jargon.

Cost: 30-day free trial, $50 per editor per month.

Luminoso

Luminoso’s products help people in customer experience, HR, and research roles to understand and extract understanding from text data.

In their words: Turn text data into business-critical insights. Luminoso allows users at all technical levels, and across any industry or use case to analyze unstructured text – no data science experience required.

Strengths: Automatically uncover topics of interest.

Cost: Not available.

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How do you build a high-impact analytics team? Jamie’s team knows.

Meet Jamie Beason. She is a Senior Director of Business Intelligence and Analytics at JLL, a global professional services company specializing in real estate and investment management. Jamie has built an analytics team with a thoroughness and thoughtfulness that I’ve rarely seen.

If you are in the position of creating your own analytics team—or even if you are an analytics team of one—her approach is a blueprint worth emulating.

Jamie is also modest. What she’s done in her role at JLL is impressive and I wanted to help share some lessons from her analytics team-building approach. Jamie’s Data Fluency team, a subset of her larger Business Intelligence team, is focused on facilitating the use of data throughout the organization. Here’s how she describes the objectives:

At the core of this team is a focus on the “last mile” challenges of analytics—bridging the gap between the data and the decision-makers. The Last Mile of Data is less about technology and more about people-to-people communication. Her team starts with this people-first perspective rather than the all-too-common fixation on the technical issues and tools of analytics. In learning about what she’s done, I bucketed her activities into six key lessons:

1. Build a team that understands the business as much as the data and tools;

2. Educate your customers;

3. Push solutions rather than wait for people to come to you;

4. Even great data products need to be sold;

5. Actively curate the portfolio of data products;

6. Always be proving your value.

Build a team that understands the business as much as the data and tools

A lack of a common language and understanding results in disconnects between data analysts and business decision-makers. Jamie prioritizes filling this knowledge gap:

In my experience, the real magic happens when someone who understands the business unites with someone who understands data and analytics. Most of my team, myself included, are new to this industry, and until we understand the business, we’ll only ever be ticket-takers who build whichever dashboards or automations we’re asked to. My goal is to up-skill BI professionals with the business-specific basics, at a minimum, so that we can connect dots our customers would have never thought to ask for.

Educate your customers

Jamie and team recognize that their analysis will only make an impact if the recipients of information have data skills themselves. To that end, the analytics team proactively delivers learning tools so business customers can become more data fluent.

• Library of Data Moments for brief drips of data education: Similar to the popular Safety Minutes/Moments we see as the common start to meetings, these micro-trainings are designed to weave organically into any meeting. Our intent here is find those that need Data Fluency/Literacy training and awareness where they’re at. We know we can’t wait for them to come to us.

• Data Fluency training program: This will be similar to other L&D content employees take at their company like leadership or presentation skills training. We have our modules defined and are nearly ready to release our first one. It is interactive and intentionally designed to inform participants first of the importance of being data savvy, not just at work but everywhere!

• Customer-facing BI guidebook: This is meant to address the repeat questions we often receive from customers about how to best engage with our team. It’s a laymen’s term quick guide that walks users through how to request access, ask for support, submit a request for a new report, etc. This has been a hit!

Push solutions rather than wait for people to come to you

Over and over, I hear from frustrated analytics professionals who create valuable data products, but can’t get their audiences to engage. One answer: bring it to them in the channels and times when they are willing to give attention. Jamie’s Data Fluency team has searched for opportunities to inject data into existing communication channels and connect with their business users.

• Data Trivia in Newsletters (with prizes!): Who says you can’t have fun at work? Not us! In another effort to meet the customer where they’re at, we’ve partnered with our communications team to create a new section in their monthly newsletter. We pose a data question and hold a random drawing for everyone that submitted the correct answer, and the winner gets $25 to our corporate store!

• Data Tips of the Week for Service Line newsletters & Client Portal tile: We added a new section to the portal we use at work that highlights a quick data tip. This is another effort to get front and center of where our customers live and breathe. If they see us (Data/BI) everywhere, our customers can only ignore us for so long!

Even the best data products need to be sold

Despite the best intentions to bring data into decision-making, business users are busy and distracted. Therefore, it is important to take extra steps to teach your users how to use these products and show why they should be excited about the impact. I particularly like Jamie’s focus on telling stories about “wins” because this is one of the quickest ways to encourage adoption.

• BI User Stories: What better way to bring new customers into the BI fold than have them hear from a colleague the wonders it’s done for them? Sharing a few first-person sentences about how BI is saving the day tackles a few things: the message increases awareness, makes it more approachable (because it’s coming from people they know with less bias than if it came from the BI team), and it helps prove our ROI because some of these include call-outs to metrics that have improved.

• Training Videos (big hitter: BI Portal Overview): The BI content (reports and dashboards) that we create lives on this BI Portal, so it’s imperative that people know how to navigate it. The purpose of creating this video is to make it quick and painless for people to both learn how to navigate but also what all is available for them. Since spinning up our Data Fluency team, we have found pockets within our customer base that don’t know a thing about BI or what we do. So, we start them at the beginning by acquainting them with what already exists.

• BI updates on all regional client-facing QBR’s and internal townhalls: One of the aims our Data Fluency team is to simply be more visible and cross more desks. One way we do this is by volunteering to present on large calls such as our Quarterly Business Reviews or internal townhalls, which so far has been eagerly accepted. Again, if we come to the customer, they can’t help but see us!

• Hosting dashboard walkthroughs: While we deliver training anytime we create a new BI product, we find that with turnover, the memory of that tool can atrophy. Hosting dashboard walkthroughs is our effort to remind people what’s out there today that they could leverage to improve their ability to make data-informed decisions.

• BI Office Hours (added recently): These are similar to the dashboard walkthroughs, but the agenda can range a bit more. In this bi-weekly call, we send an agenda in advance based either on feedback we know people want to learn about or that we think is relevant. BI Leadership is also on the line to field any questions from the wide customer base. This started small but the audience is growing.

Actively curate the portfolio of data products

Many organizations end up with more dashboards and reports than they know what to do with. And the pile of data products only seems to grow. Jamie has found ways to make the JLL data products easily searchable while also trimming those that don’t add value. Here are three approaches her team put in place:

• Customer-Facing BI Usage Dashboard with Recommendation Engine: This is designed for two main purposes. One is to give people, mainly managers, visibility into who’s using what (or isn’t). The other is to help people onboard more effectively. They can filter by their service line and see the most-used reports and dashboards. The viz will also recommend dashboards they should consider using (“People that use dashboard A also use B, C, and D the most”).

• BI Catalog to help customers locate the data, report, or dashboard they need: Our purpose here was to make searching for items easy. Our catalog is in excel which makes CTRL+F easy to locate key words. Users can also filter for their service line and see a list of what’s available. It also includes key details such as refresh timing, owner, key stakeholder, etc. Given the size of our scope and the customer base we support, everything our team does needs to work at scale, and this catalog helps us do that.

• Archiving process for BI products and reports: True to Lean methodologies, we often ask “does this add value” and if it doesn’t, we find a way to remove it. Our archiving process tackles two things: It reduced the technical debt by requiring fewer items be supported, and it leans out our offerings, thus reducing confusion amongst our customers. If there are too many choices, we risk them just walking away, so we want to keep their list of relevant reports as lean as we can.

Always be proving your value

Analytics and data teams can struggle to show the return on investment for their activities. In particular, it is hard to measure the value of the many informed decisions that you might be impacting. Don’t wait for senior leadership to start asking these challenging ROI questions. Jamie and team have proactively developed analyses and reports that explain their impact while also reaching out to stakeholders for feedback so they can continue to improve.

• QBR Decks for Global BI Leadership: These are requested and not something we volunteered to make but in hindsight we should have! These decks give us a chance each quarter to highlight all of the wins across the team in front of leadership. They’re also superb for referencing later and adding up as the year progresses.

• ROI Dashboard capturing BI’s value varietals: I will admit we are still trying to crack this nut but we are well on our way. Quantifying the full ROI of a BI team is a challenge because not everything we do is a simple cost-out or efficiency effort. That said, we capture a variety of different metrics each quarter aimed at telling our full story and articulating our full value.

• Quarterly BI CSAT Surveys: This was one of the first things we did when standing up the DF team. Leading by example, we wanted our actions to also be data-informed and we didn’t have any data…so we collected it ourselves. I have used the results from these surveys in a variety of leadership capacities to illustrate, with data, how many customers consider BI dashboards and reports as a critical part of how their team gets work done and equally as important, how they see their need for BI changing in the next 6 months.

If you are in an analytics leadership role, I encourage you to connect with Jamie Beason on LinkedIn to learn more.

20 Best Data Storytelling Examples (Updated for 2023)

This collection of world-class data stories demonstrates how to combine data visualization, interactivity, and classic storytelling. Each of these examples shows the importance of a clear message, supporting data and analysis, and a narrative flow to engage the reader.

Want to learn more about data storytelling? We’ve compiled a grand collection of data storytelling learning resources. Or if you’re ready to build your own, try Juicebox.

We Feel Fine by Jonathan Harris

“An interactive website…that searches the internet every 10 minutes for expressions of human emotion on blogs and then displays the results in several visually-rich dynamic representations.”

An extraordinary early data story (it runs in Java) that inspired a generation of data visualization professionals.

The Physical Traits that Define Men & Women in Literature by Erin Davis (The Pudding)

Davis has created a beautifully-illustrated exploration of the different words used in literature to describe characters by gender.

The interactive website uses clever tactics to engage the reader — I particularly liked the quiz at the beginning. The combination of specific, personal examples and analytical research brings home the wide and discouraging discrepancies embedded in everything we read.

Animated Sport Results by Krisztina Szucs

More vignettes than stories, these brilliant animated visualizations show the progress of sporting events through a visual shorthand created by Szucs.

There are multiple visualization types for different scoring systems. Each one delivers a beauty and energy that better reflects the action of a game than a traditional box score — and displays information to help you understand the flow of the contest.

Buy or Rent by The Upshot

An interactive calculator that lets the user answer a basic and important financial question.

This tool is a classic demonstration of The New York Times’ design aesthetic, simple and direct user experience, and editorial clarity.

Why Do Cats and Dogs…? by Nadieh Bremer and Google Trends

A delightful romp through Google Trends search data about the behavior of dogs and cats. This data story combines guidance with customized exploration and beautiful visual design. Four Treats!

Fry Universe by Chris Williams

It is time to tackle the eternal question: What is the best form of fried potato?

This light-hearted visual journey explains how the ratio of fried surface area to un-fried surface area can make for very different eating experiences.

Ready to create your own interactive data story? We’ve created the fastest path to beautiful data storytelling. Try it now!

US Gun Deaths by Periscopic

This visualization shows “stolen years” due to gun deaths. It is a masterclass in connecting your audience with the emotional message by gradually revealing the data.

Explorable Explanations by Bret Victor

User experience and information design guru Bret Victor delivers a meta-data story to show how to “enable and encourage truly active reading.” This work from 2011 was ahead of it’s time — as is most of Victor’s imaginative, inspirational work.

Cicadas, A Data Story by Kayla Brewer

We are taken on an educational journey about Cicada ‘Brood X’ and the emergence of these insect swarms (“small fly bois bring big noise”). Harvey demonstrates how creativity and a public data set can be transformed into an exploratory data story using Juicebox.

30 Years of American Anxieties by The Puddling

“What 20,000 letters to an advice columnist tell us about what—and who—concerns us most.” This data story provides big picture views by topic and demographic integrated with specific examples that bring the data to life.

Data Stories aren’t just for data journalists anymore. Create your own interactive, mobile-friendly, professionally-designed data story with Juicebox.

Redistricting as Mini Golf by The Washington Post

Not all data stories need to show a lot of data. This example leans on a fun, interactive premise to show how re-drawing districts (i.e. gerrymandering) can impact politics. By playing the game, you learn about the different political strategies and how contorted the districts can become.

Visualizing 500,000 Deaths from Covid-19 in the US by National Geographic

“What does the loss of so many lives look like? Here are some ways to envision what 500,000 really means.” A powerful example of how to make your data relatable through comparison to familiar subjects.

Can You Live on the Minimum Wage? by The New York Times

“This calculator shows the hard choices that have to be made living on the smallest paychecks.”

The simple beauty of this data story is in its ability to convey a message through user choices. It doesn’t take long to realize that the answer is “no.”

OECD Better Life Index by Moritz Stefaner

“This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential”

One of the all-time data visualization designs with clever choices for representing the data (‘blooming flowers’) along with flexibility for user control.

How to Forecast an American’s Vote by The Economist

“We have built a statistical model to estimate the odds of how each respondent will vote in next week’s mid-term elections.”

The Economist takes a complex voter model and makes it both easily accessible and fun to explore.

How Many High School Stars Make It in the NBA? by The Pudding

“Every year the top high school basketball recruits get hyped up. How often do they pan out?”

This data story teaches its readers a novel data visualization, then uses it again and again to show different angles on the data.

A Disappearing Planet by ProPublica

“Animal species are going extinct anywhere from 100 to 1,000 times the rates that would be expected under national conditions.”

This analytic explorational lets the user dive into the data all the way down to individual species.

Wine & Math by The Pudding

A fun and engaging story that is a very palatable delivery mechanism for a statistical model to predict a wine’s quality by its properties.

This data story does an excellent job of setting the context and explaining predictive modeling before diving into the results.

Which 2020 Candidates Have The Most In Common … On Twitter? by FiveThirtyEight

FiveThirtyEight, known for its election coverage, shows how the Twitter followers of Democratic candidates overlap. The data story includes creative visualizations, another hallmark of this design team.

Bussed Out by The Guardian

“Each year, US cities give thousands of homeless people one-way bus tickets out of town.”

An elegant and deeply researched story about homelessness integrates insightful visualizations alongside stories of individuals.

The Rhythm of Food by Google News Lab / Truth & Beauty

“How do we search for food? Google search interest can reveal key food trends over the years.”

Another beautiful data story by Moritz Stefaner introduces the reader to a creative seasonal view of food consumption, then provides flexibility to explore.

The Stories Behind a Line by Federica Fragapane

“A visual narrative of six asylum seekers' routes. They travelled from their hometown to Italy. This project wants to tell their stories through the data that shaped their personal travelling line.”

Multiple modes of viewing the data provide different perspectives on the individual journeys.

Can You Stay Within the World’s Carbon Budget? by The New York Times

“This climate simulator lets you explore more than 8,100 climate scenarios.”

Once again, the NYT design team delivers an interactive lesson with data.

Happy Data by Giorgia Lupi

“Hopeful views of the world through data and drawings.”

Unlike the other examples, Lupi delivers a collection of short data “vignettes” that mix data and images to deliver a people-first message.

Seeing How Much We Ate Over the Years by Nathan Yau

“How long will chicken reign supreme? Who wins between lemon and lime? Is nonfat ice cream really ice cream? Does grapefruit ever make a comeback?”

Nathan Yau, a true treasure of the data visualization community, asks and answers these fun food questions. His visualizations are equally fun, and carefully crafted to highlight insights.

For even more examples, check out 11 More Examples of Good & Bad Datta Storytelling.

Data Insights, The Next Step in the Last Mile of Analytics

The “Last Mile of Analytics” is riddled with potholes. It is a surprisingly challenging journey to go from data analysis to influencing and changing minds. One of the biggest of potholes on this journey is the available attention of your audience. We hear the same things over and over:

My audience won’t open the report that I sent, even though I worked hard to make it easy to read.

My customers don’t take the time to sign in to our analytics tool.

I have many audiences with many different ways they want to see the data. Some want all the details; others just want to be told what is most important.

This common feedback points to a common problem: How do you deliver data in ways that people will consume it? It needs to be palatable and customized to the recipient’s desired form.

Burger King gets this with their taglines over the years:

• “Burger King, where you're the boss!”

• “Your Way”

• “When you have it your way, it just tastes better.”

• Now: “You Rule”

This was the challenge we have been considering at Juice. It isn’t enough to have a world-class platform for data storytelling. You need to reach your audience in the way they want to consume data. The more we spoke to customers, the more we heard the same thing: many people just want (1) insights or highlights delivered through (2) the channels of communication they already use.

We needed to design a fresh way to capture, annotate, and share insights so that the ultimate consumer of data got it “their way”. I’m delighted to share what we have cooked up in Juicebox.

Capturing Insights Needs to be Fun and Seamless

We wanted the capturing of insights to be an irresistible and persistent “easy button” so that the moment you had an “ah-ha!” moment, you could grab it.

In Juicebox, the capture insight button is available on all the visualizations and instantly snapshots your insight.

Insight Curators Need to Add Their Perspective

Context is King (sorry, Burger King). The person who finds an insight understands what intrigued them and often has important knowledge to overlay on the data.

In Juicebox, we provide a variety of annotation tools for pointing with arrows, scribbling, adding labels, and framing the important parts. We want people to drop their knowledge over the data visualizations like melty American cheese on a sizzling burger.

Sharing Needs to be Frictionless, and Where the Conversation Exists

Insights need to be sharable in the forms and channels where people are already having their conversations.

In Juicebox, these insights can be pasted into Slack or Teams, dropped into an email, or added to a PowerPoint presentation.

Next Up: More Capabilities for Engagement and Self-Expression

For me, the exciting part is enabling more people to use data as a way to communicate and express their expertise. Insight are about taking those special “ah-ha!” moments that we relish in our data analysis and enabling you to deliver that same bit of brilliant excitement to your audience. We have some fun ideas for helping you grab attention and draw your audience into the discussion about data.

Give it a try by scheduling a demo or requesting trial access.

Use Specific Examples to Enhance Your Data Story

I’m a long-time advocate for adding specificity into data presentation as a way to humanize your message. After all, we know that “specificity is the soul of narrative” according to John Hodgman.

Specific examples are a way to zoom in to the details while connecting your audience to the big picture ideas. I ran into a couple of great examples recently that help bring specificity to this general concept (see what I did there?):

Example 1: The Hope Summit

I recently attended a workshop put on by the Belmont Data Collaborative, part of a wider Belmont event focused on “Data-Informed Social Innovation so Regions can Thrive”. The goal was to use data to drive the discussion on health disparities in our local Nashville community. In particular, the analysis focused on hypertension as a prominent health condition in economically-disadvantaged communities.

As part of the event, we created this interactive report that lets you explore how socio-economic factors correlate with health conditions. This is data analysis as a way to highlight health disparities.

https://belmontdata.myjuicebox.io/a/hypertension_1/

But this data story was just one layer in the layer cake of presentations that brought this challenge home to the attendees. To bring specificity to the discussion, the workshop coordinator Charlie Apigian focused on a particular zip code that has been under-resourced by the city.

A powerful presentation by Katina Beard, CEO of the Mathew Walker Comprehensive Health Center that serves the local neighborhood made the problem real. We understood that the data tells only a small slice the full picture. The stories of individual people, their struggles to find time for healthy activities or gain access to healthy food options brought insight to the conversation.

It takes Data + Context + Human Connection to create understanding of a problem.

Example 2: I am Steve.

Any big idea needs to be made human-sized for it to feel relevant. Here’s a non-data example. One of my new favorite songs is I am Steve, by the group Hey Steve from the album Steve by Steve. (How can you not love their commitment to a theme?). Take a listen:

The theme of this song is universal — you don’t need to be a Steve to feel it. It speaks to existential questions: Do other people experience life in the way I do? Do we all share the same doubts and uncertainties about life decisions? Am I making the most of my life?

Those questions offer little traction outside of a philosophy class or a dorm-room hallway. Hey Steve makes it human-sized simply by framing the questions as if there are a community of Steves in the world:

“Am I only Steve with a voice in my head?”

“Go Stevie Go Stevie, be the best Stevie you can be. Run Stevie, Run Stevie, do what you can! And if it doesn’t work, just try it again.”

In this song, the Steves relate to each other as a common group. The lens has zoomed in, the connections are closer — and as a result, we are drawn in to ponder the big questions. We are all a Steve.

These are the reasons why we created a data visualization platform that makes text and images first-class elements in data storytelling. We know that you can’t bring in specific examples and human-connection only through interactive charts. We also enabled drill-down as a default because when people look at data, they almost always want to be able to zoom in to see the details that are more actionable.

Give it a try…you can do it, Steve!

The Delight of Data Insight

You know that moment when you uncover something refreshingly new in data?

It is that “wow” or “aha!” when the obvious emerges from confusion, when a messy world gives way to clarity.

These moments are not dissimilar from the flashes of insight we enjoy from stand-up comics. Check out the following short video from Bill Burr on the Conan show. In about two minutes, he drops (by my count) six comic insights that reframe how you might think about Lance Armstrong.

These delightful moments of data insight are the sweet reward in the analytics world. Whether you are a researcher, consultant, data analyst, or part-time Excel jockey, there is joy in finding something that everyone else has missed, or seeing a way to break down long-held assumptions with a new way of looking at a situation.

This is an important part of the ‘The Last Mile of Data’. For years, we’ve focused on visualizing data, creating focused, actionable data products, teaching data fluency skills, and telling data stories. But capturing, sharing, and curating data insights is the last (perhaps latest?) step in bridging the gap between data analysis and the minds of decision-makers.

Those data nuggets need to be communicated and shared in a way that your audience will latch on to them. In the video above, Bill Burr seems like he is just riffing on an idea. It is hardly so simple. He works hard on his act — from the phrasing to the segues to the facial expressions — to encapsulate the insight in an easy-to-swallow lozenge that makes the medicine taste good. The listener is willing to re-consider preconceptions because of the packaging.

This is why the role of the data analyst is challenging far beyond the need to manipulating data. You are a sociologist, salesperson, psychologist, product manager, and now…stand-up comic.

Comics are weavers of a thread that connects what we think the world is to what it actually is.

The Last Mile of Data is about going the extra step that will carry your hard work to the point of action. Creating reports and dashboards that show data paves the path to insights. There is real satisfaction in the engineering of a well-crafted dashboard. You’ve created a kind-of data playground that may spawn insights.

But don’t stop before you get to the good stuff — the delight of data insight.

A 12-Point Checklist for Public and Open Data Sites (with Examples)

Let the data run free! Government organizations, academic institutions, non-profits, and even passionate sports fans are gathering and sharing valuable data sets with the public. The topics are wide ranging, from climate change to health to inequality to happiness. It is a powerful way to support a cause and encourage data-driven analysis.

These open data sets are set loose on a website in hopes that interested visitors will come flocking. How do you make that site as effective as possible? Simply posting the data in a searchable format isn’t enough. To achieve impact, you need to make it easy to understand, manipulate, and explore.

The following checklist is a collection of best practices and reminders for your open data project.

1. State Your Purpose

To start, you need to address the question: WHY this data? And WHAT can a viewer gain from using the data? The following examples feature prominent statements about the purpose.

https://www.oecdbetterlifeindex.org/

https://blackwealthdata.org/

https://champshealth.org/

2. Segment by Audience or Topic

There are always many ways that someone could analyze and explore your data. Do them the favor of explaining HOW the data can be used. The best sites provide separate sections based on different users of the data and/or topic areas.

https://www.earthdata.nasa.gov/

https://dataunodc.un.org/

https://blackwealthdata.org/

3. Provide example insights

Naturally, you would like your visitors to find their own insights. However, they will benefit with a gentle nudge toward the types of insights that are available in your data. In the following examples, a few key insights are featured as a teaser to dive deeper into the data.

https://www.boxofficemojo.com/

https://www.nashvillehealth.org/survey/data/

https://blackwealthdata.org/

4. Let users find their own data story

Too many open data sites simply provide downloadable access to the data. This is a missed opportunity, particularly for visitors who may not have advanced analytical skills. Interactive, exploratory visualizations give your visitors a playground to find their own insights in the data. This is where the leading data storytelling platform can lend a helping hand.

https://seer.cancer.gov/statistics-network/explorer/application.html

https://www.oecdbetterlifeindex.org/

https://www.nashvillehealth.org/survey/data/

5. Encourage sharing of insights

If your site enables your visitors to find insights in the data, the natural next step is to let them share what they have found. Ideally, you’ll want the ability to capture specific visuals and share via social media.

https://www.oecdbetterlifeindex.org/

https://news.crunchbase.com/web3-startups-investors/

6. Use simple, intuitive visualizations

Keep in mind that the visitors to your data site are just coming up to speed on your data. Complex visualizations and sophisticated analysis tools are likely to overwhelm them, and cause them to bounce. You want to lower the cognitive load by finding simple and familiar ways to present the data.

https://www.nashvillehealth.org/survey/data/

https://www.movebank.org/cms/movebank-main

https://www.oecdbetterlifeindex.org

7. Include real-life examples

By its nature, data is an abstraction from reality. It summarizes and aggregates many individual data points to find trends and insights. However, this abstraction can separate your visitors from the specific things that are represented in the data. Take a moment on your site to reconnect your visitors with the actual subjects of the data.

https://data.unicef.org/

https://dmp.unodc.org/

8. Explain your metrics

For many public data sites that are focused on a particular topic, there will be a few key measures of performance. You want to ensure your visitors have a full understanding of these metrics so they can interpret the results accurately. We found many sites that do this well; others fail to provide the labeling or context to clarify what the data means.

Not so good: https://climate.esa.int/en/odp/#/dashboard

9. Explain why the data is credible

There is a lot of data out there. Why should your visitor trust what they are seeing? Take the time to explain the diligent work and research that went into gathering your data.

https://blackwealthdata.org/

10. Make the raw data accessibility for advanced users

For the novice data users, interactive visualizations are a great entry point into your data. The advanced users will have their own ideas about how they want to manipulate the data. You’ll want to give these users the ability to search your catalogue of data and download the raw data files.

https://search.earthdata.nasa.gov/

11. Provide resources to learn more

Data is great — but it is better with context. You want to include resources that give interested visitors the chance to learn more with relate research and other content.

https://www.earthdata.nasa.gov/

https://blackwealthdata.org/

12. Don’t forget the outreach and marketing

You’ve made an amazing public data website. The job’s not done. You need to make sure people know it exists. There are a lot of options: search advertising, organic search, social media, and lists of open data sources. A great starting point is to reach out to sites that relate to your topic area and make them aware of your data as a valuable resource.

Deliver more “Aha!” moments in every data presentation

What Can WordleBot Teach Us About Actionable Data Insights?

I’m a Wordle obsessive. Which is to say, every morning I find myself staring deep into my coffee in search of an elusive 5-letter word.

The New York Times (who bought the word game from software developer Josh Wardle for $3 million) knows their audience. We may be playing with words, but the analytical nature of this game is the appeal. It is a game of odds and mathematical deduction as we try to reduce the potential options available.

To appease us (and sink the hook a bit deeper), the NYT recently released WordleBot. Here’s how it describes itself:

I am WordleBot. I exist to analyze Wordles. Specifically, your Wordles.

In the next slides, I’ll examine your puzzle and tell you what, if anything, I would have done differently. Words I especially recommend are marked with my seal of approval. And, if you’re curious, I’ll show you the math behind my recommendations.

Below is an example of what WordleBot shows about a game (I chose a particularly lucky game for me).

WordleBot delivers data insights in a particularly clever way. Rather than guiding you to an answer (I built a data app for that — and it immediately sucked the fun out), it takes a different tact. It guides by teaching. Let’s see how:

There is so much good data communication here:

1. WordleBot teaches me how to think about my Wordle performance by defining different measures that impact success. Getting to the answer is a combination of Skill and Luck with the goal of reducing the number of potential Solutions Remaining.

2. In straightforward language, it describes my performance on the first guess.

3. Rather than telling me what I should have done, it provides alternative options and explains how the outcome could have been different. I can play out different scenarios.

After stepping through the series of guesses, WordleBot summarizes my choices compared to the optimal, data-driven choices.

This is a non-traditional way of sharing data insights — and something worth learning from. If you approached delivering data insights like WordleBot, you would focus less on telling people what they should do or, worse, what they should have done. Instead, you would ask yourself, how can I teach by showing different decisions and explain the resulting outcomes?

In other words, show how to think, not just what to think. In this way, the role of data in informing decisions will become evident through the evidence.

Brace your audience for impact. Get started with Juicebox

15 Lessons from the Data Story Creative Process

What do you get when you put a Data Scientist and a Data Storyteller in a room full of executives for two days?

Sorry, no punchline…this is serious. The answer is The Data Story Creative Process (DSCP) workshop — a hands-on, case study-based learning event that teaches a framework for using data to drive informed action.

We played with data, explored insights, structured stories, and discussed the barriers to reaching our audience. Here is a tasting menu of the lessons we shared:

A repeatable process

Every data project is unique. Yet our methodology applies common steps and best practices to bring discipline and focus. The DSCP is a more thoughtful approach to solving tough problems with data.

Hands-on learning with real data.

We learned a lot from our workshop. Lesson 1A: People like to get their hands into data in a realistic scenario to apply the concepts and skills we are teaching.

Find your data story

Your data story exists at the intersection of your goals, your audience’s priorities and levers, and the impactful data insights you are able to find.

This is one of many places where we emphasize the need for focus.

Visualize for readability and shared meaning

When it comes to visualizing your data, you have two primary objectives:

Readability: How do you minimize your audience's effort to understand the visualization?

Shared Meaning: Does the visualization support and emphasize your insight?

Deliver more “Aha!” moments in every data presentation. Schedule your free data makeover >>

Guide your audience to actions

Actionable insights should be the goal of your story, even if that action is a need to gather more data. We discussed the characteristics of a data-driven action and the frustrations of presenting insights that can’t be acted upon.

The goals is to change the mind of your audience

Working with data is a matter of mindset as much as skillset. Most importantly, you want to understand the needs of your audience so you can tell a story that changes their perspective.

Where are you in the movie?

Charlie shared a fantastic analogy for considering how to analyze data. You want to think of the data as if you are at a particular scene in a movie. You can look back to understand what got you to that particular point. Then you can use models to predict where the story is going to end up.

Data is a team sport

Our workshop discussion underscored our belief that data is a team sport. You need different players — the analyst, the subject matter expert, the decision-maker — to work together from the beginning to chart out a successful path.

Preparing your data

Preparing your data is the hard work that needs to be done before the fun begins.

For our executive audience, we wanted them to appreciate the potential complexity and effort that data preparation can require.

Demystified data terminology

There are a lot of data buzzwords and abbreviations flying around: AI, ML, lakehouse, data engineering, storytelling. We took some hot-air out of these terms and discussed what they really mean.

Different sources of insights

Finding data insights is part of the “Play” stage in our process. And there are many tools and techniques to reveal those insight. We want to combine both data-led insights (e.g. modeling) with exploration that is guided by human understanding of the problem.

Kill your darling data insights

Analytical play-time is great, but at some point you need to evaluate and extract the most valuable and actionable nuggets. We provided an approach for sifting through insights to find those that are “story-worthy.”

Structure a data story

Data storytelling uses the patterns and expectations of traditional narratives to grab and keep the attention of your audience. We can use the classic three-act play structure to set-up and deliver on inherent story expectations.

The (Short) Attention Economy

With our full inbox and onslaught of text messages, it is hard to grab and keep the attention of your audience. We shared techniques that can help your data story stick: be unexpected, connect to emotions, be specific, and be relatable.

Advocate for your data products

Our workshop was about defining a problem to be solved with data and creating the data story that will lead to action. We closed with a final message: you need to step up to advocate for your results. Think of your dashboard, report, or analysis as a product, one that needs to be marketed and sold for people to get the value.

Want to know more about the Data Story Creative Process and our training programs? Lets talk!

How Data Storytelling Can Build Better Customer Connections

“Like a good neighbor, State Farm is there.”

The State Farm tagline — like so many advertisements — does more than connect with new customers. It also wants to convince existing customers that they made the right decision in choosing State Farm for their insurance.

The same is true about delivering data to your customers. Reporting is more than a feature of your product, it is an opportunity to remind customers of the value your solution provides.

Some product companies understand this concept well. For example, Spotify’s Wrapped is an annual report sent to listeners to summarized their music habits. It is a delightful journey through personalized data and a reminder of how much you have enjoyed the service throughout the year.

Spotify is more the exception than the rule. Here’s a more typical example of reporting, courtesy of Hubspot:

Hubspot reports are literally the very last thing (last drop down, last item) you can find in the Hubspot UI.

But what if Hubspot’s reporting had more ambitious goals than a simple data access interface? What if it defined the pressing questions that many users should care about, and drew a direct line to answers?

A better form of reporting wouldn’t necessarily require different data as much as a user-oriented mindset. It would combine utility with a story that reinforces the value of the product. We are getting closer to “data storytelling” when we use data to convey insights and a message. My definition of data storytelling is:

The presentation of data to communicate a message using the techniques of traditional narrative forms.

How customer reporting can make a difference for your product

Customer reporting is an under-utilized tool for product leaders. Let’s examine the ways that it can build customer relationships:

1. Establish a language

You are the expert on your product and the data it captures. What metrics are you going to emphasize? What behaviors do you want to encourage? Reporting is your opportunity answer these key questions and define what matters.

Twitter is encouraging activity by making their first reported measure ‘number of Tweets.’

2. More touch-points, more better

Staying top-of-mind is critical for product success. Reporting is a chance to deliver a high-value piece of information to your customers — and remind them you exist.

FullStory delivers a weekly digest to my inbox that reminds me to check our engagement numbers.

3. Differentiate your solution

You will set your product apart from the competition when you make your data a valuable part of your solution.

Lunas Consulting (Sales as a Service) recognizes that reporting on the sales activity and wins is a critical part of their client value. They have designed a Juicebox report that provides an interactive exploration of weekly and historical results.

4. Understand the value drivers of your business

One of the under-appreciated elements of reporting is that it requires you to evaluate what activity is important (and what is not). You have to understand where your customers get value. This understanding can then impact your product development decisions. You will understand the drivers of your business better than ever.

Frameworks like the “North Star Metric” force a product organization to understand the key measures of customer value. You want to consider how your product success aligns with the value your customer sees, and how that is displayed in reporting.

https://amplitude.com/blog/product-north-star-metric

Reporting requires managing a careful balance of conveying a message about your value with the transparency and hard-reality of data.

How data storytelling can help

Traditional reporting is not great at connecting with non-analytical audiences. It tends to come in one of two forms: 1) The one-page dashboard that tries to compress as much data as possible into a small space; 2) The raw data table that makes no attempt to presenting the data in a way that might reveal insights.

Densely packed dashboard

Tables of data

In contrast, a data storytelling approach takes responsibility for communicating and engaging the audience. How?

It’s not about you, it’s about them.

You need to start with your audience in mind. The audience is the people who are going to do something with your insights and analysis. If you want them to change their behavior or decisions, you need to understand their motivations. What are their priorities? How do they best absorb data? What actions can they take?

In my recent presentation entitled ‘The Star Trek Guide to Better Analytics’, I found Dr. Bones McCoy to be a good characterization of your audience: he’s not an engineer (or data-savvy), he’s skeptical, and he’s people-oriented. But every audience is different. Check out our Data Personality Profile for a framework for evaluating your audience.

Data Storytelling is Writing

I hate to take you back to your writing classes but data storytelling isn’t just a collection of data visualizations, it is a form of writing. You are building a narrative that makes an argument.

Your narrative structure should start by posing the problem and context, followed by your analysis, and finally your conclusion. In a data story, we like to use the three-act story structure.

Be Simple and Specific

When you are presenting data to your audience, your priority is to make it easy to understand and easy to connect to.

Simple to understand means using charts and visualizations that are intuitive and well-understood. I love an exploratory treemap or network diagram or Sankey chart. But each of these advanced visualizations requires your audience to learn how to read them. We’ve found that it is far better to stick to charts that have a lower “barrier to entry.”

“Whatever it is, the way you tell your story online can make all the difference.”

— Quote Source

You also want to explain the data and insights in ways that connect emotionally or logically. Often this means providing specific examples. After all, “specificity is the soul of narrative.” In addition, you want to look for ways to make your data relatable.

One of the all-time best specificity examples is the Gun Deaths visualization created by Periscope.

Package for consumption

Finally, how your data story gets delivered really matters. A lot. Some people want the ability to interactively dig into the details. Others just want the key message, and to have it delivered to their inbox. 

• What is the delivery method that will most likely grab your audience’s attention?

• How much explanation and in-person hand-holding is necessary?

• Is the data story a collaboration or a one-way broadcast?

Beyond the delivery method, we have also found that the visual design of your data story is important. If you’re not a designer, how do you make something that looks beautiful, clean, and clear? Start by removing the ‘chart junk’, all the visual elements that distract from the data. Then consider whether you’ve used color carefully, made smart font choices, and used layout to guide the reader.

Where to get started

Those are some of the basic concepts that will get you started toward presenting data as a compelling story. Of course, there is a ton more to learn. That’s why we’ve been creating learning content that we hope will teach a new generation of data storytellers. Here’s where I’d recommend you go next:

1. Learning from the best with 20 outstanding data storytelling examples

2. Dive deeper with our Complete Guide to Data Storytelling

3. Practice your skills with the only build-it-yourself data storytelling platform.