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MarTechExec takeaway

Data visualization is so much more than infographics or basic bar charts. It takes these visuals to the next level, combining them with the creativity and goal-oriented thinking that only the best marketers have.” — Lana K. Moore, Executive Editor at @MarTechExec


Data visualization is the process of translating data into a visual form, like charts, plots and infographics. This makes large amounts of data easy to understand, regardless of the audience consuming it.

Guiding principles

You can skim our research, but don’t skimp on your work. Here are our juiciest data visualization tidbits to keep handy come your next project:

Think like a journalist

Practice data journalism best practices. You don’t have to have the most groundbreaking data in the world. You just need a solid set of accurate data that can be organized to tell a strong story.

Recognize your innate bias

Always have a second pair of eyes to review your data visualization before publishing it anywhere. You may even want to create a makeshift focus group to ensure your audience “sees” what you want them to.

Take advantage of data mania

We’re practically drowning in data these days. For the best ROI, recycle your data visualizations. Much of the data used in one design can be reused in a slightly altered version — either for a different media or another stage of the customer journey.

What is data visualization?

Data in, visuals out — That’s data visualization in a nutshell. But simple as it sounds, the concept of data visualization is often confused or combined with similar tactics.

That’s because martech doesn’t exist in a nutshell — It’s more like a towering walnut tree, with buzzwords and lingo entwining every limb.

To stay clear on the enigma that is data in martech, jot down the following terms:

  • Data science — The field of study that includes subsets like math, engineering, and computer science. Data scientists are multitalented and increasingly sought out for martech teams.
  • Business Intelligence (BI) — The practice of analyzing and presenting data for internal use. Its goal is to help executives make better business decisions by allowing data to guide the way.
  • Data journalism — The semi-new facet of journalism that uses data visualization to support a story, argument or idea.
  • Infographics — Visual representations of information (sometimes copy, sometimes data, sometimes both). Infographics are a type of data visualization, but the term “has become the broadest descriptor” of visual content.

What does data visualization look like?

It’s important to note that data visualization comes in all shapes, sizes and specificities. Less is not always more, bigger is not always better and pretty doesn’t always equate to practical.

To illustrate this point, let’s look at the polar opposite ends of the data visualization spectrum.

What data viz designers should aim for

Great data visualizations say a lot with little noise. In other words, the data is organized in such a way that the takeaway speaks for itself. And anyone — whether right-brained, left-brained or a hybrid of the two — will effortlessly grasp what it’s trying to say.

Example: Based on a True True Story?

This data visualization featured on Information is Beautiful dissects popular “based on a true story” movies by looking at how factual each movie’s scenes truly are.

With a quick glance at the key in the upper left corner, the message of the data visualization is instantaneously clear: Hollywood is pretty fictitious.

Save for special cases like Selma (100 percent true? Really?), the silver screen films we know so well aren’t all that knowledgeable in themselves.

(I wonder how this data would compare to each film’s Rotten Tomatoes score. Someone please make that happen.)

What data viz designers should avoid

As one of history’s greatest logicians, Albert Einstein possessed a keen understanding of — and appreciation for — data in all its forms.

He also made it clear that those who present data need to know it best, and “if you can’t explain it simply, you don’t understand it well enough.”

In a blog post outlining data visualization strategies, Avinash Kaushik mirrored this point. Oftentimes, he said, data viz is complicated by designers “trying to demonstrate how clever they are.”

Whether for a lack of knowledge or an excess of pride, designers are spitting out messy, muddled data visualizations left and right.

Example: Dog Names in New York City

NYC.gov created an interactive data visualization representing all its licensed dogs, organized by name into one gigantic bubble chart.

As a viewer, I’m intrigued. I played around with the data visualization for a few minutes and came to the life-altering conclusion, “Hm. That’s cool.”

Cue exit page left.

While I can’t deny this data visualization is visually striking, and I applaud whoever had the gumption to assemble it, its takeaway is unclear.

I would’ve loved to see this data visualization accompanied by a blog post or video with related info — say, how licensing your dog helps prevent them from going astray in the high-traffic, dog-unfriendly streets of NYC.

Top it off with an explicit call to action, and this data viz would’ve been good as gold.

Why does data visualization work?

Like interactive content, the effectiveness of data visualization is in part due to the lovely and dependable human brain.

We’re wired to respond to visuals quicker, and thus make decisions more easily, compared to, say, “rows upon rows of numbers” or other text.

“Humans rely on visual information to understand the world around us. We want to see numbers turn into something more meaningful.” — Dmytro Moroz, Digital Marketing Strategist at Kanbanize 

read more from Dmytro

What’s more, our preference for and dependence on visuals is reinforced as we go through school.

Data journalist John Burn-Murdoch wrote an article for The Guardian that elaborated on this point, explaining that in the classroom, text is most often analyzed, whereas data visualizations are observed. The former is treated as subjective, while the latter is treated as indisputable truth.

And this constant conditioning of consumers’ minds makes them prime receptors for data visualization in marketing.

Why marketers love data visualization

While our audience’s love of visuals definitely gives our data viz an edge, the benefits of this tactic don’t stop there.

Data visualization boasts numerous benefits over other forms of marketing — both text-based and similar visuals.

Street cred

Research shows that U.S. customers have an equal amount of trust for broadcast media (i.e. TV, radio and film) as they do for print and social media. Worldwide, the average isn’t far off.

While we could cite this as evidence that modern media is reigning supreme, the research notes that “even social media channels” are subject to “increasing skepticism.”


For marketers, this translates to an imminent need to create relevant, engaging and credible content — in part to keep our edge over broadcast channels, but also to avoid drowning in the social media sea of “fake news.”

Data visualization, by its factual nature, increases brand credibility. And it may also increase conversion. Research shows that people are about 3X as compliant when following directions that combine illustrations and text versus directions with only text.

“With the rise of data visualization, consumers are increasingly looking for the proof in the pudding. They feel more relieved when a marketer includes infographics or charts to back up any claims.” — Robb Hecht, Adjunct Marketing Professor at Baruch College

read more from Robb

Data parade

Thanks to our ever-increasing access to open data, the building blocks for data visualization are just clicks away.

“We are living the era of big data. From individuals to governments, there is a movement toward sharing data for public good.” — Kristen Sosulski, Speaker, Professor at NYU Stern School of Business, and Consultant at KristenSosulski.com

read more from Kristen

ColumnFiveMedia even assembled a list of 100 free and legitimate data sources for marketers to use — from Pew Research and UNICEF to Gallup and Google Trends.

For marketers, free and open access to research data means that we spend way less of our own resources on first-party data, which only 32 percent admit is “very accurate” anyway.

But as data viz journalist Jane Pong explains, the data free-for-all causes a small problem for marketers: Competition is fierce.

With everyone given equal access to the same data, Pong insists “we have to create our own datasets” in order to “stand out and be unique.”

Flexing the facts

If you don’t have the means to collect new data, your next best option is to illustrate existing data in a new way.

Luckily, the “insights gained from data stories” can flex to fit a range of different content formats — and, as a result, different stages of the customer journey.

For example, let’s say you work for a B2B company that sells collaboration and productivity software to marketing teams. Consider how you could use "The Daily Routines of Famous Creative People" in each stage of the customer journey to promote your product.

To drive awareness (stage 1), you could create a quiz based on the data visualization that asks “Which famous creative person are you?” Depending on their answer, you’d direct the user to a particular product that aids in their area of need.

To drive consideration (stage 2), you could incorporate the data visualization into a testimonial page that compares the daily routines of your customers — before and after introducing your product into their lives. Your own personal hall of fame, so to speak.

To drive decision (stage 3), you could build a case study around the data visualization to explore how productivity impacts creative outcomes.

As a bonus, you could even break down your clients into their respective teams and “identities” (based on your stage 1 quiz) and match them against the testimonials in stage 2.

To drive loyalty (stage 4), you could create a rewards program based on your clients’ individual and team progress. Reference back to the data visualization by naming reward levels based on the highest-achieving names on the chart — starting with Mozart, onto Angelou, etc.

Why marketers hate data visualization

To be quite honest, the aforementioned tasks are much easier said than done.

Creating even a great data visualization requires more than just an eye for detail and love of design. For this reason, and the ones below, marketers hesitate to give data visualization their all.

User (t)error

According to eMarketer, 43 percent of technology executives site “resistance to change” as the top challenge to implementing new digital tools.

But even teams that are more inclined to experiment with new tech can have a tough time actually implementing it. eMarketer also found that employee training alone can take up more than 20 percent of marketers’ time.

As this area of the martech landscape grows, though, there’s hope that a rise of user-friendly tools will mean smoother and quicker adoption of data viz solutions.

“The [winning vendors] will be the ones who can meet user demand with the widest variety of integrations across the most popular platforms while also giving users the most creative freedom.” — Wes Marsh, Director of Digital Marketing at Solodev

read more from Wes

“As the legacy providers continue to face pressure from [new tools], we expect to see them adapt to this changing landscape.” — Sam Underwood, Vice President of Business Strategy at Futurety

read more from Sam

“We hope to see a lot more competition in the future as data viz technology gets easier to implement for the non-technical user.” — Gil Gildner, Co-Founder at Discosloth

read more from Gil

Obviously not objective

We’re all inherently biased to a certain degree — so much so that we need a cognitive bias map just to keep ourselves accountable.

As noted by Huffington Post writer Bahar Gholipour, these biases “can even bypass well-established rules and lead to disasters ― from the loss of lives in Mount Everest expeditions to the global financial crisis in 2008.”

And while marketing is a practice of twisting the facts, to a degree, that doesn’t excuse us from taking responsibility of how true, untrue or semi-true our data visualizations appear to be.

Even technologies like AI can’t totally take subjectivity out of the mix. That’s because the algorithms AI works from are “based on human inputs, and human inputs can be fundamentally flawed.”

“What's really important to note is how AI in any data-intensive application is driving user design and how data gets displayed and visualized. If you have more data, you have to tell the machine in more detail what you want to get out of it.” — Tuomas Rasila, Chief Technology Officer and Co-Founder at Vainu

read more from Tuomas

Too little talent

According to eMarketer, 55 percent of business professionals worldwide feel the talent gap in their company is only growing by the day.

Want to hear worse news? 44 percent of brands and 39 percent of agencies believe the talent gap will be a concern in 2018 — making it the second top concern among those surveyed.

But truth be told, their concern is justified. According to IBM, demand for data scientists will increase 28 percent by 2020.

Innovation Enterprise’s Jake Hissitt admits the talent gap “is going to be a key challenge for the creation of decent data visualizations.” Luckily, this demand creates an opportunity for aspiring martech execs to polish their skills and expand their horizons.

“While many of the new jobs will be filled up by data analysts and data engineers, there is definitely a lot of space for non-technical subject-matter experts to join the field.” — Olga Tsubiks, Data Visualization Consultant at OlgaTsubiks.com

read more from Olga

The future of data visualization

All prior pros and cons considered, the future of data visualization will definitely be dynamic. How much so, exactly?

Let’s leave it up to the experts to answer that.

Here’s what our martech gurus had to say about how data visualization will evolve in the next 3-5 years.

Virtual Reality (VR)

With VR, consuming data visualization will become a totally immersive experience.

According to eMarketer, 48 percent of U.S. customers said they would use VR to virtually browse a store. 36 percent said they would use it to virtually try on clothes before buying.

Just imagine if each virtual item was also accompanied by a data visualization showing its ratings by different customer traits — like “20 percent of users who bought XYZ Product also like this.”

Hey, if Alibaba can give China virtual access to the Macy’s in NYC, then I’d say just about anything is possible.

“Virtual reality has great potential to help with visualising complex data and can literally put consumers right in the centre of the visualisation.” — Greg Wolejko, Head of UI Practice at Cognifide

read more from Greg

“[Our] analytics will [soon] be tailored or personalized for the specific user by leveraging machine learning to ‘know’ what data you, as a user, are most interested in.” — James Huddleston, Senior Director of Product Marketing at Certain Inc.

read more from James

Augmented Reality (AR)

AR will work in a similar vein to VR, as it “creates a new dynamic” in how marketers streamline data visualizations into users’ everyday lives.

But unlike VR, in which the user is engulfed by a single, detached world, AR creates a hybridized version of the user’s real world by layering in scattered bits of virtual content (as seen in the graphic above).

“The Internet will wrap around us and we'll increasingly see kiosks, panels, surfaces and VR/AR simulations supplying us with real time data visualizations.” — Robb Hecht, Adjunct Marketing Professor at Baruch College

read more from Robb

From radio... to TV... to IoT

Before we explore any new reality, we first need to make sure our data visualizations are up to the job.

This means all of our charts, graphs and other data viz formats will need to adapt to an array of devices — from smartwatches to VR headsets to AR projections and beyond.

“2018 will bring more mobile-specific analytics solutions, not just interfaces for existing software. Developers will focus on customizable dashboards and visualizations that make the most of smaller screens.” — Humberto Farias, Chief Executive Officer at Concepta

read more from Humberto

There’s no time to waste, either. Studies show that users are gaining interest in connected devices across the board, which means we’d better start prepping our data visualizations to work with their changing lifestyles.

“Data visualization is becoming extremely important. Gartner predicts that 95% of new products by 2020 will have an IoT element to them.” — Eric Ebert, Communications Manager at Zenkit

read more from Eric

“Data is constantly created by people, machines, IoT and other devices but it's only useful if it can be gathered, made accessible, interpreted and analysed for swift action.” — Greg Wolejko, Head of UI Practice at Cognifide

read more from Greg

Current trends in data visualization

A number of data visualization trends and tactics are already setting us up for the aforementioned changes.

Here are the most significant to note.

"Martech, party of 3D?"

Marketers are already pushing data visualizations into the 3D world. NASA did it. Google Maps did it (the image below shows the tool’s multiple views — 2D in the middle and 3D on each side).

3D is no stranger to data viz, either. And though 3D technology has suffered some abuse from amateur designers in the past, anyone who dismisses 3D entirely either “isn’t familiar with the benefits” or “hasn’t been using the right tool.”

“Marketers will soon be able to click on any point in a graphic, and then take an instantaneous marketing action based on the data point.” — Juan Manuel Hayek, Co-Founder and Chief Technology Officer at Datagran

read more from Juan Manuel

Stop, drop and pin

Speaking of maps, it should come as no surprise that marketers love location data (No wonder we’ve already created dozens of data visualizations based on it).

“In the same way that a user-flow report in analytics might show you how visitors interact with your website, indoor maps can contextualize large sets of location data to illustrate how a physical space is being used.” — Chris Wiegand, Chief Executive Officer at Jibestream

read more from Chris

Location data is gift that keeps on giving. That’s because you can use your target audience’s location data to create relevant, timely, targeted data visualizations… and then feed those visuals right back to them.

For example, this data visualization from MapBox took location data from Twitter to show the presence of locals versus tourists in 29 cities around the world.

Marketers can easily use this data to fuel a number of data visualization projects. A restaurant, for instance, could show the concentration of tourists in its area as a way to boost its reputation as a vacationer’s must-see.

Simple and snackable

Minimalism is the name of the game nowadays. Marketers are using simpler content types, like GIFs and short videos, as data viz vessels.

This presents a challenge in AR/VR, as I talked about before, and marketers will have to get creative. Both the design and delivery of our data visualizations need to feel natural to the virtual environment they’re in.

Ironic, I know.

But once we do start applying adaptive design… once we are ready to dive head-first into virtual and augmented worlds… the simple, snackable content we create now will be ever the easier to deliver.

“There has been a push for more short-form visual content. These bite-sized pieces of content are great for gaining insight quickly, grabbing people's attention, and enhancing memorability.” — Nadjya Ghausi, VP of Marketing at Prezi

read more from Nadjya


In a time when “complex” content is an understatement and abundance is the norm, data visualization may be more valuable than ever before. It’s an experiment in simplicity and an expression of ideas. And because of this, it takes quite the multitalented marketer to make it work well.

We can only master it by working smart — through planning, analysis and an almost nonhuman attention to detail (though, how human are marketers anyway?).