Your guide to big data visualization

Published Mar 15 2024 Last updated Apr 16 2024 7 minute read
big data visualization
Contributors
  • Sean Dougherty
    Written by Sean Dougherty

    A copywriter at Funnel, Sean has more than 15 years of experience working in branding and advertising (both agency and client side). He's also a professional voice actor.

Data has gotten BIG for every industry, but it's really apparent in marketing. It's not just that you have hundreds of data sources to review before making decisions. Those sources also capture a huge range of data types. Depending on your role, you might encounter quantitative (measurable information represented by numbers) and qualitative (descriptions usually represented by words) data about conversions, customer service interactions, social media impressions, or A/B testing. It goes on and on.

What is "big data," exactly? Data scientists use velocity, variety, and volume (they call them "the 3 V's") to determine whether a dataset counts as "big data." There isn't a concrete definition, but the term certainly pertains to marketing campaigns that collect millions of rows of data. When you pull data points from a dozen social media profiles, your e-commerce platform, your sales team, customer service representatives, and who even knows how many Google Ads campaigns... there's no doubt that you work with big data.

The obvious problem here is that the human brain can't process that much information in a lifetime. That's why data analysts and developers have built tools that simplify everything from collecting information to spotting trends. Even with data analysis on your side, though, it's often challenging to determine how you can use big data to your advantage. 

Data visualization for big data has become essential for marketers and other professionals who rely on data points.

If you're completely new to big data visualization, even the tools people call "user-friendly" can seem impossibly complicated. Don't worry too much. We're going to break down the basics and introduce you to some big data visualization tools to get you started.

Why marketing data is hard to work with

When LinkedIn asked top marketers about why they like their jobs, most of them talked favorably about creative thinking, the thrill of solving problems, and working with interesting people. Metrics came up, but most marketers seem to think of data as a tool that confirms a campaign's success or reveals where the campaign has gone astray. You probably didn't get into marketing because you love math, but it's still part of the job.

The truth is out: marketing is a creative field that relies on effective data and data visualizations. Once you find the right tools to visualize vast amounts of data visualization, you might even decide you like working with numbers. No promises! But it has certainly happened before.

You might also like: All about data visualization

 

So, why is marketing data so hard to work with? It isn't just that marketing professionals would rather focus on creative endeavors. Marketing data really is tough. Here are some reasons why.

So many data points from so many places

The data that shapes marketing campaigns comes from so, so many places. You might pull in raw data from sources like:

  • Google Ads

  • Facebook Ad Manager

  • Google Search Console

Every interaction that existing and potential customers have with your brand creates a piece of information. You get quantitative data from your social media profiles, online ads, videos, and e-commerce stores. But you can't forget about the importance of qualitative data created when people talk to your customer service reps, chatbots, managers, and sales professionals. The more your company cares about customer service, the more structured and unstructured data it collects.

Data analytics and big data visualization can make your job much easier.

Double-counting conversions? It happens

There are several ways for a single conversion to get counted twice. It often happens when information comes from multiple data sources. If someone clicks a Google Ad that takes them to a Facebook page, both of those events could get counted, even though you only wanted clicking the ad to count.

Double-counting can also come from different ad platforms taking credit for a single conversion. The path to a conversion usually includes a lot of touch points, so it's not that the ad platforms are "lying." It's just very easy to get confused when deciding which ad finally converted a lead. It's not surprising that platforms want to take credit for the work they contribute.

Double-counting conversions can even happen within e-commerce platforms. Purchases can get logged twice when a code snippet recognizes the actual purchase and the confirmation page as two separate purchases.

Counting one conversion more than once will obviously throw off your data. Unfortunately, it happens a lot in marketing. There probably isn't a way to eliminate the problem right now, but you can minimize it's negative influence by connecting your data sources to a single source of truth, organizing the information carefully, and removing duplications.

Long buying journeys create complex data

Some consumers convert quickly. They see a product they want, do a little research, and make a decision.

Others take the longest possible route. They'll check out your social media channels, visit a bunch of your landing pages, head over to your competitor's website, and then wait two months before they buy anything.

Every step that person takes creates more data, making your job harder.

It's tempting to ignore these stragglers, but you need to understand why it takes them so long. That's how you optimize your marketing funnels.

Data visualization can't eliminate the headache, but it can provide insights into why those people behave the way they do.

Challenges in data visualization

The deeper you wade into the pool of data visualization, the more often you'll encounter these common issues.

Information overload

Big data is... well, it's big. You're dealing with vast amounts of data. So much information that the human brain can't possibly understand it.

Information overload creates two immediate threats:

  • When you can't get your head around the data, it's hard to know whether your data visualization expresses information correctly.

  • When you have a seemingly endless number of options, it becomes very hard to decide what metrics are relevant to your campaign.

What can you do?

First, learn about the different ways big data visualization techniques work. Is it better to use bar charts or pie charts? What about line charts versus a heat map?

Keep in mind that some visualizations express certain types of data more accurately than others. The wrong choice could mislead your audience.

Second, reach out to others for feedback. If your company has data analysts on staff, ask them to review your work for accuracy. They know a lot more about big data analytics than you ever will. That doesn't mean they always make the right choices, but it gives them an advantage.

Data accuracy and quality

Data accuracy requires reliable sources. You can't track trends or measure performance when your insights come from bad data. Ideally, you want a single source of truth that makes it easier for you to work with big data.

Data inconsistencies can also cause trouble when you try to use a big data visualization tool.

When you have diverse data sources, you can expect to find some syntactic inconsistencies. For example, one source might use MM/DD/YYYY, while another uses DD/MM/YYYY. If you try to smash those data points together, you'll get wildly inaccurate results.

Data cleansing does a tremendous job of solving these problems. The good news is that most ETL (extract, transform, load) platforms can also perform some data cleansing tasks.

Diverse data types

Your research might include structured, unstructured, and semi-structured data. You might need to work with qualitative and quantitative data.

With so many diverse data types, you need a reliable way to integrate information.

An ETL tool can do much of the heavy lifting for you. Still, you could run into issues when using diverse data types with some big data visualization techniques.

Opportunities in data visualization

Let's move beyond the challenges of big data analytics and visualization. It's time to talk about the opportunities data visualization offers marketing teams and their colleagues.

Visualization can make data analytics interesting!

Typical pie charts, line charts, bar graphs, and other visualizations are just so basic. It's hard to feel excited even when they communicate positive news for your business.

Advanced visualization techniques can bring data to life.

Some options to explore include:

  • Interactive charts that let viewers find and compare the data that matters to them. (In this example from data journalist Talia Bronshtein, viewers can hover over sections of the graph to access more information about immigration trends.)

  • Dynamic visuals that add value to what the data says. (Visual Capitalists uses dynamic visuals in its History of Pandemics infographic to show how viruses throughout history have affected human populations.)

  • 3D graphs that add a whole new dimension to data visualization. (Check out NASA's Eyes on Asteroids map to see how 3D graphs let viewers move through complex information.)

  • Animated charts that help data tell its story. (These animated charts from Flourish use movement to show how global temperatures change between 1850 and 2020.)

If you want to get really fancy, look for ways to integrate violin plots, cluster maps, joint histograms, and bivariate density plots into your presentations.

Remember, though, that big data visualization isn't about looking cool (although it's worth considering how a pleasing appearance will influence your audience). Always prioritize accuracy. If advanced big data visualization techniques look awesome and communicate information accurately, your brand will stand out as an industry leader.


Get machine learning involved to find deeper insights

You know the dread you feel when you see a huge list of numbers? Machine learning loves it. Or at least we imagine that it does. It's just so good at using big data to predict trends and recognize patterns!

Machine learning can digest data in real time to create more accurate predictive models. As new information becomes available, machine learning solutions can update their data visualizations to reflect changes. You can sit back and watch the visualization evolve as more data gets added to the predictive model.

You can also use machine learning for pattern recognition. Human brains are really good at some types of pattern recognition. It's partly why we see human faces everywhere, even on the surface of Mars.

When it comes to big data, though, our brains don't have a chance. It's just way too much. Machine learning's pattern recognition abilities, however, excel in this area.

Machine learning solutions won't blink twice at a terabyte of data. Sure, they won't blink at all. They're computers, after all. The point is that they can process massive amounts of data, create visualizations, and spot patterns humans don't notice.

Better data exploration through collaboration

Most of the top big data visualization tools, like Miro and Google Sheets, let teams collaborate on projects. Cloud-based tools let all members of your team work together regardless of their physical locations. If they can access the internet, they can contribute.

It's a good idea to get more people involved, too. Big data visualization tools do excellent work, but they can't think creatively and critically like humans. Getting human eyes on the data visualization can help uncover biases that make it harder to reach your goals.

More people looking at data visualizations also means you get more interpretations of the data. Data doesn't tell you how to proceed. It gives you insight into how you might proceed. The decision always falls to you and your colleagues.

What are the most common big data visualization tools? 

Big data visualization tools play a crucial role in interpreting and presenting large datasets in a comprehensible way. Some popular tools include:

Tableau

A powerful and widely used tool for interactive data visualization and business intelligence.

Power BI

Microsoft's business analytics tool enables users to visualize and share insights across an organization.

QlikView/Qlik Sense

Qlik's associative data modelling allows for flexible exploration and visualization of complex datasets.

D3.js

A JavaScript library for creating dynamic, interactive data visualizations in web browsers.

Looker

A platform that combines data exploration, analysis, and visualization in a unified environment.

The choice of a specific tool depends on the nature of your data, your organization's requirements, and the desired level of interactivity in visualizations.

You can read this blog post to learn more about some of our favorite data visualization tools.

Would you like to see how Funnel can improve your approach to big data visualization? Get a free demo to experience the benefits of Funnel.

Want to work smarter with your marketing data?
Discover Funnel