In an increasingly data-driven world, creating compelling data visuals is almost as important of a skill as effective writing. They’re both forms of storytelling, and telling stories is how humans have been sharing ideas for over 10,000 years. Data without data visualization is like reading the dictionary as if it were a novel. Having context and concrete example is what creates insight, evokes emotion, and drives action. The same could be said for data visualization.
Data visualization is more than just painting a pretty picture. It gives users the power to make data more accessible and understandable, even to data-averse employees or time-strapped executives who need insights quickly. Good data visualization is the difference between confusing and capturing your audience—would you rather people fall asleep when you share your data project, or join the conversation?
In this post, we’ll dive into why you need data visualization for more than infographics and scatter plots, and how you can start amplifying its impacts at your organization today.
What is Data Visualization?
If you’re not thinking about data visualization as the art of storytelling through data, you’re going about it wrong. It’s a way to simply, efficiently, and effectively communicate complex data and information through easy-to-understand charts, maps, and graphs.
You’ve heard of line charts, tree maps, infographics, box plots, histograms, bar graphs, and pie charts. All these data visualization techniques aim to do the same thing—allow even the least technical user to see what’s most important in a dataset and ignore what’s not relevant.
Why Data Visualization is Your Secret Weapon
Human brains process visuals 60,000 times faster than they do text. So, not only are data visualization techniques easier to understand, but they also offer a much faster way to communicate and process insights. When presenting findings to stakeholders, for example, they can quickly recognize trends and patterns, allowing your team to communicate value and take follow-up actions promptly.
Data visualizations are also harder to ignore than paragraphs of text. How many times have you skimmed an email rather than reading it word for word? How often do you actually read through an entire news article before losing interest?
Communicating information is more effective with data visualization. High-quality infographics are 30x more likely to be read than plain text, and the Wharton School of Business found that while only half of an audience was convinced by a purely verbal presentation, that number jumped to over two thirds when visuals were added.
So, what impact will this have on your business? Better communication by itself is priceless, but shorter meetings and the ability to make decisions faster than your competition are a bonus. The fact that your entire organization, from business analysts to leadership, will have more conviction in those decisions and ensuing actions is the icing on the cake.
Spice Up Your Initiatives with Data Visualization
Okay, so you’re sold on the value of data visualization (if you weren’t already), but how do you actually use it to improve your business?
Take BI, or business intelligence, for example. Almost every organization leverages BI for data-driven decision making, as it allows users to look in the rearview mirror and see what happened, when, and how. BI uses dashboards, reporting, and scorecards to communicate data, and analysts can recommend further action based on the data displayed.
But, if you’re proposing a BI initiative, like ways to optimize manufacturing processes based on supply chain metrics, how do you present it so that your audience:
- Doesn’t fall asleep
- Actively listens
- Cares and gets invested in what you’re working on
Remember that storytelling we talked about? With data visualization, you can use interactive elements that allow users to play a part in data discovery and interpret results on their own, making them instantly more invested in the project. Engagement is key! And data visualization is an excellent way to give your everyday business problems a major glow up.
Here are a few other examples where data visualization techniques can paint a poignant picture:
- Demonstrating the positive impacts of the marketing team’s campaigns on ROI
- Showing an employee’s improved performance before and after taking a training course
- Tracking progress made on organization-wide KPIs
Future Scope & Trends to Watch
Today, you can use data visualization to tell a story with the data your organization is already collecting. Tomorrow, data visualization will offer more augmented, automated capabilities, making your business smarter, more savvy, and more competitive.
Graph analytics is all about discovering relationships between data. In graph analytics, also called link analysis or network visualization, users can see how different elements relate to each other to explore networks of complex connections at scale.
So, what does this mean for businesses? There are six main types of graph analytics—path, community, centricity, similarity, link prediction, and connectivity—all of which enable users to see the connection, correlation, and causation between data points.
Real-world impact: Government agencies can use graph analytics for investigative intelligence, discovering links between data points that could be key for solving high-profile cases.
In the past (and for some, this is still true today), organizations relied on historical data that was analyzed in batches, making it impossible to get instant insights. Today, technologies like Apache Kafka and IoT have made it easier to capture, stream, and react to data in real-time.
With real-time analytics, data visualizations change dynamically over time—enabling businesses to be proactive, rather than reactive. Say goodbye to static charts and hello to real-time data streams that immediately influence and update the user interface.
Real-world impact: Product managers use real-time product dashboards to maximize revenue through optimized dynamic pricing.
Data Visualization for AI
Imagine if you could use what’s happened in the past to explore and visualize what will happen next. Predictive analytics does just that and allows you to take data visualization to the next level.
Integrations between data visualization and AI tools allow users to create and conceptualize insights about the future, as seen in the integration between data science platform RapidMiner and Tableau. A partnership like this allows users to create predictive models with RapidMiner, enrich existing Tableau dashboards with new data, and promote stakeholder buy-in through powerful digital collaboration.
Real-world impact: Manufacturers can use sensor data to map out a color-coded factory floor and determine which machines are likely to need maintenance. Using data visualization for predictive analytics, the shop floorman can push model results directly to their smartphone and take preventative action with the push of a button.
As computer scientist Ben Shneiderman said, “The purpose of visualization is insight, not pictures.” For data visualization to be successful, it needs to help users—from business analysts to data scientists to C-suite executives—conceptualize what the data says and communicate value across the entire team. With data visualization, everyone can engage in what the data is telling them and participate in the discussion about the data story, ensuring everyone’s invested in the outcome.
Combining data visualization with forward-looking techniques like AI makes the narratives you craft even more compelling. Decision makers won’t just be able to react to what has happened, they’ll be able to easily understand and plan for what’s likely to happen.
Want to see how the partnership between RapidMiner and Tableau is helping users create explainable models and meaningful predictive insights? Check out our joint webinar, Better Together, to see how data experts are creating and visualizing the future.