Quick Summary
Data visualization is the process of displaying data in easy-to-comprehend formats. It augments our understanding of data and helps us see the hidden patterns and insights that data wants to convey. It’s an incredible way to understand data better, remove redundancy implied in textual data, and make intricate details easy to digest. CEOs, CFOs, C-Suite executives, and managerial-level personnel use data visualization tools and techniques to save time they would spend on deriving insights from data. However, all who are involved in creating data visualizations from data must be aware of the best practices that lead to the development of effective data visualizations.
What is effective data visualization? Is it about appealing charts? Or is it more to do with information that drives action and understanding? Maybe it’s a mix of both. However, what really makes data visualization effective is how quickly and robustly it can differentiate between confusion and insight. Data visualization powered by the right tools and technologies replaces unprocessed information with valuable business insights. Implemented by the experts, it converts intricate data into visual narratives that convey a story.
Therefore, whether creating a dashboard for executives or an infographic for the general public, data visualization consultation can make a world of difference. In fact, the top analytics platforms like Power BI, Tableau, Qlik, etc., have their own specific guidelines to create the ultimate visual excellence.
This article discusses the 8 best practices recommended by X-Byte Analytics to create effective data visualization for your business. Let’s get started
Importance of Data Visualizations
The brain processes visuals 60,000 times faster than text. This is why data visualization forms the core of data insights, making them easier to comprehend. For many years, businesses have depended on the expertise and gut instincts of senior leadership to drive growth. This changed when organizations started harboring data as a key differentiator. The expansive and decentralized nature of modern enterprises has shifted the dependence on insights and decision-making from senior executives to advanced business intelligence (BI) tools like PowerBI, Tableau, and Sisense.
Further, AI-powered intuitive data visualization helps businesses sustain a competitive advantage as AI technologies and machine learning models offer innovative methods for analyzing and synthesizing data–particularly when it comes to delivering predictive and diagnostic insights.
A Forbes article reveals the 5 commandments of data visualizations.
- First, “Purpose drives the visual,” emphasizing that visualization objectives (distribution, composition, relationship, trend, or comparison) should align with stakeholder needs.
- Second, “Data type determines the selection of the visual,” where nominal, ordinal, or numeric data each requires appropriate chart types.
- Third, “Less is more,” advocating simplicity through high data-ink ratios and appropriate data density.
- Fourth, “Apply consistent scales” to maintain integrity in data representation, using the lie factor metric to ensure accuracy.
- Finally, “Aesthetics matter,” highlighting the importance of proper visual sizing, typography, layout flow, and strategic color usage (following the 60-30-10 rule) to enhance comprehension.
So, if you aim to become an IDO (insight-driven organization), you need to identify what data visualization tools and technologies work best for your industry type and business approach.
Best Practices that Help in Creating Effective and Sophisticated Data Visualizations
1. Identify Your Audience and Purpose
The best data visualization techniques start with a clear purpose and understanding of the audience. So, before jumping to create, ask yourself the following questions:
- What am I trying to say?
- Who am I saying it to?
- What can people learn from my data visualization?
Having clear answers to such questions is vital, as a single creative visualization can have multiple interpretations. For example, a mid-level executive can benefit by having a broader view of the data. In contrast, a CEO would prefer going through the trends at a granular level.
So, knowing the level of expertise that your audience has is integral to the decisions one make. Therefore, it always makes sense to determine the tone and context of a visualization. So, as an analyst, you need to think from two perspectives- as a designer and a consumer. Remember, the visual you create should answer the most pressing queries of your target audience. This is only possible if you design your visualization with intent.
2. Keep It Simple
It’s always tempting to pack a particular chart with multiple data points. However, doing so obscures the key message. Besides, too much information is always overwhelming. Here are a few things you can do to keep it simple.
- Focus on the most vital data supporting your message.
- Remove any element that doesn’t have a clear purpose.
- Steer clear of displaying too much data in one go.
- Work with clean layouts and add breathing space.
- Highlight key insights with concise labels and use straightforward formats.
Remember, simple visualizations are the most impactful visualizations. So, cut through the noise and create something that helps your target audience pick up the message faster.
3. Choose the Right Visualization
Data visualization doesn’t work with a one-size-fits-all approach. So, when it comes to data visualization best practices, matching your chart to the story is the key. This is because different charts have different strengths. Say, a line chart works best to depict trends over a period, whereas bar charts are great for comparing different categories. Similarly, a scatter plot is ideal for highlighting multiple correlations.
Choosing the wrong visualization adds to the confusion of your target audience. This is perhaps best described in the words of industry leader Qlik as they like to call it “make form follow function”. It simply means one must be clear about the metric or relationship that needs to be highlighted. Accordingly, you pick up a certain visual form.
Choosing the right data visualization tool is equally important. Check out our extensive guide on Domo vs Power BI and Sigma data analysis for making informed choices.
However, sometimes, chasing the right visualization means overlooking default choices. For instance, heat maps can work better in revealing patterns than tables. Such conscious choices also mean avoiding problematic visuals. Some industry experts advise against charts misleading the audience, like complex pies and 3D graphs distorting the proportions. So, before choosing a particular type of chart, ensure it’s the simplest and easiest way to communicate specific data points.
Struggling to simplify complex data metrics?
4. Use of Color and Contrast
The success of the visual design is owed to color. When used wisely, it can draw attention and convey the right meaning of your visualization. However, things are easier said than done. To use the colour and contrast in the right way across your visualization requires you to follow certain definite steps, like;
- Always use a color palette that easily highlights the most critical data.
- Stick to one accent color to emphasize data points.
- Use muted tones for background information.
- Don’t use too many colors.
- Match your data series across charts for the same categories.
- Always use intuitive colors to allow natural associations, like using green for positive and red for negative figures.
A University of Berkeley article on the usage of colors in data visualization states that while creating data visualizations, choosing the right color scheme for data is key. Therefore, choose different color schemes for different data types— categorical, diverging, and sequential. For instance, the article suggests not using more than 6 colors for categorical data.
Leading data visualization companies use colours to guide readers to the place that holds the most important data. Choosing the right colours and contrast also means lowering the visual overload. And that’s always a plus for any business using data visualization to drive strategic decisions.
5. Always Prioritize Readability
It’s not what you show in your visualization. It’s how you choose to show it. One of the standout principles of creative data visualization is using predictable patterns for layout. Why? Because our eyes are used to it. For example, in most cultures, people read from left to right. So, placing the most critical data near the top left would make sense. But that’s not enough. To ensure readability, you must :
- Maintain balance and hierarchy
- Arrange all charts and legends in order
- Visually emphasize the most important information (use the correct font size)
- Be consistent with formatting (like labels, axis scales, etc.)
These pointers will help you achieve clarity in layout and ensure your data story is presented in the most logical manner.
6. Always Provide Context
No matter how creative your visualization is, it will always need some context. Despite your audience being intelligent, you should always assume they are seeing the data for the first time. So, always figure out the explanatory cues that you can offer.
Some of the best ways to add context to your visualization include:
- Starting with a clear and descriptive title
- Use the right labeling for the axes, units, and key data points to prevent misinterpretation.
- Adding captions or tooltips that offer additional detail
- Avoiding clutter near the main visual
- Ensuring your visualization has proper baselines or reference points, like for bar charts, there shouldn’t be any breaks or unusual scales.
The ultimate objective of contextualizing your data is to create a self-contained visual. Additionally, it helps the audience find answers to all questions without an outside explanation.
You can read all about it in our detailed guide to data visualization for understanding it better.
7. Say Yes To Storytelling with Data
“Storytelling with data” is quite a buzzword today, and it has good reasons to be so. Instead of a random representation of charts, a storytelling approach allows you to connect the dots to deliver a compelling narrative. Microsoft advises presenting data using a data dashboard for effective storytelling, such as creating a power BI dashboard.
Here are a few steps that you should follow, including a storytelling approach and presenting your data for maximum impact.
- Start by introducing the problem or context.
- Show rising actions and key insights.
- Highlight trends (like a sales spike) and other contrasting segments (like regional performance)
- Use sequencing, annotations, and interactive data visualization elements for a logical progression
Remember, the best practices for visual storytelling with data involve emotions and a sense of discovery. While you strive to be technically correct, it’s also vital that you design your visualization to be memorable. This is the true essence of visual analytics for a business.
Pro Tip: Focus on a single visualization for main insights. For example, if your chart depicts three different data sets, it’s always wise to break it into multiple visuals. That way, you are transforming the data from mere numbers into a vivid narrative and fueling actio
8. Seek Feedback for Improvement
Creating a successful data visualization is quite an iterative process. You will seldom get things done right in the first attempt. So, always be open to refining things. Make it a point to proactively ask for feedback from your peers, real users, and stakeholders. Then, make a note of the feedback points and ask yourself a bunch of vital questions:
- Does the visualization clearly tell the story at a glance?
- Are there any unnecessary design elements that can be removed?
- Are you missing out on any essential insights?
- Is the data accuracy intact?
Once you have clear answers to such questions, you can refine your visualization into a sharper, more relevant, and error-free offering. In actual practice, this would mean a dashboard revision after the executives have used it in their initial meetings. They might also suggest that you update the chart based on the latest data visualization practices. As a designer, it’s your job to stay abreast of the latest technologies and learn from data visualization examples.
Are your current visualizations failing to drive action?
Visual Analytics and Data Visualization: Similarities
Visual Analytics and Data Visualization are related but distinct fields in the data science spectrum. While both help transform raw data into visual formats, they differ significantly in purpose, interactivity, analytical complexity, and application areas.
Aspect | Visual Analytics | Data Visualization | Similarities |
Definition | An analytical reasoning process supported by interactive visual interfaces that combines automated analysis techniques with human-guided exploration | The representation of data in graphical or pictorial format to communicate information clearly and efficiently | Both transform raw data into visual formats to enhance understanding |
Primary Purpose | Problem-solving, decision-making, and discovering insights through interactive exploration | Communication of information and storytelling through static or minimally interactive visuals | Both aim to make complex data more accessible and understandable |
Interactivity Level | Highly interactive with real-time manipulation, filtering, and analysis capabilities | Typically less interactive; often static or with limited interactive elements | Both can incorporate some level of interactivity depending on implementation |
Analytical Complexity | Integrates advanced analytical methods (statistical analysis, machine learning, data mining) | Focuses primarily on representation rather than analysis | Both require an understanding of data structures and relationships |
User Involvement | Requires active user participation in the analytical process | Users are often passive consumers of the visualization | Both consider user needs and perceptual abilities |
Tools & Technologies | Specialized platforms like Tableau, Power BI, and QlikView with advanced analytical capabilities | A range of tools from simple (Excel, Google Charts) to complex (D3.js, Matplotlib) | Both utilize similar visual encoding principles and design elements |
Wrap Up
By now, you must have amassed a good deal of information on how to create effective data visualizations. In essence, it demands an equal understanding of art and science. But things are easier said than done. In a professional scenario, it could be tricky to stick to the best data visualization practices all by yourself. That’s where you need expert intervention.
At X-Byte Analytics, we’ve delivered commendable results for forward-thinking businesses like yours with our dedicated offerings like Tableau consulting services. Besides, there are other occasions when you need to choose the right BI tool, which is, by far, a strategic decision itself.
Over the years, our data visualization team has adhered to rewarding data visualization design principles, helping transform raw data into actionable visuals. In turn, it has helped businesses drive smarter decisions and add value.
So, quit waiting! It’s about time you kickstart your data visualization game and take things to new heights. Get in touch with our experts to learn to unlock the true potential of your data.
