Effective data visualization is an important aspect of creating impactful and actionable reflections of key metrics in today’s business environment.

Our five key takeaways are:

  • Complex data sets are made more valuable for decision-makers by applying data visualization best practices when preparing such visual assets as graphs, charts, and interactive dashboards

  • Adhering to core data visualization best practices, such as clarity and simplicity (especially when working with Big Data), results in more effective, naturally intuitive visual assets.

  • Intelligently integrating data from sources throughout your organization provides a comprehensive starting point for presenting effective data visualizations.

  • Effective data visualization approaches using thorough and reliable categorical data can help you avoid common challenges resulting in distorted data values and analyses and hindered decision-making. Innovative data visualization techniques and integration methods can help expand data collection for more accurately informed visualizations.

  • One of the most important data visualization best practices is consistent data hygiene. Applied throughout your organization, ensuring clean and complete data helps maintain integritous visuals.

This piece explores data visualization best practices and core principles, from integration to analysis, and the skills you need to create stunning dashboards, breathe life into your KPIs, and discover the secret to designing data visuals that shine.

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Disparate datasets are like tiny lumps of coal: pretty worthless on their own. But when you put them through a well-structured ETL (Extract, Transform, Load) process, the data integration process reveals diamonds in the rough. View these rough-cuts through the lens of data visualization tools, and these jewels shine like the precious nuggets of insight they are.

Effectively communicating these insights so everyone who needs to can understand what the data represents, however, can be tricky. Visual representations allow professionals across domains—like data scientists, data engineers, CTOs, VPs of Data, data analysts, BI experts, and software engineers—to spot trends, identify opportunities, and make well-informed, data-driven decisions.

Understanding Data Visualization

Data visualization refers to representing data, often complex data sets, through visuals like charts, graphs, spreadsheets, and interactive displays. It goes beyond the basic readability of typical bar or pie charts and leverages sophisticated techniques that uncover valuable insights within your data.

You choose metrics and KPIs to measure every quarter, but the results aren’t always easy to understand—especially for non-technical members of your team. The goal of visualization is to communicate complex data findings in a visual format that intuitively represents your data. Well-designed visuals tap into the powerful data processing capabilities of the human visual system, which is our brain’s visual information processing hub. 

When we view anything, our eyes “ingest” the scene and send that information to our visual cortex for decoding. These images are then “digested” or transformed before being loaded into the part of our brains responsible for spotting trends, patterns, similarities, and nonconformity; all of which present us with the opportunity to highlight our evolving understanding of data and how we approach decisions.

Data visualization is like biological ETL, and has progressed just as fast as the explosion in data and technological innovation. Today’s data visualizers use advanced tools and platforms to create robust interactive displays, three-dimensional and animated visuals, virtual and augmented reality data representations, and more.

But they can’t do so effectively without adhering to some basic principles.

Core Principles of Effective Data Visualization

Having a data integration and visualization tool—even one with a multifunctional dashboard—doesn’t mean your input will result in the van Gogh of data visualization output. For your data to truly shine in visual representations, you need to follow a few guiding principles.

Clarity and Simplicity

The most fundamental principle for sound data visualization is clarity; the visual display and main takeaways or insights should be immediately clear to the viewer. Prioritizing simplicity in both design and messaging is key. The most intuitive visuals use basic shapes, universal colors, and elements that focus attention squarely on the data’s meaning.

Matching Visuals to Data

The type of visualization used should suit the specific data type and intended message. Consider whether a bar chart, scatter plot, pie graph, geographic map, or more advanced option would best highlight the key data stories and connections. Form should follow function when it comes to data visuals.

Function and Aesthetics

It’s important to balance aesthetics with functionality when designing data visualizations. Attractive designs engage viewers and lend credibility, but stylistic elements should never overpower or obscure the data’s core story. Well-designed infographics, for example, use color, hierarchies, and white space to strategically pull the viewer’s eyes toward the next chapter, guiding the visual journey.

Data Integration for Comprehensive Visuals

Robust data visualizations require inputs from reliable, comprehensive data sources. Data integration via data pipelines using ETL/ELT processes is often necessary to aggregate relevant data from unrelated systems in an organization. These data pipelines typically load the required data to a data warehouse to create a single source of truth for both reporting and data visualization. Establishing scalable pipelines with continuously flowing data nourishes your dashboards to prepare visually pleasing and effective visuals for analysis.

With clean, integrated data pipelines creating your single source of truth in your data warehouse, modern data visualization platforms like Tableau and Microsoft Power BI sit on top of this single source of truth and enable users across skill levels to create rich interactive visuals on their own with drag-and-drop ease—no coding skills necessary. 

Data integration with Integrate.io’s strong pipeline-building capabilities aids teams when creating their single source of truth - consolidating vital data sources, extracting raw data, and transforming it for useful insights—all of which lead your team to successful visualizations.

Expert tip: Your data integration partner can help ensure you apply consistent data cleansing practices across the board. Regular data validation, data cleansing, and data standardization methods help clean up errors, even out inconsistencies, and, ultimately, ensure reliable visuals.

Advanced Techniques: Data Visualization Best Practices

Fundamentals like clarity and simplicity never go out of style, but visualization innovators today are testing the limits of what’s possible in the industry in hopes of discovering even deeper and more interactive insights.

Advanced techniques like 3D visuals and animations introduce unique benefits. An animated data story incorporates movement to illustrate the changes in comprehensive data over time. 3D graphs add depth, allowing users to approach a representation from multiple angles for a deeper understanding.

Virtual reality (VR) and augmented reality (AR) take visualizing data even further through multi-dimensional experiences facilitated through headsets and other sensory technology. These frontline applications are gaining in popularity—most notably in public data consumption in the form of infographics, data science facilities, and even at museums hosting data sculpture installations.

However, these modern contributions should never compromise on delivering clarity or accuracy when using data to communicate. 

These data visualization best practices can ensure the integrity of your data visuals:

  • Prioritize simplicity: If you’re convinced you need flashy visuals for your presentation, ensure your data speaks louder. Visuals should highlight meaningful insights clearly. If it’s not central to your data presentation, forgo decorative filters, gaudy color palettes, and other design features that limit interpretability.

  • Check for bias: Advanced visualizations shouldn’t guide viewers to predetermined conclusions or obscure uncertainties. Review your visualizations for bias prior to unveiling to confirm the accuracy and truly impartial representations.

  • Focus on utility: Your visualizations should answer real questions and meet true business intelligence needs. Creativity has its place, but that’s not typically in a stakeholder meeting.

Common Mistakes to Avoid When Designing Visuals

Data visualizations done well clearly illustrate the core stories your data communicates. When visuals aren’t well-designed, your data doesn’t get the opportunity to share its narrative. If certain mistakes are made, your data won’t say anything at all.

Some of the most common visual design faux pas include:

  • Cluttered visuals with excessive or overly stylized text, data, and imagery make it tough to discern your core message.

  • Inconsistent scaling, ambiguous or tone-deaf labeling, unnatural color combinations, or other elements could confuse or mislead readers.

  • Neglecting visualization accessibility options, such as screen readers and text-based alternatives, excludes people with vision impairments, neural disorders, and other conditions.

Learning visual design best practices and understanding tomorrow’s techniques today can help your organization meet the future’s potential challenges head-on.

So, what does the future hold for data visualization tools and platforms?

Future Trends: 4 Predictions in Data Visualization

While it’s challenging to predict anything in the world of data with unwavering accuracy, consider the following predictions about data visualization’s future as close as it gets: 

Prediction #1: Enhanced Collaboration

Data visualization platforms continue advancing in lockstep with accelerating data analytics capabilities and pivoting to meet evolving user needs. Key trends just over the horizon involve expanded customization options, more flexible collaboration methods, and improved consumption tactics.

Prediction #2: Expanded Customization

More customizable templates and white-label options emerge for enterprises to craft proprietary branded visuals. Self-service analytics features empower more users to develop basic charts and graphs independently.

Prediction #3: Flexible Collaboration

Visualization, analytics, and business intelligence tools and platforms become more collaborative, fostering greater integration capabilities and unified insights. Teams can more intuitively explore company data through tech advancements, such as conversational analytics.

Prediction #4: Improved Consumption Methods

Consumption formally meets automation through interactive displays, smart(er) dashboards, and embedded reporting, bringing data-driven insights to more of your staff, and fostering more actionable decision-making company-wide.

See the Future of Data Visualization Today with Integrate.io

Creating world-class data visuals attuned to the needs of modern businesses requires a dedication to clarity, accuracy, aesthetics, and utility—all while avoiding the most common challenges the industry presents. Data visualization of any type requires a clean and standardized single source of truth dataset. Integrate.io powers the data pipelines for 100s of companies in order to give their teams the data they need to make data-driven decisions. 

Integrate.io offers enhanced data preparation, pipeline integration, and other resources to ensure your data is clean, authoritative, and valid—the very foundations of compelling visualizations. Sign up for a free 14-day trial to experience our versatile integration platform and unleash the power of your data. 

Wondering how our platform can work for your specific use case? Schedule an intro call to arrange a custom demo tailored to your organization’s unique data environment and data visualization goals. Our data integration experts look forward to showing you how to optimize your stack, make the most of your data’s story, and use it to share valuable data insights.


What are the key components of effective data visualization?

The key components of effective data visualization are:

  • Clarity

  • Simplicity

  • Alignment between visuals, data types, and messaging

  • Balanced aesthetics and functionality

  • Meaningful insights.

  • Accuracy

  • Objectivity

How does data integration enhance data visualization?

Data integration via pipelines aggregates, validates, and transforms data from multiple systems and sources to create comprehensive, reliable data streams. This consolidated data delivers dashboards with improved details resulting in broader, deeper insights. Data integration presents timely and thorough data visualizations.

What are some common mistakes in data visualization?

Overly customized visuals accomplish the opposite of what an organization is striving for—valid, valuable, and easy-to-understand results.

These are some of the most common data visualization mistakes:

  • Excessively clustered visuals that obscure key takeaways.

  • Design elements that inhibit correct interpretations of your data, such as ambiguous labeling, inappropriate color palettes, and overly stylistic fonts, can alienate viewers, such as those with vision impairments.

  • Biased visuals hinder your intended meaning and can influence a viewer’s conclusions.

When you prioritize a show over the depth of the content, meaningful insights you might have shared are lost—you lose the opportunity to share a key metric that tells your story. Data visualization tools with highly customizable features are enticing—but don’t use all the available features at once just because you can! 

For satisfying visuals with clear information, aim for an Excel spreadsheet appearance rather than a Michelangelo experience. You don’t need a bright chart—you need the right chart.

How can I choose the right visualization technique for my data analysis?

Consider the data type—do you want to reflect something numeric, geospatial, time-based, relational, or something else? What insights do you want to highlight?

Then match the appropriate visualization methods to represent your metrics’ insights intuitively, whether that’s line charts, line graphs, maps, animations, or another data storytelling method.

What are the future trends in data visualization?

Key data visualization trends include:

  • Increased customization and white labeling options

  • Self-service analytics features

  • Deeper collaboration across and between data platforms

  • Conversational interfaces

  • Automation

  • Interactive displays and dashboards 

…all of which help you create visuals with shareable, accessible data insights.