What is Data Visualization? Benefits and Techniques

Data visualization is when data is represented through visual depictions, making it easier for people to digest.

It transforms complicated statistics into intuitive charts, graphs, and maps to help businesses share information and spot important details and trends.

Data visualization can transform standard figures into eye-catching, simple visuals, making complex insights accessible and engaging.

Relying solely on spreadsheets or numbers makes it challenging for you and other viewers to grasp data meaningfully.

Data visualization turns dense and confusing data sets into intuitive snapshots that are easy to understand. You can quickly interpret the story behind the information and uncover critical insights, patterns, and trends.

With clearer insights unlocked, your teams are better equipped to spot potential problems, see opportunities for improvement, and track your progress on specific objectives.

You can pick between many data visualization types.

The following are widely used, and you can combine visualizations to tell a complete story about your data.

Scatter plots

Scatter plots use dots on a graph to display where points cluster between two variables. They help visualize if there’s any type of connection, which can help you analyze potential relationships.

Line charts

Line charts track changes over time. The line helps you spot upward and downward trends quickly, so you aren’t relying on complicated tables to see growth trajectories or dips.

Pie charts

Pie charts transform data into “slices” representing different percentages of a whole. They give you an easy-to-grasp picture of how categories compare.

Histograms

Histograms show data distribution using rectangle “towers” of different heights. They help you spot areas of concentration and outliers.

Treemaps

Treemaps display hierarchical data using nested rectangles. The section size shows proportions, making it easier to see what groups make up the biggest chunks.

Bar charts

Bar charts use horizontal or vertical bars to compare categories across one or more metrics. They are handy for effortless comparisons, such as comparing regional revenue side-by-side.

Heat maps

Heat maps use color intensity so viewers can quickly spot the hottest (most popular) and coldest (least popular) data points. They’re a great way to track activity clustering to see user preferences.

Data visualization is used across every industry and function.

Infographics, dashboards, and charts enable diverse teams to grasp key takeaways faster through “data storytelling” than traditional spreadsheet analysis.

For instance, marketing teams can use data visualizations to:

• Analyze customer segmentation trends
• Monitor campaign performance
• Make the most of different assets based on engagement data

Sales organizations use visualization tools to:

• Track revenue over time
• Compare regional performance
• Forecast future growth

Operations and finance teams apply it to visualize KPIs like order volume, inventory, budgets, and operating costs to inform strategic decisions.

Data visualizations also improve communication, enabling teams to conveniently share their findings with leadership teams and stakeholders.

Beyond business, effective data visualization now plays a pivotal role in advancing fields like science, economics, and resource management by making large, complicated issues easier to conceptualize.

Look at any successful organization—they’ll likely rely on engaging, perceptive data visualizations to shape their raw data and guide decisions. It offers numerous benefits that empower businesses and drive growth.

Easily share information

Data visualizations make complex insights easy to understand and share through simple yet compelling graphics.

Interactive review and exploration

Unlike static reports, interactive data visualizations support dynamic filtering, drilling into details, and manipulating views to uncover insights. Dashboards enable you to visualize and explore data relationships better than tables alone.

Visualize opportunities and patterns

Visualizing the data through graphs instead of raw figures makes spotting trends, relationships, and outliers easier. Data visualization also helps surface opportunities and risks that can stay buried in tables.

Improved decision-making

Formatting critical information into consumable visuals speeds up and enhances business decisions. Visualized data makes analytics more accessible, encouraging data-informed strategy over gut instinct.

Can simplify complex data

Visualizations like shapes, placement, and colors turn data into understandable snapshots. Careful visual representation helps viewers grasp essential points to guide better decisions.

Real-time monitoring

Data dashboards enable real-time tracking of business activities through flowing, visualized metrics. This enables quick response to emerging trends, risks, or opportunities—something static reports don’t offer.

Storytelling techniques

Visual storytelling techniques turn datasets into compelling narratives. Charts present situations, characters, and events, while annotated infographics plot context, actions, and outcomes to simplify complexity. Shape and color add meaning that is lacking in basic tables.

Efficiency

Visual data enables faster information absorption, pattern recognition, and decision-making. Making data easier to understand helps businesses quickly analyze and review it, boosting productivity while minimizing confusion.

Predictive analysis

Data visualization helps predict the future by spotting historical insights and potential trajectories, enabling solid, data-backed forecasts.

Data visualizations can face challenges that impact how effectively they communicate information.

Understanding these common pitfalls can help you address issues head-on and take steps to overcome them.

Using the wrong chart type

Selecting the wrong chart type can lead to confusion and misinterpretation. Understand the nature of your data (e.g., categorical, numerical, or time series) and choose a chart that effectively communicates your information.

Insufficient data

Incomplete or missing data affects accuracy. Ensure you have enough relevant data for the visualization and handle missing data properly by putting in more values or clearly showing where there isn’t any data.

Poor presentation and design choices

Cluttered or overly complicated visualizations make it difficult for the audience to understand the information. Keep visualizations simple, using suitable colors, labels, and legends, and prioritize clarity over elaborate or “fun” designs.

Data visualization bias

Data selection, analysis, or presentation biases can result in the wrong conclusion. Be transparent about data sources and methodology, giving viewers context for accurate interpretation.

Updating and maintenance

Outdated visualizations can give you misleading information, especially in dynamic data environments.

Update visuals regularly with the most recent data, automate when possible, and communicate the data’s date to users.

Creating compelling visualizations comes with a few best practices. You’ll want to ensure they’re visually appealing and convey meaningful information to your audience.

Understand your audience

Tailor data visualizations to your audience’s expertise. Match the complexity to their technical familiarity, balancing clarity for general viewers and depth for experts. Gather information about their background and interests to adapt your approach accordingly.

Be conscious of your design

Opt for clean and focused designs in data so information can quickly sink in. Avoid busy graphics, prioritize intentional visual groupings, negative space, and minimal non-data ink. Keep visuals straightforward, highlighting only meaningful data streams.

Tell a story with your data

Effective data visualizations act as a narrative, guiding the audience from start to finish. Craft a story to provide context, emphasizing the message or insight you want to share. Structure your visualization logically, drawing attention to critical points and relationships.

Make it accessible

Ensure accessibility in visualizations for all audiences, including those with disabilities. Use alt text, clear labels, and screen reader compatibility. Choose colorblind-friendly palettes, consider font and text sizes, and be mindful of cultural meanings in visual styles and graphics.

We’ve long used data visualization to explore information and share our findings—they’re by no means a modern technique!

From healthcare to city planning, these data visualization examples demonstrate how eye-catching, insightful visuals help us understand (and improve) the world around us.

John Snow’s Cholera Map

In the mid-19th century, an esteemed English physician, John Snow, created a map to visualize the cholera outbreaks in London. By plotting cases on the map, he spotted a cluster around a contaminated pump in Soho, helping to establish a link between cholera and contaminated water.

NASA’s Eyes on the Solar System

NASA’s Eyes on the Solar System is an interactive 3D visualization tool that enables viewers to navigate the solar system and different space missions in real-time. It offers a captivating way to understand the positions and movements of celestial bodies.

Uber Movement

Uber Movement is a platform that shows anonymized and aggregated traffic data. It provides insights into city transportation patterns, helping urban planners and policymakers make data-driven infrastructure and traffic management decisions.

Data visualization usually falls into four categories: descriptive, diagnostic, predictive, and prescriptive, aligning with the different types of data analytics.

These categories aren't strictly exclusive and often overlap. Users regularly move between these techniques while exploring and analyzing data for insights and decision-making. Your approach depends on the context, nature of the data, and analysis goals.

Descriptive

Descriptive visualization presents the data in a way that summarizes and describes vital characteristics, patterns, and trends.

Many organizations use heatmaps and bar, line, and pie charts to help viewers understand what’s happening.

Diagnostic

Diagnostic visualization digs deeper into the data to understand why specific patterns or trends exist. It tries to uncover the causes behind observed events.

Drill-down dashboards, scatter plots with trend lines, and interactive data explorations are helpful here. They enable users to investigate relationships and correlations in the data.

Predictive

Predictive visualization forecasts future trends based on historical data and patterns. It uses statistical models and ML algorithms to make predictions.

Time-series forecasting charts, regression analysis plots, and decision trees are standard tools. These visuals help us understand possible future scenarios based on what’s happened in the past.

Prescriptive

Prescriptive visualization goes a step further by providing recommendations on actions to take based on data analysis. It informs decision-making by suggesting the best action to reach a desired goal.

Decision trees, optimization charts, and scenario analysis dashboards are often used here. Control elements like slider bars can manipulate decision variables so you can instantly see multiple projected outcomes.

They guide the audience to make quick, informed choices by highlighting the most effective strategies.

Amplitude is a digital analytics platform that visualizes product usage metrics to maximize user experiences.

It helps organizations understand their customers’ behavior, providing features to create behavioral cohort charts, retention graphs, and conversion funnel visuals that guide product direction.

Amplitude helps you visualize your data to gain valuable, reliable, and up-to-date insights that inform your strategic decisions.

Create charts and dashboards to reshape your approaches and ensure they align with ever-changing business goals and customer needs.

With Amplitude, you can:

• Focus on behavior insights: Amplitude visualizes user flows through products with our robust behavioral cohorts and user journey analytics. Spot drop-off pain points in conversion funnels and compare behaviors across segments.

• Build custom dashboards: Create custom dashboards to monitor critical metrics and visualize data trends. Customize your dashboards to show the most relevant information for your team, making it easier to track progress and make informed decisions.

• Compare performance: Visualize KPIs across key variables like device type, geographical location, and marketing channels. Double down on what works while fixing poor performers.

• Collaborate and share insights: Share your dashboards with team members, fostering a culture of data-driven choices by encouraging collaboration and knowledge sharing.

• Integrate with other tools: Integrate Amplitude with other tools your organization uses, such as data warehouses, CRM systems, or marketing tools, to give you a holistic view of user behavior and business performance.

Make the most of your data and visualize the findings in a way that works best for you. Contact Amplitude today or browse our template library for inspiration.