Data Visualization

What it is, why it matters, and best practices.

What is data visualization?

Data visualization enables people to easily uncover actionable insights by presenting information and data in graphical, and often interactive graphs, charts, and maps. The best data visualization transforms messy, massive data sets into discussions, understanding, and well-informed decisions. By creating visualizations that don’t just present data, but tell a clear, compelling story, you can explore, explain, and express critical information and make intelligent decisions that can have a big impact on your business.

How data visualization can benefit your business

Data visualization is more than transforming data into graphical formats. It’s an essential capability within an organization’s business intelligence (BI) strategy. The most effective visuals tell stories that can help you better understand your data, identify and share new opportunities and insights, and respond to market changes faster.

Data visualization allows for faster decision-making because you can understand the story your data tells you at a quick glance.
  1. Faster decision-making: By viewing and manipulating large data sets in visual formats, you can understand the story your data tells you at a quick glance, rather than poring over piles of numbers and tables for hours or weeks.
  2. More data exploration: Modern data visualization tools allow users to interact with data in context to discover hidden patterns, see data relationships and uncover actionable insights — all without IT support.
  3. Better track business initiatives: Dashboards help people easily track the performance of business initiatives by allowing them to quickly see how everyday operations affect key performance indicators (KPIs).
  4. Extend your analytics investment: Because visuals make it easier to understand data, everyone in an organization — including business users — can explore data and find insights that improve company growth and effectiveness.

Data visualization examples

Organizations across every industry and sector use data visualization to help them better understand industry-specific data sets and business contexts. Here are just a few examples.


Retailers use retail data visualization within retail analytics applications to better understand customer buying behavior, analyze market share, explore performance optimize and launch new store locations and more.

Learn how retailers can use a visual representation of data to analyze consumer browsing data.


Financial institutions use financial data visualization within financial services and banking analytics applications to gain insights on sales performance, profitability attribution, branch performance, credit pipeline forecasting and more.

Discover how banks use advanced techniques such as spatial analysis to explore loan portfolio performance.


Visualization of healthcare data within healthcare analytics applications can help organizations in this industry better understand clinical variations, revenue cycle management, labor productivity, patient readmission risk and more.

Discover the top 12 healthcare insights powered by visual analytics.

Additional industry-specific examples

Data visualization types

From simple bar charts and histograms to sophisticated map-based analysis, data can be visualized in many different ways. But not every format works for every data story. Here’s a quick overview of the four most basic types and how they’re best used:


This type of data presentation helps you identify highest and lowest values, compare recent and older values, and recognize trends. Comparison visualizations give insight into which products sell best, or how this year’s sales compared to last year. Examples include bar charts, line charts and circular area charts.

Comparison visualizations identify highest and lowest values, compare recent and older values, and recognize trends.


This presentation type allows you to see the changing relationship among data points over time, or see the relative difference between parts of a whole. These help you understand your market share size, or analyze where you spend your budget. Examples include pie charts, stacked area charts or stacked bar charts.

Composition visualizations show changes among data points over time, or see the relative difference between parts of a whole.


This type of data presentation helps you spot outliers and commonalities, as well as see the shape of your data. For example, you might uncover insights about the number of customers in a specific range, or customer payment trends. Examples include bar histograms, line histograms and scatter plots.

Distribution visualizations help you spot outliers and commonalities, as well as see the shape of your data.


This type shows correlations and clusters, and helps you see outliers. These insights uncover how advertising spend and sales are correlated, or variations between expenses and revenue across regions. Examples include scatter plots, or scatter plots with different bubble sizes.

Relationship visualizations show correlations and clusters, and helps you see outliers.

Best practices for data visualization

Before you get started with your own visual analytics initiatives, review these best practices to ensure you’re successful right out of the gate.

Know your data

Understand the size and scope of your data, including what kind of information you want to communicate, and the kinds of decisions you want people to make.

Understand your audience

Find out what your audience wants to accomplish, and how you can best enable them to take action on the insights they uncover.

Keep visuals simple

Size up your data and determine the visual technique you should use to present your story in the simplest way possible.

Let users dig in to the data

Interactive data visualizations enable users to explore and analyze directly within the visualization itself. This helps them uncover insights which lead to better, data-driven decisions.

Guide users when they need it

Help users explore data in a guided way by giving them access to the data that’s most relevant to their analysis, without requiring them to ask for it.

Embed data everywhere

From websites and portals to apps and business processes, let users visualize their data anywhere they make decisions.

Common data visualization challenges

While some tools can help you make better decisions and track business performance, there are some significant challenges you need to look out for.

Lack of data understanding

No matter how pleasant your visuals appear, if the underlying data doesn’t tell the right story, users won’t get value from them. To avoid telling incomplete, misleading or inaccurate stories, understand your data first. And be sure to spot and resolve any data issues before you publish.


Trying to cram too much data into a visual can leave users confused and frustrated. Instead, limit the number of KPIs in your dashboard, use pie charts for limited data sets, choose colors carefully, and use the simplest format possible.

Lack of data governance

While many people feel comfortable using spreadsheets and ungoverned analytics tools to create their own presentations, this presents many challenges. Implement proper data governance practices to avoid inaccurate data stories, incomplete analyses, and non-standard visuals.

Lack of data governance

When users create visualizations by manually manipulating data in spreadsheets, they can make data and mathematical errors, waste hours of productivity, and distribute improper information. AI and machine learning can help you automate time-consuming tasks and overcome these challenges.

Key capabilities of data visualization software

From single spreadsheets to complete business intelligence platforms, organizations have many choices when it comes to visual analytics tools. However, it is the data analytics platforms that include the following capabilities that provide the most value:

Present data in the optimal context

The best tools offer the flexibility to visualize data in the most relevant and intuitive formats and can combine data from multiple sources to give users the full picture.

Facilitate data discovery

Static charts and linear drill-downs stop short of answering user questions about their data. Tools should allow users to freely explore data in whatever direction their intuition leads them.

Suggest visuals with AI

Data analytics tools use augmented intelligence to recommend visualizations that can help even novice users build their own analytics views and discover hidden insights.

Embed anywhere

People need access to visual representations of data and insights no matter which application they’re using. The best tools make it easy to embed analytics wherever people are working.

Learn more about data visualization.


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Take data visualization to a whole new level with Qlik

Take data visualization to a whole new level with Qlik Sense.

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