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Data Visualization

What it is, why it matters, and best practices. This guide provides practical advice and examples to help you create best-in-class data visualizations of your own.

What is Data Visualization?

Data visualization refers to the representation of data or information in charts, graphs, maps or other visual formats. This makes it easier for stakeholders to see trends, recognize relationships and uncover outliers in their data. With the rise of big data, effective visualization helps transform massive datasets into a clear, compelling story and actionable insights that can increase efficiency, revenue and profits.

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. Because when you choose the right visualization to highlight the most important aspects of your data, you can illuminate new insights and communicate them more persuasively. And that can result in smarter actions and bigger outcomes for your business.

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: The best data visualization tools allow users to interact with all their data, directly on the chart 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.

Get inspired by today’s best data visualization examples

Data Visualization Types and Examples

The world of data visualization has exploded. From arc diagrams and bullet charts to violin plots and waterfalls, there are more techniques than ever before. Here we discuss how to choose the right format to tell your data story.

Form follows function

Before you choose a visualization type, ask yourself which relationships in your data you want to show. In other words, what’s the function of your chart?

Here we describe and give examples for nine functions of visualizations and the corresponding chart types for each as originally defined by The Financial Times’ Visual Vocabulary. Once you’re clear on the function, you can select a chart type.

9 Functions of Visualizations:

Change Over Time

The function of these charts is to show how data is trending over any amount of time. For example, change over time charts can give insight into a product's sales over the past five years or a stock’s price over the past five hours.

Chart types:

  • Area Timeline
  • Calendar Heatmap
  • Circles Timeline
  • Column Timeline
  • Column-Line Timeline
  • Fan Timeline
  • Gantt Chart
  • Line Chart
  • Scatterplot-Line Timeline
  • Seismogram
  • Slope Chart
  • Stock-price

The function of distribution charts is to show how data is spread across a group. This helps you spot outliers and commonalities, as well as see the shape of your data. For example, public policy officials might want to see the demographic or income characteristics of a certain population.

Chart types:

  • Barcode
  • Boxplot
  • Cumulative Curve
  • Dot Plot
  • Dot Plot Strip
  • Histogram
  • Population Pyramis
  • Violin

This category of charts is best for showing how a single thing can be broken down into component parts. A good example would be if a marketing leader wanted to see all new leads broken out by their source.

Chart types:

  • Arc Chart
  • Bar Stacked Proportional
  • Donut Chart
  • Gridplot
  • Pie Chart
  • Stacked Column
  • Sunburst
  • Treemap
  • Venn Diagram
  • Voronoi
  • Waterfall Chart

The function of correlation charts is to convey relationships between variables. Some charts, like a bubble chart, can represent three dimensions of data, where the size of the bubble is the third value on top of the XY axis values. For example, an executive could see in one chart how sales volume and profit are correlated by country.

Chart types:

  • Bubble Chart
  • Line Column
  • Scatterplot
  • Scatterplot Connected
  • XY Heatmap

The function here is to show movement data or the flow of data between conditions. For example, flow maps show how something like migration happens from one location to another.

Chart types:

  • Chord
  • Network
  • Sankey
  • Waterfall

The function for ranking charts is to show how a list of data points relate to each other. For example, a bar chart in descending order would highlight which salesperson is driving the most sales.

Chart types:

  • Bar Ordered
  • Bump Chart
  • Column Ordered
  • Dot Plot Strip
  • Lollipop H
  • Lollipop V
  • Slope Chart
  • Symbol Proportional Ordered

The function here is to highlight variation of data points from a given baseline. For example, a finance leader might want to visualize an organization’s budget surplus vs deficit.

Chart types:

  • Bar Diverging
  • Bar Diverging Stacked
  • Line Surplus/Deficit Filled
  • Spine Chart

The function of magnitude charts is to convey relative or absolute comparisons in quantity. An example would be if an executive wanted to compare domestic vs international revenues.

Chart types:

  • Bar Chart
  • Bar Grouped
  • Bar Stacked Proportional
  • Bullet Chart
  • Column Chart
  • Column Grouped
  • Isotope (Pictogram)
  • Lollipop H
  • Lollipop V
  • Parallel Coordinates
  • Radar Chart
  • Symbol Proportional

Spatial, or geospatial, charts serve the function of communicating geographical locations and patterns in data. For example, police might want to evaluate crime statistics in different parts of a city.

Chart types:

  • Basic Choropleth
  • Contour Chart
  • Dot Density
  • Equalised Cartogram
  • Flow Chart
  • Heatmap
  • Scaled Cartogram Value
  • Symbol Proportional

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.

Make form follow function

As stated above, be clear on which relationships in your data you’re trying to show before you choose your chart. Know what your audience wants to accomplish, and how you can best help them gain insights.

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 discover freely

Rather than restricting users to a limited drill path, enable them to explore all relationships in the data, so they can get the whole story.

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.
Get inspired by the ten best modern data visualization examples.

Titles matter.

Compare the visualizations below and see how much more the charts on the right with effective titles help the viewer quickly understand the information.
  • Generic Chart Titles

  • Descriptive Chart Titles

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.

Reliance on manual processes

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 Tools

From single spreadsheets to complete business intelligence platforms, organizations have many choices when it comes to data visualization tools. The following capabilities 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.

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