Data Visualization
What it is, why it matters, and best practices.
Data Visualization Guide
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.
Given the ongoing rise of big data, effective data visualization is a critical step in transforming messy, 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.
- 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.
- 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.
- 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).
- 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 Types and Examples
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
Distribution
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
Part-to-Whole
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
Correlation
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
Flow/Movement
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
Ranking
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
Deviation
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
Magnitude
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
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
Know your data
Make form follow function
Keep visuals simple
Let users discover freely
Guide users when they need it
Embed data everywhere
Titles matter.
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Generic Chart Titles
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Descriptive Chart Titles
10 Ways to Take Your Data Visualizations to the Next Level
Inspire action with your data. Learn about the latest visualizations and how to choose the right ones to highlight the most important aspects of your data.
Common Data Visualization Challenges
Lack of data understanding
Clutter
Lack of data governance
Reliance on manual processes
5 Data Visualization Pitfalls (and How to Avoid Them)
Learn about the most common challenges in visualizing data, and how to overcome them.
Key Capabilities of Data Visualization Tools
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
Qlik Sense® Self-Service Visualization and Discovery
Compare Qlik Sense® to other data visualization tools and you'll see why Qlik takes data visualization to a whole new level.
- Easily combine, load, visualize, and explore your data, no matter how large (or small).
- Smart visualizations reveal the shape of your data and pinpoint outliers.
- Every chart, table, and object is interactive and instantly updates to the current context with each action.
- Get insights faster with assistance from Insight Advisor for chart creation, association recommendations, and data preparation.