Data Visualization

This guide helps you understand how data visualization can enhance data analysis and facilitate faster, more effective decision making.

What is Data Visualization?

Data visualization refers to the representation of data in graphical formats such as charts, graphs, dashboards, or infographics. This makes it easier for you to see trends, recognize relationships, and uncover data-driven insights from large, complex data sets. These insights can increase efficiency, revenue, and profits for your organization.

Benefits of Data Visualization

Visualizing data is more than transforming it into bar graphs and pie charts. When you choose the right interactive data visualization to highlight the most important aspects of your data, you can illuminate new insights and communicate them more persuasively. And that data storytelling 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 big data in visual and animation 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 tools allow you to interact with all your 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 you easily track the performance of business initiatives by allowing you 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.

Types of Data Visualization

Form follows function. In other words, before you choose a visualization type, ask yourself, what is the function of your chart? What relationships in your data do you want to show?

Here we describe and give examples for 9 functions of visualizations and the corresponding chart types for each.

Change Over Time

The function of time series 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:

Screenshot of a Qlik Sense dashboard showing change over time charts and graphs

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:

Screenshot of a Qlik Sense dashboard showing distribution charts and graphs

Part-to-Whole

This category of charts is best for showing how a single KPIs or metrics 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:

Screenshot of a Qlik Sense dashboard showing part-to-whole charts

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:

Screenshot of a Qlik Sense dashboard showing correlation charts

Flow/Movement

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

Chart types:

  • Chord

  • Network

  • Sankey

  • Waterfall

Screen shot of a Qlik Sense dashboard showing flow graphs

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:

Screenshot of a Qlik Sense dashboard showing ranking charts

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

Screenshot of a Qlik Sense dashboard showing deviation charts

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:

Screenshot of a Qlik Sense dashboard showing magnitude charts

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:

Screenshot of a Qlik Sense dashboards showing spatial charts

Best Practices for Data Visualization

As we stated above, first consider the function of your chart. The Harvard Business Review helps frame this question, suggesting that you determine whether your goal is to declare or explore information and whether this information is conceptual or data-driven.

Using these two parameters as axes, we get the 2x2 matrix below, showing the four main purposes for your visual communication: idea illustration, everyday dataviz, visual discovery, and idea generation.

Image credit: Harvard Business Review

Before you get started with your own visual analytics initiatives, get inspired by today’s best visualization examples. And learn from the grandfather of data visualization, Edward Tufte. In his groundbreaking book, The Visual Display of Quantitative Information, he emphasized clarity, simplicity, and precision to communicate complex information. His tenets hold true today.

And, here are 6 modern visualization techniques:

Know your data

Understand the size and scope of your data, including what types of data 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.

Titles Matter

Compare the visualizations below and see how much more the charts with effective titles help the viewer quickly understand the information.

Generic Chart Titles

Screenshot of a KPI dashboard with generic chart titles such as "sales per year" and "sales share by category"

Descriptive Chart Titles

Screenshot of a KPI dashboard with descriptive chart titles, such as "sales up 6.4% in last period" and "women's wear largest sales category with 38%"

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Data Visualization Tools

From single spreadsheets to complete business intelligence platforms, you have many choices when it comes to data visualization tools.

Data scientists and data analysts often use open source JavaScript libraries and tools such as D3.js, ECharts, deck.gl, and Vega or programming languages such as Python or R for complex data analysis. Open-source or custom data visualization options offer advantages in cost-effectiveness and flexibility but they pose challenges such as a steep learning curve, limited official support, limited templates, integration complexities, and varying feature availability.

For modern data viz tools, the following capabilities provide the most value:

Number 1

Suggest visuals with AI

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

Number 2

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 data sources to give users the full picture.

Number 3

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.

Number 4

Embed anywhere

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.

Clutter

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.

FAQs

What are the 5 forms of data visualization?

The five main forms of visualizing data are:

1. Charts and Graphs: This includes bar charts, line charts, pie charts, and other graphical representations that display numerical data.

2. Maps: Geographic data can be visualized using maps, highlighting spatial patterns and relationships.

3. Tables: Tabular presentations of data provide a structured format for easy comparison and reference.

4. Infographics: Visual representations combining charts, icons, and text to convey information in a more engaging and digestible manner.

5. Dashboards: Interactive displays that combine various visual elements to provide a comprehensive overview of data, often used for monitoring and analysis.

How do I learn data visualization?

To learn data visualization, grasp fundamental statistics and design principles. If you have data science skills, explore popular tools like Python with Matplotlib or R with ggplot2. Or, modern analytics tools simplify the process, offering user-friendly interfaces and extensive resources. You can also utilize online courses like Coursera, Udacity, or Khan Academy for theory and hands-on practice.

How important is data visualization?

Visualizing data is crucial as it transforms intricate datasets into clear visuals, facilitating easier comprehension and decision-making. Visualizations efficiently convey insights, patterns, and trends, leveraging your brain's ability to process visual information. They enhance communication, aid in identifying correlations and outliers, and empower you and your organization to derive meaningful insights from complex datasets. In fields like business, science, and public policy, the skill of presenting data visually has become fundamental, driving informed decision-making and fostering a deeper understanding of complex information.

What is the difference between data visualization and data analysis?

Data visualization and data analysis are distinct yet interconnected processes in the realm of data interpretation. Data analysis involves the systematic examination of datasets to discover patterns, relationships, and insights. It encompasses statistical methods, machine learning, and other analytical techniques to extract meaningful information. On the other hand, visualizing data focuses on presenting the results of data analysis in a visual format, using charts, graphs, and dashboards to communicate complex findings effectively. While data analysis involves the exploration and interpretation of raw data, data viz transforms these findings into visually accessible representations, enhancing understanding and decision-making. Both are integral components of the data-driven decision-making process.

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