# data[].attributes.body
This a google drive link

# data[].attributes.body
To learn about Data Visualization

# data[].attributes.attachment_link.description
My creative type is Visionary, and we're known to be charismatic and expressive. To discover your Creative Type, take the quiz now.

# data[].attributes.body
Complete a worksheet on different types of data visualizations.

# data[].attributes.attachment_link.description
Join the SWD community to practice and be supported in your journey to better data storytelling. Are you interested in improving your personal data visualization and communication skills? Join our community to connect with others, share your work, practice, and give and receive feedback.

# data[].attributes.body
Why is data visualization important in interpreting complex data?

# data[].attributes.body
For those needing extra help, offer additional worksheets. For advanced students, propose extension activities like creating interactive visualizations.

# data[].attributes.attachment_link.readable_text
"A diagram is a diagram. Don't cheat me," says a heated Massimo Vignelli during an AIGA/NY event earlier this year. It's been over 30 years since the Italian designer's New York subway map was axed, but it's still a passionate issue.

Vignelli's now-classic New York City subway map was first introduced in 1972, following his work on the signage system in the late 1960s. Inspired by London's Underground map designed by Harry Beck in 1933-which, in turn, was inspired by electrical circuit diagrams-Vignelli simplified New York's complex subway system into a clean graphical system. "A different color for each line, a dot for every station. No dot, no station. Very simple. The whole map is designed on a 45/90 degrees grid with geographic distortions to accommodate the lines," recalls Vignelli in .

Today, the Vignelli map has a near-cult following-with vintage maps selling for upwards of $200 on eBay, a place in the collection of the Museum of Modern Art, even a designer dress. In 2008, an updated version was commissioned by Men's Vogue magazine to raise funds for charity.

A striking piece of graphic design history, the map also had its shortcomings. As designer Michael Bierut points out:

The result was a design solution of extraordinary beauty. Yet it quickly ran into problems. To make the map work graphically meant that a few geographic liberties had to be taken. What about, for instance, the fact that the Vignelli map represented Central Park as a square, when in fact it is three times as long as it is wide? If you're underground, of course, it doesn't matter: there simply aren't as many stops along Central Park as there are in midtown, so it requires less map space. But what if, for whatever reason, you wanted to get out at 59th Street and take a walk on a crisp fall evening? Imagine your surprise when you found yourself hiking for hours on a route that looked like it would take minutes on Vignelli's map.

We spoke with Paul Shaw, author of Helvetica and the New York Subway System, back in February on the topic.

"Most designers-if blogs are to be believed-prefer the Vignelli map. But the Vignelli map is terrible if you actually want to use the subway. I know this from my first years in New York and I have heard similar comments from others who remember trying to use the map when it was introduced," says Shaw. He points out a scene from The Warriors in which a gang trying to get from The Bronx to Coney Island on the subway look at Vignelli's map and find themselves totally confused. "The problem is not that Central Park is a square rather than a rectangle. It is that the locations of some stations are geographically incorrect and that of others misleading in relation to their physical presence. The pair of stations that bothered me back in 1977 and still do with Vignelli's 2008 revision of the map are South Ferry and Whitehall Street. They are much closer than the map indicates, so close in fact that in 2009 they became one station."

In 1979, Vignelli's linear map was replaced with a more traditional-style topographical map. Designed by Michael Hertz, the map evolved from subway-only to include inter-modal transportation like buses and ferries. "Since 1997 or so it has been The Map-the Hertz-style map with lots of pop-up boxes to indicate connections to other forms of transportation such as trains, buses and ferries. The back of the map which used to show each subway line in a Vignelli-like manner as route strips now has a commuter map of the New York metropolitan region. The emphasis now is on the entire MTA (which is much more than the subway)," explains Shaw.

So what does Mr. Vignelli think?

"I think the real reason is space. But not because Manhattan is too small, it's because they want to put too much information that doesn't belong in the diagram. That's why. All of a sudden there is ...and there is no reason. I mean, all you want to know is [how] to go from A to B," explained Vignelli at Navigating the Labyrinth, an AIGA/NY event hosted in February.

While it has been criticized for being geographically inaccurate, this was by design. "On purpose we rejected any visual reference to nature or landmarks," Vigenlli told Men's Vogue in 2008. "People expected a map instead of a diagram. But diagrammatic representation is common practice around the world since the London Underground map of the thirties."

Vignelli had actually envisioned the map as a four-part complimentary system: the stylized map providing the basic route information; a geographic map showing the surface and the relationship between the subway and the city; a neighborhood map providing information of the area around the station and surface connections; and a verbal map to provide instruction on how to reach a destination from a particular station.

All New York subway maps have tried to convey all the information on a single map, with the result of making semantic overlaps, and a very fragmented and visually unpleasant map. The only way to provide clear information is to have both an abstract system map and a geographical map as separate complementary maps. One on one side, the other on the opposite side. Easy.

- Vignelli From A to Z

Easy or not, the complimentary maps were not widely implemented. The verbal map, for example, was only implemented from one station while the neighborhood map was implemented in a few stations.

The perfect solution?

"I would love to see a subway map with the elegance of Vignelli's and the practicality of Hertz's. That is the Holy Grail," says Paul Shaw. "But until then I prefer the messiness of the current map."

This summer, New York City's Metropolitan Transportation Authority unveiled its first major subway map redesign in over a decade. Is today's new subway map the Holy Grail? Far from it, says Shaw in a follow-up discussion.

"The new map looks essentially like more of the same. The colors have been tweaked to be more harmonious overall. However, the green that has replaced the sand for the background reminds me of baby food. A more serious complaint is the unnecessary addition of shadows to the individual route lines. They serve no purpose and can be mistaken for an extra gray route running in tandem with other routes (which is what my wife initially thought was happening). This is dumb design."

Searching for the Holy Grail

Is it possible to get everything on one map, and have it look good at the same time?

New York-based design duo David Heasty and Stefanie Weigler of Triboro Design make it work-and they do it all in one single color. Triboro's one-color map builds upon the Vignelli-style map, retaining all of the content from today's map including the service guide, the city streets, and the various bus and ferry connections.

One goal was to try to make the landmasses and geography more true to life than the current MTA map and establish a stronger sense of typographic hierarchy... Initially we tried making the subway lines very strong-which is a typical feature of most subway maps-but this made the map extremely difficult to decipher. The solution to the problem was counter intuitive. We decided to deemphasize the subway lines, making them so light that they almost disappear. The stops then come to the foreground and viewers can connect the dots (so to speak) with their eyes, tracing and revealing individual subway lines. I wouldn't say that our one-color map is easy to use but we think it is surprisingly functional.

As beautiful (and functional) as this map is, it's not likely to replace the official MTA map anytime soon. "The goal was not so much to try to create an alternative to the official map but instead to tackle an interesting design challenge and emerge with an object that we would be happy to hang on our wall. We chose florescent red as the color because...how could we not? We wanted it to look outrageous!" explains Triboro.

Perhaps a more viable solution is Eddie Jabbour's Kick Map. The Kick Map aims to resolve the 50-year debate between diagrammatic and topographic mapping, with a design that combines the best of both worlds. Unlike the Vignelli map, where stations and lines are distorted for pure graphic harmony, the Kick Map attempts to be geographically accurate while still standardizing angles whenever possible.

The Kick Map is designed to get more people to ride New York City's subway system... A well-designed map not only welcomes and empowers novices to use the subway but also encourages additional use for regular 'home-to-work-only' commuters to use the subway for recreational destinations where they might otherwise take a car. For this reason the design of the subway map can directly influence ridership numbers and can indirectly have an effect on New York's traffic congestion and pollution. In short, a better-designed subway map will make our subway system more open and accessible.

Jabbour's map even caught the (brief) attention of the MTA in 2007, but was soon dismissed for being geographically inaccurate. "He's a good designer and it's an interesting map," MTA executive director Christopher Boylan told the New York Times. "The design is important, but the thing we're concerned with is the best directional guidance. We design a map for use, not solely to look good..."

So the debate continues, underlining a struggle designers continue to face. Does Mies van der Rohe's mantra 'Less is more' hold up or does the consumer, especially in the information age, need more? On the other hand, it's 2010 and with technology like Google Maps and mobile apps (including some which even tells us which subway car to get on) perhaps the printed subway map is a thing of the past?

By Skylar Challand

# data[].attributes.attachment_link.description
"A diagram is a diagram. Don't cheat me," says a heated Massimo Vignelli during an AIGA/NY event earlier this year. It's been over 30 years since the Italian designer's New York subway map was axed, but it's still a passionate issue.

# data[].attributes.body
Find a real-world example of data visualization and write a short analysis of its effectiveness.

# data[].attributes.attachment_link.readable_text
A part of any strong revenue operations team is the dependence on data and data visualization to support decision making within your go-to-market teams and business. And while investing in reliable data is critical, it's just as important to invest in the mechanisms that allow you to interpret and understand what the data is telling you.

Having spent the past decade supporting revenue leaders within sales, marketing and customer success, I've witnessed the growing reliance on having strong data visualization to support both short and long term business planning. No wonder why the market for data visualization tools is set to grow to $10.2 billion by 2026.

Investing in the right technical skills and data visualization software can provide your organization with the leverage to acquire, retain and grow customers in the long run.

What Is Data Visualization?

Data visualization is both the art and science of representing large and sometimes complex data sets visually. For some, this involves excel spreadsheets while for others who are further along, it involves interactive reporting and dashboards through comprehensive BI tools.

Data visualization lessens the reliance on summarized text and emphasizes the important brain function used to understand images. From a physiological perspective, reading text is rather inefficient for us, as the human brain processes images 60,000x faster than text. This is partly why infographics are so popular for digesting information.

Within revenue operations, creating efficiencies in how we understand, interpret and action on data is critical. And this is why we see many B2B SaaS organizations invest in growing the business intelligence functions that are responsible for measuring business performance.

Visualization such as charts, graphs and diagrams are great tools in turning raw data into expressive visuals that can tell a story. These visuals serve as powerful solutions for conveying information and facilitating making important decisions at all levels.

The Benefits Of Data Visualization

Within RevOps, data visualization has countless use cases. Data visualization can be used to forecast revenue, measure marketing effectiveness, or understand the overall health of your customer. Although these use cases are many, I found that they are primarily centered around five very important benefits received when you make the investment in robust data visualization.

1. It Provides Accessibility: Information needs to be shared and remain accessible for employees within your organization. Formatting data into an easy to understand manner can help drive alignment between teams. Everyone's role has some level of dependence on data which makes data accessibility an important benefit of data visualization.

2. It Helps Draw Comparisons: Data visualization is often used to compare data points. Visual images and representations make it alot easier to spot trends, differences and similarities. Visuals like bar charts or line graphs are great examples commonly used for comparing data sets.

3. Describing Relationships: Revenue operations professionals must be able to articulate and translate the relationship different data points have within your business. A simple example - what is the relationship between your sales team's order values and your sales team's average sales cycle. With the help of data visualization, describing relationships becomes easier.

4. Recognizing Patterns: Humans are great at detecting patterns through visualization. In RevOps we use data visualization to highlight both patterns and outliers within data. Visualizing patterns makes it easier to spot trends, anomalies and themes that would be much harder to articulate through just text.

5. Data Storytelling: As a revenue operations professional you are very close to the data within your organization. It is not only our role to maintain and aggregate data, but to also inform stakeholders what the data is telling us. Data visualization is a critical component to effective storytelling. Learn to use data visualization to share your point of view and story while also enabling other GTM leaders to effectively storytell and also share their own narrative.

10 Real World Data Visualization Examples in Action

Now that we have unpacked what data visualization is and the realized benefits in investing into it, I'd like to take you on a visual journey through some real life examples of different data visualizations.

Here are 10 real world examples of both business and non business-related data visualization examples for your viewing pleasure.

Column Chart: Apple's iPhone Quarterly Review

Column charts are unique in that they are simple to use and understand. A column chart is the representation of numerical data displayed as columns with varying width or height. Take for example this column chart displaying iPhone Quarterly Revenue for Apple from 2014 to 2022.

When to Use a Column Graph

A column chart could be used to track changes over time intervals just like in the image above. These charts can also be used to compare data across different categories or groups.

These chart types are great at highlighting differences and visualizing rankings, making these charts effective in presenting discrete data such as frequencies, count or percentages.

Waterfall Chart: A History of the European Union

Within Revenue Operations, I've seen waterfall charts frequently used to forecast sales performance within specific periods. For example using these visualization types to forecast sales pipeline within a specific time frame.

Waterfall charts are useful in displaying a host of data beyond sales and sales pipeline. This real life example is a waterfall chart that displays how the EEC/EU region grew and shrank throughout the years up until Brexit.

When to Use a Waterfall Chart

Use this chart to show the breakdown of a whole number. These visualization types make it easy to understand how specific variables impact a specific value over time.

Line Graph: Tesla Vehicle Delivery History

A line graph (also known as a line chart) shows trends and progress over a specific time. Take for example this real life illustration of Tesla's Vehicles Delivery History since 2016. This line graph is clearly illustrating the exponential growth of the Model 3 and Y while a steady decline in the delivery of Tesla's model S and X.

When to Use a Line Graph

In my view, line graphs are best used to illustrate trends and as well as showcasing comparative differences. Within Revenue Operations, you can use these graphs to highlight sales trends over a period of time. Add in multiple lines to showcase specific products and how their sales are also progressing or declining.

Scatter Plot: Populations in the World

A scatter plot is a graph type that allows you to see the relationship between two variables. These graph types are also used to reveal distribution trends as well.

In the real life example below you can see the comparison drawn between Birth Rates and Death Rates for different countries. The different colors represent geographical regions while the size of the dots display population size.

When to Use a Scatter Plot

Scatter plots are great for displaying multiple data points just like in our example above. They are best used to show relationships between these various data points. This graph type is also helpful in visualizing distribution patterns such as outliers you may have within your data analysis.

Pie Chart: Spending Benchmarks for SaaS

A pie chart shows how specific categories can make up part of a whole number. Generally, a pie chart displays numbers in percentages and the total sum of all parts needed to equal 100%. In my example below, you can see a pie chart that illustrates spending benchmarks for SaaS companies with more than $20 Million in ARR.

When to Use a Pie Chart

Pie charts make it easy to see how your data is segmented. You can think about leveraging pie charts when looking to segment customer demographics, look at product sales, or even analyzing customer churn data.

Mekko Chart: S&P 500 by Sector

Also known as a Marimekko chart, this type of chart is a visual representation that uses stacked bars of varying widths to represent categories of data. In our example below, you can see how the top S&P 500 stocks fair in terms of size both relative to their industry as well as the index as a whole.

When to Use a Mekko Chart

Mekko charts are great at illustrating things like growth, market share or even doing a competitive analysis. With that said, use Mekko charts when you want to emphasize scale or differences between groups of data.

Funnel Chart: Website Conversions

Funnel charts are excellent for viewing a series of steps and progression through a path. Commonly used to measure the marketing and sales processes and conversion rates, funnel charts are great at visualizing a linear journey. Take for example this funnel below that is measuring website conversion metrics.

When to Use a Funnel Chart

Use funnel charts in your data visualization when you have 4-5 stages of sequential data to display. These charts are very versatile especially for marketing and sales teams as they can be used to report on a wide amount of data such as sales pipeline, website conversion and/or marketing campaign performance.

Bubble Chart: Tech Markets in North America

Bubble charts are an extension of scatter plots and are used to measure the relations between three numeric variables. In the case of our example below, you can see visualization that representings the technology markets in North America broken down by major cities.

When to Use a Bubble Chart

Bubble charts are really helpful in visualizing the relationship between different variables. Within RevOps, bubble charts are great business indicators as they can help examine the relationship between related variables like cost, margins, and price as an example.

Heat Map: Risk Level for Contracting Covid 19

A heat map shows the correlation between two variables and provides a scale of grading such as high to low. For example, this heat map below is a visual representation of the risk level for contracting covid 19 by county within The USA. Americans in red areas are at a higher risk while Americans in green are considered to be at lower risk.

When to Use a Heat Map

There are several different use cases for this type of chart. Heat maps can be used to understand population density or to visualize your customer base. They are great at displaying a more generalized view of numeric values especially when working with large data sets.

Stacked Bar Chart: Famous Writers' Productivity

Last but not least is one of the most common visualizations I've personally seen being used to measure sales performance often. Stacked bar charts (also known as a stacked bar graph) are great for visualizing how a group of data moves from one column to the other. An uniquely interesting display of this is below in our example. See how each famous writer's productivity varies as it also segmented this further by literary type.

When to Use a Stacked Bar Chart

Stacked bar charts are a good option for measuring sales performance over a given time frame and segmenting by sales team. They help in visualizing outliers in historical trends while also comparing your strategy with your overall performance.

Bringing The Picture Together

Business that want to align their people and strategy should go beyond basic data visualization techniques and begin to invest in robust types of data visualization going forward. With the growth of powerful visualization tools like Tableau, Looker and Domo, it no longer requires a team of data scientists to transform raw data into beautiful data creations that measure KPIS and business performance.

Make sure you tune your visualization to support the needs of your target audience and remember to make the visualization part of the story you are looking to tell. The best data visualizations are ones that are easy to understand by all who wish to interpret it (and not just data science experts), so don't overcomplicate it. Interactive data visualization can also be a useful tool to keep everyone engaged.

Enjoyed this article? Be sure to follow The RevOps Team on social media, and subscribe to our newsletter to get the best of all things revenue operations straight to your inbox.

# data[].attributes.attachment_link.title
10 Real-Life Examples Of Data Visualization In Action

# data[].attributes.attachment_link.description
Discover 10 data visualization real-life examples, from Apple's column chart quarterly review to Tesla's line graph of delivery history.

# data[].attributes.headline
The 30 Best Data Visualizations of 2024 [Examples]

# data[].attributes.subject
The 30 Best Data Visualizations of 2024 [Examples]

# data[].attributes.attachment_link.readable_text
Data is beautiful - it can inspire, improve lives and bring out the best in people. To keep you inspired, we've gathered the best data visualizations of 2023.

The chosen works cover a variety of topics from Covid-19 healthcare to environmental issue statistics and futuristic LIDAR data graphs.

With over 4.54 billion people using the Internet in 2020, we're sure to witness even more amazing data visualizations next year. For now, get ready to dive into 2023's best data visualizations.. Enjoy your flight of imagination!

But first, check out other top data visualizations in our previous collections from and .

Quick Read

NASA's Eyes on Asteroids is a good data visualization example that provides a great user experience. The design is simple and intuitive, making it easy for users to navigate the site and find what they're looking for.

The History of Pandemics is an infographic that presents a visual timeline of every known pandemic and includes information on how many people were affected, where it spread and what caused it.

Void of the Memories is the rarest data visualization on this list. It's a great combination of calligraphy and data visualization that tells the story of human memory and experience.

The search for dark matter is one of the most important scientific questions in physics today, and this infographic, "The Search for Dark Matter," serves as a great introduction to the subject.

Enhance your data storytelling skills and creatively showcase your data by signing up for Visme's data visualization tools.

1 Nasa's Eyes on Asteroids

If you are interested in exploring data visualization topics in space exploration, check out this striking data visualization created by NASA.

NASA's Eyes on Asteroids is one of the best data visualizations due to its exceptional design and functionality. This interactive visualization allows users to explore the asteroid belt and see the real-time positions of asteroids in our solar system.

The design of this visualization is highly engaging and visually stunning, with a sleek and modern interface that is easy to use. The visualization features a 3D solar system model, allowing users to zoom in and out to explore asteroids and other celestial bodies.

One of the key features of NASA's Eyes on Asteroids visualization is its real-time data feed, which provides up-to-date information on the positions and trajectories of asteroids. This feature makes the visualization highly informative and relevant to current events, allowing users to track potentially hazardous asteroids and see their projected paths over time.

2 Selfiecity - The Science of Selfies

Selfiecity is an innovative and engaging data visualization project exploring the selfies world. It uses a variety of visualizations to analyze selfies from five cities around the world.

They collected over 120,000 selfies from the five cities and selected nearly 1,000 photos from each town. After collecting the images, they analyzed various metrics such as demographics, poses, moods and features.

The project then revealed exciting insights into the culture and social behavior of the people who take selfies. For example, the project shows that women take more selfies than men and that people tend to take selfies in public places rather than private spaces.

The study was quite complex and yielded valuable insights, which presented a challenge when it came to sharing the results. However, the team did an excellent job creating visually appealing data visualizations to present the information.

3 The Ancient Seven Wonders of the World

The civil engineering feats of humankind have reached the highest peaks of the mountains and deep into the ocean, and we have built pyramids, temples and statues that are still standing today.

The seven wonders of the ancient world are a collection of man-made structures that are considered to be remarkable feats at the time they were built.

Pranav Gavali, a Data Scientist, created this graphic using data from Encyclopedia Britannica and Wikipedia to visualize the world's seven ancient wonders along with their features and modern-day locations.

The graphic perfectly illustrates how the seven wonders were built and why they are considered a wonder of the world. The Great Pyramid of Giza is the only one of the seven wonders that still stands today.

4 The World's Population at 8 Billion

On November 15, 2022, the world's population reached 8 billion. This is the first time in history that there have been this many people on Earth. And there can't be a more straightforward and visually appealing way to present this data than this visualization.

What makes this big data visualization stand out is its simplicity and effectiveness in conveying the message. Using a circle to represent the earth is a powerful symbol that makes the visualization easy to understand and remember.

By using colors to represent continents and lines to separate countries, the visualization effectively conveys the complexity of the world's population in a simple and visually appealing way.

5 The Top 10 Largest Nuclear Explosions

This is a prime example of how creative design can bring data to life. It uses a unique approach, similar to an infographic, to showcase the impact and size of the largest nuclear explosions ever detonated.

It features a series of explosion image examples that help visualize each explosion's scale and impact. The use of images effectively conveys the destructive power of each blast in a way that is easy to understand and remember.

The data is presented clearly and concisely, with each explosion listed along with its country of origin.

6 Visualizing the History of Pandemics

This is an informative graphic named Visualizing the History of Pandemics by Nicholas LePan. It tells the story of all the known pandemics in the history of mankind, including the name of the disease, death toll and the approximate date the pandemic occurred.

While the exact number of victims of every disease is still under question, we can still learn from this graphic that super-spreading infections happened across all history of mankind. Statistical data of this infographic shows some diseases scaling with the growth of the population.

Striking 3D illustrations of diseases are combined with the research data from CDC, WHO, BBC, Wikipedia, Historical records, Encyclopedia Britannica and John Hopkins University. The illustrations scale according to the recorded death toll to allow scanning and recognizing data easily.

7 It Fell From the Sky

Created by a UK-based designer, this infographic highlights beautiful data visualization of 34,000 meteorites that have fallen on the Earth. You will discover the map and timeline of the impacts per year, wrapped up in clean, stylish graphics. The visualization also shows spikes on the records and comparing the size of the biggest meteorites recorded.

Meteorites hit almost all of Earth's surface, but some areas seem untouched; this phenomenon could be connected with Earth's magnetic fields. And who knows - the future may bring us even more meteorites to explore!

If you're a fan of space and astronomy, you can learn more about meteorites from NASA website or check out this of the Meteoritical Society. You can use our data visualization tool to create new stunning visualizations on meteorites and space - here for free.

8 Mars Mission 2024 Promo Reel

Vivid, rich in details. This 3D graphic uses beautiful data visualizations to share the vision of the future. Space missions and sending people into space are shown in an eye-catching red-grey palette.

The complicated animation of terrain exploration, space module flight and surface graphics are breathtaking. For a moment, you feel like a Mars mission crew member with your eyes on the stars.

9 Void of the Memories

These mesmerizing circles were brought to you by one of the best-in-class street art and calligraphy authors, Pokras Lampas. Whether you would like to decipher this canvas or refer to it as a pure visual object, the unique gothic Calligrafuturism style is an eye magnet for anyone.

The project is focused on the human consciousness and the theme of dreams in the context of human memory and experience. According to the author, the future is for global unity and harmony of cultures - and it's visible in the fusion of styles, techniques and systems used in the project graphics.

10 Plastic Waste Pollution

Based on data on the distribution of total plastic waste generation by continent, Jamie Kettle created this personal project to estimate the percentage of the plastic waste that was inadequately disposed of.

The infographic provides a clear and precise picture on current surface plastic mass by ocean, measuring it in a creative way. We can see plastic waste management for every country in a colored bar chart. The names of the countries that report 100% of all their plastic waste handled properly are highlighted in bold.

One of the major findings here is that the GDP of the country and efficient plastic waste management aren't always correlated - you can see it by irregular patterns shown in the infographic.

If you are curious about plastic waste, here are some resources for you: a on plastic waste, detailed info on the plastic waste pollution from the UN Environment Program and Impacts of Mismanaged Trash by the United States Environmental Protection Agency.

11 Fossil Fuels

This profound and complex visualization tells us about one of the most pressing environmental issues - the increasing amount of carbon dioxide in the Earth's atmosphere.

While CO 2 buildup is responsible for climate change, the trend is projected to continue, and the infographic provides insight into when this could happen. It's easy to notice a steady increase in fossil fuel emissions since the Industrial Revolution and the projected sharp rise in the concentration of carbon dioxide until 2100.

12 Price of a Pandemic: Poverty Spreads Around the Globe

In this classic data visualization by National Geographic, data is placed against the dark background for better contrast and readability. Simple, comprehensive charts show us the effect of the pandemic on the income of people in various countries.

The authors distributed three levels of income range for countries with low and middle class income to provide a clear picture of the current situation. Core findings of the report were that the pandemic pushed a tremendous amount of people to extreme poverty - projected data is 100 million of people living on $1.90 per person/day.

Based on the World Bank data, the infographic provides a wide view of the exact factors influencing people's wellbeing - from travel restrictions and job loss to wars, displacements and higher food costs. Highlights at the beginning reveal rapid shrinking of income in examined countries across all continents on a mass scale.

13 Water Consumption

Hidden food production costs involve a great amount of freshwater. This stunning example of visualization created by Chesca Kirkland unfolds a story of water consumption required to produce certain kinds of food.

From chocolate to cheese, coffee and beer, every product requires a certain amount of freshwater to grow or be produced. The second part of the infographic is centered on the water resources available, including the map of the water footprint per capita per year and general availability of clean water to people.

Nominated for two C-Change Environmental and Sustainability Awards, the project won First Class Honours in Final Design Futures. Raising awareness about water sustainability is vital as we move forward to a more intelligent, AI-driven future.

We at Visme are inviting you to take up the challenge and create informative infographics that can invite change to various industry branches. Use our amazing free infographic library to create graphics for your personal projects as well as corporate or brand presentations.

For more detailed info on the infographic creation, watch this video on the 13 major types of infographics.

14 Icebergs and Climate Change

Dedicated to "travel adventures" of this 4,200-square-kilometer iceberg, this infographic alerts people to climate change. A giant chunk of ice the length of Puerto Rico broke off the Antarctic peninsula coast to wander into the wild - and dangerously close to the South Georgia Island, packed with wildlife.

The graphic compares the size of the berg with 66 countries or territories, and cites that the ice mass is so large that it cannot be captured in one photograph. Besides, we can also see impressive geodata on the wildlife from the IUCN Red List of Threatened Species inhabiting the endangered South Georgia Island.

15 Cell Towers Map of the World

This stunning, elegant and creative visualization of 40 million cell towers is surely an unforgettable view. Based on OpenCelliD, the world's largest open database of cell towers, this interactive map is so far one of the most precise publicly available data sources for telecom-related projects.

We can see how the cell tower network lights up Europe and other big cities of the world; simultaneously, vast areas of "wilderness" are still present on the map. Harsh climate and low population density in the northern regions of Russia and Canada, along with central areas of Africa and Mongolia result in low quantity of cell towers in these areas.

Closeup view of this cell tower map resembles the brain structure. Similar to the neurons, axons and dendrites that create the communication network of the human body - cell towers keep humanity connected.

16 Active Satellites in Space

Created for Scientific American, this colorful and bright data visualization displays satellites in an original way. Neat and stylish satellite cluster grids sort them by country, orbit and class - business/commercial, civil, amateur/academic or defense.

The graphic details the mass of the satellites (100 kgs - 5,000 kgs), category (Test and Training, Communications, Images, Surveillance and Meteorology, Navigation and Research) and the launch date, from Nov 1974 till Aug 2020.

According to the graphic, six countries of the world control the largest amount of the satellites in orbit, and the US owns the largest share so far.

17 Covid Vaccination Tracker

Updated daily, this animated Covid vaccination tracker shows the percentage of people in the world given at least one dose. The infographic and data illustration displays data on the vaccination rollout plan in over 80 countries and 50 US states.

Data presented in this data visualization is sourced from the Our World in Data project at the University of Oxford. Uncluttered, simple graphs show the 7-day Covid vaccination rolling average as well. Interactive charts allow you to sort the percent of population given at least one dose by country or income.

At the bottom of the page we can see the detailed, in-depth Covid-19 vaccination statistics, with type of vaccines offered (Pfizer-BioNTech, Moderna, Sinopharm, CanSino, Oxford-AstraZeneca, Johnson & Johnson, Covishield, Sputnik V, etc.) and vaccination priority groups for various countries separately.

18 Blindsight

It took 4 years to create this non-commercial self-funded project. Based on the eponymous sci-fi novel by Peter Watts, this visualization row includes breathtaking renders of the solar system, four-dimensional objects as a system of data visualization and manipulation, spacesuit interface renders, cryo capsule graphics and nonhuman species concepts.

The visualization received over a dozen awards and nominations such as Best VFX Screen Power Film Festival 2020, Outstanding Achievement Award (Sci-fi Short) Indie Short Fest LA 2020, Winner Best Sound & Music Fantasy/Sci-fi film Festival 2021, Award Winner Flickfair 2020, Official selection Miami International Sci-fi Film Festival 2021 and so on.

Space mysteries have always tempted mankind. With the outstanding talent of the team behind the project, we hope to enjoy the related movie one day.

19 Gravitational Waves

Introducing to you another captivating space-themed project - the interactive visualization of gravitational wave events. Created for Science News, this space-time ripples design is amazingly minimalistic, slick and informative.

This enchanting spirals animation is saturated with the useful data about the black hole mergers, or cosmic smashups. You can learn about the original and final mass of the mergers, total merger size and other details of gravitational wave events.

20 Map of the Lighthouses of Ireland

Updated my map of the lighthouses of Ireland from the #30DayMapChallenge - now with the correct timings/flash patterns etc. Thanks to @IrishLights for providing additional information pic.twitter.com/eLlicP8fw5

- Neil Southall (@neilcfd1) December 8, 2020

This great animation was created as a part of 30 Day Map Challenge and it depicts all lighthouses in Ireland according to their timing and flash patterns. Here, the author visualizes data from the IrishLights - the maritime organization delivering the safety service around the coast of Ireland.

Aside from being a vital part of the water safety of coastal waterways, lighthouses are a symbol of hope and undying light even through the toughest circumstances. That's one of the reasons why this minimalistic graphic is so appealing.

21 Together [Hierarchical Positions of Employees in a Corporation]

Good data visualizations are essential for conveying complex information in an easily understandable way. Look at this creative way of displaying the hierarchical organization structure in a large corporation with a presence in over 100 countries. This creative data visualization example looks fun and a bit otherworldly, with muffled but contrasting colors.

Linking C-level executives to their subordinates in every branch revealed an intricate and complex corporation structure. It's suggested that in most cases, flat patterns would fail to represent company structures correctly because of the flexibility of human relations.

22 The Search for Dark Matter

The search for the ever elusive and intriguing dark matter continues. The problem isn't likely to get solved any time soon - but here is a striking infographic for you to follow the lead.

Quanta Magazine created this interesting data visualization to represent the types of particles that dark matter could be made of. Axions, WIMPs, ultralight dark matter or primordial black holes - any of these could be a star candidate.

Distributing every particle type along the scale according to their mass, the visualization also provides clear, concise descriptions for every type. Additionally, you can dive into the experiments' data. Are you the one to solve the new puzzle in particle physics?

23 2020 Autonomous Vehicle Technology Report

Concise and lean, this comprehensive report draws focus to autonomous vehicle technology and provides an insight into the hardware & software market for self-driving vehicles.

The report starts from the visualization explaining levels of autonomous vehicle capabilities in context of the environment. We learn that the greatest challenge for Google (Waymo), Uber and other companies building self-driving vehicles is to enable the vehicle to adjust to all driving scenarios.

Sensory technology is an essential part of autonomous vehicles, and they're designed to build an environment map and localize themselves inside that map at the same time. This requires huge computational technologies - maps created by AI systems and humans are of great help here.

Further in the report, we see the visualization of the electromagnetic spectrum and its usage for perception sensors, graphics of the time-of-flight (ToF) principle of environment sensing and various object detection sensor types such as radars, cameras, LIDARs, MEMS, etc. The next visualization covers different sets of sensors used for autonomy by Tesla, Volvo-Uber and Waymo.

Short, clean-cut schemes of the AI architecture of autonomous vehicles, the computation/decision making environment of an autonomous vehicle and the concept of vehicle-to-everything (V2X) communication complete the report.

24 The U.S. Election Twitter Network Graph Tool

These cutting edge visuals from the U.S. Election Twitter Network Graph Tool enables a viewer to analyze social media interactions that define the online political landscape. In this case, we're tracking the influence and connections between various political figures.

It's clearly visible which accounts the target account is most likely to mention or reply to. The network graphs clearly show the potential of certain accounts to generate new connections and influence their followers.

You can search for specific nodes in the interactive map. All information flow between nodes is reflected in the color of the node edges. Working together with other open-source investigation tools, this graph is meant to increase transparency and help fight misinformation in social networks.

25 Map of a Fly Brain

The high-resolution nervous system map represented in the above graphic is a part of the fruit-fly's brain - yet the complexity and harmony of the structure is astounding.

Millions of connections between 25,000 neurons create a wiring diagram, or connectome, of connections in various parts of a fruit fly's brain.

It's estimated that tracking all neuron connections in the fruit fly's brain manually would need 250 people working for 20 years at least. Google's computational power has helped to speed up this research, and scientists are aiming to create a full fruit fly brain visualization by 2022.

26 Freight Rail Works

Our next interesting visualization highlights the advanced layers of technology Freight Rail Works uses across its infrastructure. Talented Danil Krivoruchko & Aggressive/Loop teams produced a futuristic and dynamic animation of the data-world around a train in motion.

Magnificent waves of data light up outlines of the objects and then vanish in waves as the train moves forward to the smart city. Graphics of the giant city cluster zoom out to reveal the continent routes and the beauty of a simple railway communications network.

In the era of semi-autonomous aircrafts and drones, the simple, down-to-earth railway system looks stable but innovative in this graphic.

27 The Korean Clusters

Korean hospitals and churches experienced a burst of Covid infections among their visitors in January 2020. Having linked connections between the confirmed cases, scientists were able to trace back the first case and build a tree of contacts between the affected people.

Tracking the timeline of the first patient's actions revealed that this person caused thousands of infections. Wandering sick for a few days resulted in over 30 more people infected. Subsequently, the Shincheonji Church cluster with 5,016 infected people accounted for at least 60% of all cases in South Korea at that time.

28 2020's Biggest Tech Mergers and Acquisitions

Despite the fact that for most businesses 2020 was a devastating year with grim outcomes, this data visualization shows that Big Tech experienced a growth boost. It's not surprising that people working remotely increasingly need digital services of all kinds.

The graphic shows the biggest tech mergers and acquisitions closed in 2020, together with the short description of the acquired company, acquiring company, deal amount and deal date. While the chart is visually busy, it's also innovative and visually appealing.

If you need a market report from your industry area, grab the data from Crunchbase and build your own custom branded infographic via our data visualization tool quickly and easily. Sign up free .

29 Stolen Paintings

This wonderful visualization was created for Visual Data, a column on "La Lettura," the cultural supplement of "Corriere Della Sera."

Since 1900 to present day, the infographic reveals the details of 40 stolen paintings. Neutral, minimalistic visuals highlight the painting's artist, the year when the painting was created and the year of theft.

It was shocking to find out that the majority of thefts took place during the last 20 years (2000-2020) - and most of the art works have never been recovered.

30 House Of Cards LIDAR

House of Cards from Brendan Dawes on Vimeo.

Take a look at the last cool data visualization in this list - the rework of Radiohead's House of Cards video. This astonishing art was created on the basis of around one minute of the LIDAR data.

Motion graphics of particles scattered around a person's face create an unforgettable image. The hero of the story in the video is clearly emotional - but we can't tell anymore whether this person is even human.

AI generated data can be beautiful, but how can you take control?

Data Visualization FAQs

What Is the Most Popular Form of Data Visualization?

Bar graphs, bar charts or column charts are the most popular type of data visualization.

Bar charts are best for comparing numerical values across categories using rectangles (or bars) of equal width and variable height. You can use bar charts to compare items between different groups, measure changes over time and identify patterns or trends.

Other popular forms of data visualization include pie charts, line graphs, area charts, histograms, pivot tables, boxplots, scatter plots, radar charts and choropleth maps.

What Are the Benefits of Data Visualization?

Here's how data visualization helps users to make the most of their data.

Data visualizations make data clear, concise and easy to understand. Users can easily unlock key values from massive data sets, interpret them and draw conclusions.

Visualization allows business users to identify relationships, patterns and trends between data, giving it greater meaning. You can easily uncover fresh insights and focus areas that require more attention.

Creative data visualization is about creating compelling narratives through the use of graphics, diagrams and visual analytics. Visualizing data helps users tell better stories and convey messages in an engaging manner.

Data visualization can significantly increase the pace of decision-making processes since it makes it simple for us to understand visual data. It's no surprise, as The Wharton School of Business says that data visualization can cut down on meeting time by up to 24%.

Visualizing data helps quickly spot any errors so they can be removed. If you still doubt the importance of data visualization, this article about 50 data visualization statistics might change your thought process.

What are the Best Practices of Data Visualization?

Below are data visualization best practices to help you present data in an engaging and appealing way.

Specify the audience and their unique needs. Your data visualization should be crafted to communicate, provide real value and meet the needs of the target audience.

Define a Clear Purpose. Specify what questions you want your data visualizations to answer or the problems you want them to solve.

Keep your data clean. Before visualizing your data, make sure to fix or remove incomplete, duplicate, incorrect, corrupted and incorrectly formatted data within your dataset.

Use the right visuals. With so many charts available, identify the best type for presenting the particular data type you're working on.

Keep your data organized. At a glance, your audience should be able to view and digest information quickly.

Use the right color combination.

Read our article to learn more about data visualization best practices

Create Your Own Data Visualizations

If you are feeling inspired by these data visualizations, use our data visualization software to convert disparate data into clean, comprehensive visuals.

Wondering if Visme's data visualization tools are right for you? Take a look at what one of our satisfied customers, Cassandra C. | Owner, has to say:
"I also appreciate the wide range of features, including charts, graphs, and other visuals that can be used to present data in a clear and concise way. Overall, I'm very happy with Visme and would highly recommend it to anyone looking for a fun, user-friendly tool to create visuals."
To learn more about creating your own data visualizations, check out our detailed guide on data visualization types and the introduction to data viz on our blog.

# data[].attributes.attachment_link.title
The 30 Best Data Visualizations of 2023 [Examples]

# data[].attributes.attachment_link.description
We've created a list of the 25 best data visualization examples from 2020. Gather inspiration for your next data visualization or infographic.

# data[].attributes.attachment_link.readable_text
Home Topics What is Data Visualization? What is data visualization? Explore IBM's data visualization solution Subscribe for AI updates

What is data visualization?

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it's important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends. Harvard Business Review (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We'll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or Design Thinking sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they're looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate workflows. Data modeling also uses abstraction to represent and better understand data flow within an enterprise's information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn't limited to performance dashboards. For example, while text mining an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it's important to remember that it is a skillset that should extend beyond your core analytics team.

Guide How to choose the right AI foundation model

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

Related content
Register for the ebook on generative AI

Types of data visualizations

The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published The Visual Display of Quantitative Information (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.

Pie charts and stacked bar charts: These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.

Line charts and area charts: These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.

Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.

Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.

Heat maps: These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.

Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Open source visualization tools

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers. D3.js (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.

ECharts: A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc. Echarts (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.

Vega: Vega (link resides outside ibm.com) defines itself as "visualization grammar," providing support to customize visualizations across large datasets which are accessible from the web.

deck.gl: It is part of Uber's open source visualization framework suite. deck.gl (link resides outside ibm.com) is a framework, which is used for exploratory data analysis on big data. It helps build high-performance GPU-powered visualization on the web.

Data visualization best practices

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It's important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company's open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You'll want the data that you provide to motivate people to act within their scope of their role. If you're unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn't mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

Resources Tutorial Get started using IBM Watson Studio
Use features within IBM Watson® Studio that help you visualize and gain insights into your data, then cleanse and transform your data to build high-quality predictive models.
Tutorial Data visualization with Data Refinery
Data Refinery makes it easy to explore, prepare, and deliver data that people across your organization can trust.
Article Netezza with Apache Superset for data visualization and analysis
Learn how to use Apache Superset (a modern, enterprise-ready business intelligence web application) with Netezza database to uncover the story behind the data.

Predict outcomes with flexible AI-infused forecasting and analyze what-if scenarios in real-time. IBM Planning Analytics is an integrated business planning solution that turns raw data into actionable insights. Deploy as you need, on-premises or on cloud.

# data[].attributes.attachment_link.description
Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations.

# data[].attributes.headline
How to get started with data visualization

# data[].attributes.subject
How to get started with data visualization

# data[].attributes.wish_content.attachment_props.alternative_text
data wrapper how to get started with data visualization

# data[].attributes.attachment_link.readable_text
Sometimes, I get emails asking me "how to get started in data visualization." Maybe you're one of the people wondering about that. Maybe you found a book full of data vis in a bookstore, followed somebody who makes data vis on social media, noticed that you love playing with the "charts" feature in Excel, or just really like these data visualizations that newsrooms are creating.

And now you want to create them yourself but don't know where to get started.

This article tries to give some very practical advice on your next steps. If you ask ten people for that advice, you'll likely get ten different answers. This is mine:
1 Find out what you like about data visualizations
2 Don't worry too much about data or tools
3 Start to visualize data
4 Get better at visualizing data
It's based on the experiences I've had and what's worked for me. I hope some of these ideas will help you to find your own path.

1 Find out what you like about data visualizations

If you just have a blurry feeling that "data vis is great!", I recommend spending an afternoon figuring out what you like about data visualizations. You'll likely want to create visualizations similar to what you already admire, so this exercise can help you find out what exactly that is:

Do you know roughly what you want to create? Awesome, let's start. But before we do, let's get some potential blockers out of the way:

2 Don't worry too much about data or tools

Here's what used to hold me back (and still does sometimes!) when attempting to visualize data:

"What data should I visualize?"

It's easy to get lost in the sea of data ("Too much to choose from..."), or to get discouraged by searching for it ("The data I want to visualize is so hard to come by!") As everyone who's worked on Datawrapper's Weekly Chart blog series can tell you: You can spend hours and days searching the web for data, trying to find that one perfect CSV about your favorite topic. Don't fall into that trap if you want to get better at data vis design. (Feel free to fall into it if you want to get better at data analysis or data research!) Your topic and data don't need to be original. It's totally fine to visualize what others have visualized before. Here's how:

Start with the data, not with the topic

It's unlikely you'll get to work with data about that one exact thing you've always wanted to see visualized...because the data probably just doesn't exist. Surprisingly many data sets, you will find, don't exist. Or aren't free for you to use. Or are just very well hidden on the internet. So instead of starting with a chosen topic, it's way easier to look at what data sources exist and use them.

Use simple data sources

Simple data sources are ones that you can use right away, without much cleaning up. They're simple CSVs and not bigger than a few megabytes. Here are some good sources for simple data:

Kaggle Datasets: Data about movies, clothing, and much more. You can filter to show only CSVs.

Our World in Data: Click on "Browse by Topic" at the top, then see what's interesting to you. The articles are very well-written and feature many charts. Below each chart is a download button to get its full data as a CSV.

Datawrapper River: You can duplicate any chart and go to step 2 to see its data.

Your digital life. Lots of apps can track your behavior: The number of steps taken, hours slept or spent in front of your phone, messages sent, songs listened.

A bit more overwhelming, but also maybe helpful: the structured archive of the Data is Plural newsletter, Storytelling with Data's list of public data sources, and the Awesome Public Datasets collection (although the data they link to is often big - think millions of entries). Browse around until you find a data set that looks interesting, then stick with it.

Get rid of data points liberally

Data sets can be overwhelming: So many rows and columns! Many data sets have lots of more or less interesting stories in them. You don't need to tell them all. Try to find and visualize just one, and keep only the data that's relevant to your story. If you still have space to show more context, you can add more data to your visualization little by little - as long as your main point is still the main point.

Use data about a topic you understand

This could be a subject close to your heart (a TV show you know, or data about your life), one you read a lot about in the news, or one that's just easy to understand for most people. This way, you don't need to read three papers and decipher five acronyms before starting to visualize.

Stick with your data (more often than not)

One trap I often encounter is the fear of missing out on better data. "My data isn't that interesting," you might think. "There must be more interesting data out there." There probably is - but it would also take a long time to find it. If you constantly fall for that thinking and keep looking for new data without actually visualizing it, then I recommend working with data that's already been visualized by others. Somebody else has decided that it's interesting, so you don't need to doubt that anymore.

"What tool should I use?"

To create good visualizations, you will need to learn how to use some tools well. These could be pen and paper - but more likely will be something like Datawrapper, Observable, d3.js, ggplot2, RAWGraphs, or others. When I started with data vis, I pondered a lot about which tools to learn. R or Python? Adobe Illustrator with RAWGraphs? Am I missing out if I don't look into Figma? What would be the easiest? What would make me a better data visualization designer?

Based on these questions, I wrote a lot about data vis tools. And as somebody who did that, let me tell you: Tools really don't matter that much.

If you spend a year creating visualizations in Datawrapper, you'll become a better data visualization designer. If you only use Excel during that year, the same will happen. You'll learn about colors, phrasing, data framing, and chart types no matter which tool you're using - and you will easily transfer these skills to other tools if needed, and they'll stay with you longer than just the time spent using your first (or any) tool.

The Datawrapper app in 2023. Here's how to create your first chart with it.

So just start using what you know and feel comfortable with. Or research what tools people are using who create the kind of data visualizations you want to create - and then stick with them.

3 Start to visualize data

With two potential blockers out of the way, let's focus on how to actually visualize data.

You can admire all the amazing data vis out there, follow data vis designers, read all the books - but this all doesn't make you a data visualization designer. To become a data visualization designer, you need to design data visualizations.

Well, ok, the result is more the following:

Here's how to do that.

Don't plan, do.

You're one of those dreamers who have grand ideas but never actually sit down and work on them? Just start with 10 minutes. Sit down and tell yourself: "I will work on this data visualization for 10 minutes." If you stop after that, you did exactly what you planned on doing. If you continue: Well, that's also good!

Or give yourself an hour to create a visualization. Just one hour. Here's a concrete idea:

You're disappointed with what you got after 10 minutes or an hour? Read on, we'll get there.

Make visualizing data a routine (with others!)

"I'll create one small chart a day/week." "Every Monday night, I sit down and try to create a data vis." "I'll create 20 new charts in the next year". Routines and goals like this can help you to actually visualize data, and making it public can increase the pressure (in a good way!). No wonder that quite a few people have set themselves routines with a clear goal or end:

Dear Data by Giorgia Lupi and Stefanie Posavec, who sent each other data vis postcards every week for a year

Data Sketches by Shirley Wu and Nadieh Bremer, who created a big data visualization about an agreed topic every month for a year

Day Doh Viz by Amy Cesal, who tasked herself with creating 100 visualizations made of PlayDoh, one per day, each in 60 minutes or less, captured and annotated with her phone.

1 dataset, 100 visualizations by the data vis agency Ferdio (check out their self-imposed limitations). A good kind of project if you tend to get stuck in data research and preparation.

There are also community initiatives that you can try. Because everybody is visualizing a certain data set or working on the same prompt, it's fun to compare results. And many people are happy to give feedback:

Recreate a data visualization you admire.

Good data vis design is made of so, so many little details that you probably don't pay attention to when looking at it. That's why copying great data visualizations is one of the best ways to get better yourself.

✏️ Exercise: Copycat

Not convinced by this exercise? UI/UX designer Erik Kennedy makes a great case for copywork in the interface design field in his article "Copy If You Can: Improving Your UI Design Skills With Copywork" - a lot of this is applicable to data visualizations too.

Copy the style of a visualization you admire.

A similar exercise with more creative freedom is to design a new visualization with your own topic and data, but copying the design of a visualization you like. Ask yourself: " What would the creator of this visualization do? What design choices would they make? What vis type, fonts, colors, title, annotations would they use?"

Copy the style of an art or design movement you admire.

This exercise goes even further: You don't have a visualization as a reference anymore, but just an art or graphic design style (here are nine classic and 12 new ones). Browse Pinterest ( possible starting point), Instagram ( possible starting point), or collections like but does it float and Designspiration until you find something that makes you think, "Hey, let's try to translate that into a data vis." Then try it.

Have fun!

All of these little exercises should be fun. You might need a few minutes to get into the flow, but you should get there eventually. If they're not fun after 30 minutes, stop doing them. Data visualization should be a satisfying activity, and you want to finish feeling like "this was time well spent," not "urgh."

4 Get better at visualizing data

This is sorted by how effective I believe these exercises are at getting you better at data visualization:

Create more visualizations

Have I mentioned that, to get better at data vis, you'll need to create more data vis? I think I have, but in case I haven't, let me just state that again: To get better at data vis, you'll need to create more data vis. It is truly the most effective way I know to get better at something: Just keep doing it.

Not every chart or map you build will turn out awesome. Even if you build an awesome data visualization one day - the next one might not be awesome again.

Personally, that dynamic held me back quite a bit over the years. Every time I created a data visualization I was really proud of, I was either disappointed the next time I visualized data ("that looks only meh compared to last time!"), or I didn't dare to do so in the first place ("it won't turn out that great again.")

But keep going. Keep creating, and keep sharing. Because over time, the average quality of your visualizations will become better:

Don't compare your latest visualization with one from last week. Compare it with one you created six or twelve months ago.

Seeing yourself improve can be a huge boost in motivation to keep going. And to improve more quickly, it's crucial to know what's not working in your visualization and how to make it better - either by asking others, or by training your critical eye.

Ask your coworkers for feedback

One of the biggest reasons I wanted to work at Datawrapper in 2017 was that Gregor Aisch was working there. And Gregor, I knew, is an excellent chart designer. I believed that whatever visualizations I created, he would be able to improve them. And he did. I would create a chart, put my best effort into it, then show it to Gregor. And Gregor would make it so much better, every single time. I might have learned more about data visualizations by watching him (and others) improve my charts than by any other method.

If you have a great data visualization designer sitting next to you, make use of them: Show them your work, and ask what they would improve. If you don't happen to share desks with such a person, consider applying to jobs or internships where you will.

Not an option, either? You can always email data vis professionals for feedback, too:

Ask data vis pros for feedback

They might give you feedback during their free time, so the more concrete your feedback questions are, the better. Not: "Can you please give me feedback on this chart?" (If that's what you want, consider paying the person you're asking: "What's your hourly rate? Can I pay for in-depth feedback?") It's better to ask for the part you're the least sure about, like:

"I wonder if these colors work - what do you say?" or "I wasn't sure about this headline - how would you improve it?"You could just as well ask about the annotations, writing, data selection, visualization type, or general ease of understanding.

"What's the first thing you'd change in this chart?"

Consider not asking the designers with tens of thousands of followers, busy jobs, teaching/speaker side gigs, and book deals. You're more likely to get replies from somebody who gets an email asking for feedback once a year instead of once a week.

✏️ Exercise: Ask an expert

Asking strangers feels weird to you? Consider going to local meetups or international conferences to get to know people before you email them a few days later: "Hi X, it was great meeting you the other day! (I was the one with the dinosaur sweater who couldn't figure out how to use that fancy bottle opener.) I was wondering if you have five minutes to give me feedback on..."

Ask a community for feedback

Some organizations out there do amazing work for people who want to start or get better at visualizing data. Joining a community can be a great way to keep you accountable and get inspired... and, yes, you can ask them for feedback:

Data Visualization Society: Become a (free) member to join their Slack. People like you discuss in channels like #share-critique and #help-code, but also #connect-early-career and #topic-career-advice.

Elevate: Pay a membership fee to get four really experienced (and friendly!) people to mentor and coach you and give you freelance and career advice.

Storytelling with Data community: Sign up for free to discuss in the forum, attend challenges, and ask for feedback.

Whatever community you join: Give, not just get. It's more likely that people will help you out when you help them somehow too. And please don't spam with three posts about your work, but post one and see how it goes.

✏️ Exercise: Ask the community

Look at your visualizations with fresh eyes

You'll improve at visualizing data by understanding what could be improved about a visualization you created. But it's not necessary to always ask people for feedback to get there. It's also helpful to train yourself to look at your work like you've never seen it before. To do so, here are two ideas:

✏️ Exercise: Fresh eyes

Criticise visualizations
If you're reading this with a bit more experience, I wonder: What worked well for you? Let me and future readers know in the comments below, or write to me at lisa@datawrapper.de. I'm looking forward to hearing from you.
By training your critical eye on visualizations from others, you'll get better at turning it on your own visualizations too.

✏️ Exercise: What to improve?

Get inspiration

While getting inspiration alone won't make you a better data vis designer, it can help to motivate you: Every time I come back from a great conference, I get at least three ideas for new data visualizations I could create and can't wait to do so. Here's what works best for me:

Conferences. A good talk at 11 am after a great breakfast in another country. The room is dark, the stage is bright, the speaker is nervous, and I'm in focused sponge mode (soaking up all their ideas and lessons) and idea firing mode ("ooh, one could even go one step further and do...", "I wonder if that data exists for my city as well"). Some I like: Outlier, Information+, S-H-O-W, Visualizing Knowledge.

Conversations with other people who like to create. The ones where at some point, somebody says: "I wonder if anyone has visualized that before."

Exhibitions. Art exhibitions work great: "Wow, that turquoise and orange combination is beautiful! Maybe it'll work for my next data vis?" Exhibitions about random topics work too: "Fascinating topic, and I'm glad they chose a chart to show that dataset here! Should probably be a scatter plot, though? I'll take a photo of the source and try this at home..."

Also useful to get inspired:

# data[].attributes.attachment_link.title
How to get started with data visualization - Datawrapper Blog

# data[].attributes.attachment_link.description
A subjective and practical guide on what to do if you want to create data visualizations yourself (including lots of exercises and examples!)

# data[].attributes.body
Learn and apply the principles of effectively visualizing data.

# data[].attributes.body
Explore practical applications of data visualization

# data[].attributes.body
Collaborate

# data[].attributes.attachment_link.readable_text
Lets work together

Interaction, Interface and Information Design

With the human in mind, I ask for project goals, do research, ask people, do workshops, create concepts, visualize and test ideas, design wireframes and visuals, produce prototypes to get feedback and to iterate to come up with products, experience or systems for my clients.

I'm interested in designing systems for real people rather than power points or shiny pitch decks for tomorrow.

Data visualization

With data in hands, I ask for project goals, lead workshops, do research, explore data to find trends, patterns and relationships, design visual languages, develope information architectures, design wireframes, create prototypes, design layouts, tell stories and produce full working experience as well as products for my clients.

I'm interested in exploring new ways to visualize data to empower people to make thoughtful actions rather than designing generic dashboards and decorating numbers.

Lets work together

Work experience

I'm a freelance designer focused on creating meaningful applications and experiences driven by data visualization with a strong background in design and web development.

After starting my career as a bank clerk, I worked more than ten years as a web developer and designer. In last two years, I was working as a Senior Interaction Designer at MING Labs. Before that, I had the chance to design interactive data visualizations at ZEIT ONLINE and I gained knowledge through several internships at SQUARE in New York City and San Francisco, Interactive Things in Zurich and Strukt in Vienna.

I had and have the pleasure to work with great clients from small energetic startups to well-established enterprises and institutes during my freelance and agency time. I just want to mention Adidas, Bertelsmann Stiftung, brand eins, Deutsche Bahn, Google News Lab, Universal Music and Vodafone. Furthermore I partner up fine individuals like Moritz Stefaner of Truth and Beauty and support agencies like MING Labs in Berlin or Perception in New York.
Get more information at LinkedIn or Xing or better in a personal conversation.

Awards

2017

Information is Beautiful Awards - Bronze (Peak Spotting together with Moritz Stefaner and Studio NAND)

Information is Beautiful Awards - Honourable Mention (Travel visa inequality)

2016

German Design Award 2016 (Finalist Newcomer)

2014

Axel Springer Preis für junge Journalisten - (3rd Place - Internet for 100 Millionen at zeit.de with Paul Blickle)

Malofiej Bronze - Features (for Modeindustrie at zeit.de)

Malofiej Bronze - Breaking News (for Wählerwanderung at zeit.de)

The Best Bachelor Thesis of 2014 (Interface design in government surveilance between fiction and reality)

2013

dpa-Infografik-Awards - 2nd Place interactive (for Wählerwanderung at zeit.de (with Ole Häntzschel))

2012

Winner Visualizing.org Challenge (112 Years of Olympic Games)

Exhibitions

Don't be shy to contact me. I'm looking forward to hear from you - Cheers Christian!

Write me an email

# data[].attributes.attachment_link.title
Christian Laesser - Interaction design meets data visualization

# data[].attributes.attachment_link.description
Christian Laesser is a freelance interaction designer and data visualization designer from Berlin.

# data[].attributes.attachment_link.description
I'm a designer, and data is my favoured medium. I tend to work with data projects that involve language, literature, or science (or all of these at once). Many of these projects represent data using a hand-crafted approach. Besides designing with data, I occasionally design books.

# data[].attributes.attachment_link.description
I am an information designer and a Partner at Pentagram advocating for Data Humanism.

# data[].attributes.headline
7 Awesome and Free AI Tools You Should Know

# data[].attributes.subject
7 Awesome and Free AI Tools You Should Know

# data[].attributes.attachment_link.readable_text
Originally appeared in digitalgiraffes.com
The world of Artificial Intelligence continues to fascinate us. We collected 7 free artificial intelligence(AI) tools, most of them easy to use and some more sophisticated... like building ML models. We did try to be unique and avoid many of the AI platforms you'll find in most posts of Best AI Tools. Additionally, we wanted them to be free for testing, without caveats like adding your credit card number. Some of these we use ourselves (e.g., Lumen, JADBio), so it was important for us to include them as we truly value what they have to offer.

The list is not extensive, but we plan to come back to the topic often. Consider this version I.

GFP-GAN is a new Free AI tool that can fix most old photos instantly. GFP-GAN (Generative Facial Prior-Generative Adversarial Network) was developed by Tencent researchers by merging info from two AI models to fill in a photo's missing details with realistic detail in a few seconds, all the while maintaining high accuracy and quality. In this video (2:26), Louis Bouchard enthusiastically describes how well the project worked at photo restoration with details on how to use it for free. The results are very impressive and work well even with very low-quality images.

You can try a demo of GFP-GAN for free. The creators have also posted their code to let anyone implement the restoration tech in their own projects.

With this one, you literally just have to "copy." This AI-based copywriting tool generates unique content that you can post on your blog, social media, or even for a video by simply providing a short description of the topic you want content on.

Copy.AI could be helpful for content creators. The tool can help a user write Instagram captions, blog posts, product descriptions, Facebook content, start-up ideas, and many more.

Sign up for an account, give a description of your topic of interest, and the AI tool will generate content based on your description. Awesome tool that will be handy in the future of digital content.

Note: This ^ is not the only tool that uses AI to create content. There are several out there, some better than others so we suggest you give them all a try.

There's

- Microcopy for headlines, slogans, and more
- Speedwrite for rewriting and enhancing any sentence
- Hemingway for making your writing bold and clear
- Jarvis AI is a quality AI copywriting tool, as they say

plus Notion.AI that just arrived, and we've given it a whole paragraph below.

JADBio is a user-friendly machine learning platform that needs no expertise in coding but is equipped with powerful knowledge extraction tools. Although it is purpose-built for biotech and multi-omics, you can basically upload any kind of curated data and analyze it. Very useful for students who want to dive into machine learning or people who want to build a predictive model, for example, which potato will yield the best fry (binary classification). They offer a lifetime free account, and the use is pretty straightforward.

We guess you've heard all the hype about this one. DALL·E 2 by Open AI is a new AI system that can create realistic images and art from a description in natural language. It can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles. Worth playing around with, the website has several examples to inspire you. It's completely free to use.

Notion is testing a new feature called Notion AI to create content. All you have to do is tell the app what you want - a blog post, a recruiting email template, a list of great business books to read- and the app actually creates the content right in front of your eyes. A lot of users love Notion for note-taking, and this will definitely take it to the next level. We signed up to test it but are on the waiting list (as of Nov.16th) for now.

Lumen5 is an online video-making platform with tools that allow you to make videos effortlessly in a few steps. It includes great templates and various formats for different social media platforms. The AI comes in when you upload a transcript; it creates the whole video sequence, including images, using your imported transcript. A great and easy-to-use tool, especially if you want to create fast content for social media platforms. Lumen also offers an extensive range of images and videos to include in your final video.

Lalal uses artificial intelligence to remove vocals and conduct music source separation fast, easy, and precise stem extraction. You can remove vocal, instrumental, drums, bass, piano, electric guitar, acoustic guitar, and synthesizer tracks without quality loss. Makes creativity painless!
You can start playing around as it gives you 10 minutes free, but the paying models are also very reasonable. 15 euros one-time fee for 90 minutes of processing will get you far.
...to be continued
Written by Vicky Tsiachri
Social Media Expert at Digital Giraffes

# data[].attributes.attachment_link.title
7 Awesome and Free AI Tools You Should Know

# data[].attributes.attachment_link.description
The world of Artificial Intelligence continues to fascinate us. We collected 7 free artificial intelligence(AI) tools, most of them easy to use and some more sophisticated... like building ML models...

# data[].attributes.headline
What Data Visualization Is and How to Get Started In This Field

# data[].attributes.subject
What Data Visualization Is and How to Get Started In This Field

# data[].attributes.attachment_link.readable_text
From COVID-19 data dashboards and election results to personal finance apps and social media analytics, visual representations of data are increasingly ubiquitous in day-to-day life and in business. If there are numbers involved, it's often expected that those numbers will be visualized in a chart or a graph.

"Ten years ago, we definitely saw people who specialized in business intelligence or data or analytics, and that was their job, and everyone else was expected to send a request to those people and wait for reports back," says Ellie Fields, senior vice president of product development at Tableau.

That's no longer the only option. With a plethora of online tools and software packages available, and online classes and resources to help you hone your skills, the barrier to entry for creating compelling data visualization is lower than ever before. That means you can become proficient in presenting data visually without a background in computer science, data science or programming.

But with so many options, finding a place to start can be overwhelming. In this guide, we'll give you the rundown of how you can start creating charts, graphs, maps and reports for your business or organization - or if you're thinking about pursuing data visualization as a career or personal hobby.

What Is Data Visualization and Why Is it Important?

Businesses large and small use data every day to make decisions about their inventory, sales and investments. From sales reports to human resources data, hard numbers help businesses track growth and analyze trends that help them make strategic decisions for the future. Business analytics includes everything from data aggregation (the process of gathering data) to forecasting and predictive analytics, which provide businesses with insight on what could come down the road. According to a 2018 MicroStrategy study cited by Harvard Business School, companies have boosted process and cost efficiency by 60% using data.

Business owners and executives may have a "mental story" about how their business works, their costs and where they're succeeding, Fields says, but data often tell a different story. "When you can incorporate data into the way that you understand your business and the decisions that you make about it, you can be much more effective," Fields says.

And there's more data being captured today than ever before, with the widespread use of smartphones and other technology. An estimated 41 zettabytes (each zettabyte a trillion gigabytes) of data were created, captured, copied or consumed across the globe in 2019, and that total is expected to continue increasing, reaching 59 zettabytes in 2020 and 149 zettabytes by 2024, according to Statista.

But it's often hard for numbers and data, even in more comprehensible smaller chunks, to tell a story in their raw form on a table or in a spreadsheet. "People think in stories, and using data as part of that story is really important," Fields says. "For most people, if you literally have an Excel sheet of even just 20 by 20 rows and columns, it's hard for the human brain to parse. Our brains just don't work that way. And even if you're really smart and good with numbers, it's hard to get an accurate picture of what's going on there - to find trends and outliers."

Visualizing data - whether in charts, graphs or some other form - is important because it can give data meaning to a broader audience. "Visualization gives us a way to parse and understand data so that we can add it to our stories, we can incorporate it into our thinking," Fields says.

Growth in big data and the increasing importance of data to business strategy makes it a growing field - and one where labor is in short supply. As demand for data increases, jobs analyzing and visualizing data will, too. LinkedIn lists data analysis, analytical reasoning and business analytics as some of the top skills companies are searching for to help their organizations make data-driven decisions.

Data Visualization Types and Examples

Data visualization - like the underlying data itself - comes in many shapes and forms, but you're probably already familiar with some of the most commonly used types of visualizations, even if you're not familiar with their names.

The Most Common Types of Data Visualizations: Charts and Graphs

Below are some commonly used types of data visualizations. All of them are charts, which use symbols including icons, lines, bars, points, areas, colors and connections to bring meaning to data.

What it is: A line chart or line graph uses points connected by lines to display data and comes in a number of styles and variations.

When it's used: Line charts are most often used to display data over time.

What it is: A pie chart divides a circle into pieces resembling slices of pie to show proportion, with the size of each slice showing the amount or quantity it represents.

When it's used: Pie charts are most commonly used to represent percentages of a whole.

What it is: In a bar chart, two or more rectangles are used to display variables, with the height or length of the bars corresponding to the value of the variable.

When it's used: Bar charts display and compare amounts of different variables. They can also be used to display data and trends over time when the bars are placed in order along an axis representing time.

What it is: A scatter plot displays two variables of the same data point along an x and y axis. For example, a point on a scatter plot could be positioned on the x axis for an individual's height and the y axis for the same individual's weight.

When it's used: Scatter plots are used to try to identify trends or the relationship between two variables.

Variations and Other Types of Data Visualizations

There are so many other types of data visualizations and variations that it would be difficult to list them all, but here are some other examples:

Histogram. A histogram is a type of vertical bar chart that shows the frequency of distribution. The bars correspond to a range of values and the height shows the number of data points that fall within that range. Unlike a bar chart, in which the bars can be rearranged, a histogram cannot be rearranged.

Area chart. An area chart or area graph is similar to a line chart, except the area between the line and the axis is filled in. Like a line chart, area charts are great for showing trends over time, and the addition of the filled-in area emphasizes differences between the data points.

Maps. Maps can be used to display data. Geographic areas can be shaded with the opacity or color of the shading corresponding to a range or value. Data can often be displayed in different ways. For example, here's a chart that displays the same data both on a graph and with a map.

Treemap. A treemap uses shapes, usually rectangles, with the size corresponding to a value and each rectangle representing a data point. The rectangles can be nested to create categories and subcategories.

Infographic. Infographics cover pretty much any visual representation of information. This might include multiple graphs, charts or other information.

Here are some other types of visualizations:
Data Visualization Tools

There are several data visualization tools available, from simple to complex. Some are free, some are open source and some require paid licenses. Different tools serve as industry standards, including Tableau, Microsoft's Power BI, Domo and QGIS, along with many others.

Tableau is a widely-used visualization tool. It's a software package that can be used for free (if you're willing to save your visualizations to Tableau Public) or with a license. It can connect with Excel files, text and larger scale databases. For people new to data visualization, Tableau offers powerful capabilities with a drag-and-drop interface and without coding. While it's pretty simple to use Tableau to create basic visualizations, it can be more difficult and has a larger learning curve when you need more advanced visualizations. It can be used for pretty much anything, from business analytics to journalism or for making data visualizations as a hobby. There's a large community of data viz creators who use Tableau, so there's plenty of support online.

Microsoft's Power BI is oriented toward businesses for business analytics, but it's powerful for analyzing business data. Users can connect to various data sources, and it allows for customizable dashboards and detailed data reports. It's also integrated into Microsoft's product suite and works well with Excel and Azure. It is a paid service, but it's more economical than Tableau's paid licenses.

Flourish is another excellent data visualization tool used by businesses and journalists, among others. It's entirely online with no need to download software, and it has a wide range of templates and examples. For people new to visualizing data, Flourish is easy to use, has a ton of customization and interactive capabilities, and offers a free version. There's also a special free license for journalists and newsrooms. For business use, you'll have to buy the paid version.

You can explore more tools at vistools.net, where you can sort tools based on your programming skill, operating system, features and pricing.

How Does Programming Factor Into Data Visualization?

With so many data visualization tools on the market, learning a programming language or coding is no longer a requirement. "There's a huge amount of really interesting and thoughtful viz done without necessarily programming," says Tamara Munzer, an information visualization expert and professor of computer science at the University of British Columbia.

The question, Munzer says, is whether you need something custom or bespoke, or to take existing charts and templates and put them together thoughtfully and carefully to tell the story you mean for them to tell. If it's the former, it may be helpful to learn a programming language.

But in most cases, the tools that are already on the market can help you accomplish your goals without coding. "Thirty years ago, yeah, I had to program," Munzer says. "But we are not 30 years ago, and there are a number of tools that are trying to democratize visualization."

R and Data Visualization

Whether or not you're visualizing data, R is a free, open-source and widely used programming language and software environment for statistics and data. It's great for cleaning, mining and analyzing data. There's an enormous online community using R, so there's plenty of help and inspiration. One of the best parts of R is that it's highly extensible, meaning that there are add-ons, called packages, which add to its capabilities. Two of those packages are ggplot2, used to create static visualizations, and Shiny, which can be used to create interactive visualizations and dashboards.

Python and Data Visualization

Python is a programming language with a variety of uses well beyond data visualization. It's often used to gather, process and analyze data. It's flexible and relatively easy to learn. Like R, Python has add-ons, called libraries, that are used to visualize data. One that's often used is Altair, which allows for a wide range of visualizations.

Here's an example of what this language looks like using the Altair library.

import altair as alt import pandas as pd source = pd.DataFrame({ 'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], 'b': [28, 55, 43, 91, 81, 53, 19, 87, 52] }) alt.Chart(source).mark_bar().encode( x='a', y='b' )

The previous code produces this result:

Javascript and D3

If you're particularly interested in data visualization on the web, you might consider learning JavaScript, a common programming language used on web pages. D3, a powerful JavaScript library, is often used for creating interactive web-based visualizations. D3 has a high learning curve and requires a pretty good understanding of the JavaScript language with the upside of being highly flexible.

How to Learn Data Visualization

If you're creative, like numbers and have an interest in identifying trends and insights from data, you might want to learn more about data visualization. While you may not need to learn a programming language, you'll need to be willing to learn by trial and error, and you'll have to decide what your goals are.

But first, you need to be able to understand data and how to present it without misinterpreting that data and misleading those you're trying to reach. For example, you'll want to know the difference between a median and an average and how average can be misleading if there are outliers in your data. If you don't have a good grasp of data itself, your data visualizations may be incomplete or inaccurate. But that doesn't mean you should be afraid to begin working with visualizations.

"You know, there's sort of a delicate line between cautioning people appropriately and making them afraid to take any step forward for fear that the rattlesnakes will bite," says Munzer, who wrote a book about working with data visualizations. She also recommends "Data Feminism," a book about data ethics inspired by intersectional feminism. There are plenty of online data literacy courses and resources to get a basic understanding of data, including this free course from Tableau.

There's also the option of pursuing undergraduate degrees in data science or business analytics, though many professionals who work with data visualization every day don't have a degree in those fields. There is still room for degrees, especially for people who want to dive deep into data as their career.

"The vast majority of people working in data today, and making use of data, do not have computer science or data-oriented degrees. They have degrees in everything, and are doing jobs doing anything," Fields says.

Without pursuing a formal degree, you'll need to set aside time to learn on your own, but there's a range of courses, books, tutorials and other online resources for getting started in data visualization. Here are a few:

# data[].attributes.attachment_link.title
What Data Visualization Is and How to Get Started In This Field

# data[].attributes.attachment_link.description
From COVID-19 data dashboards and election results to personal finance apps and social media analytics, visual representations of data are increasingly ubiquitous in day-to-day life and in business. If there are numbers involved, it's often expected that those numbers will be visualized in a chart or a graph.

# data[].attributes.attachment_link.readable_text
After the success of Dear Data, we're back with our new book Observe, Collect, Draw! Pre-order this visual journal today to document the world around you and discover more about yourself and/or life!

# data[].attributes.attachment_link.description
Two girls who switched continents get to know each other through the data they draw and send across the pond. Dear Data is a year-long, analog data drawing project by Giorgia Lupi and Stefanie Posavec.

# data[].attributes.headline
Dear Data has been acquired by MoMA, but this isn’t what we are most excited about.

# data[].attributes.subject
Dear Data has been acquired by MoMA, but this isn’t what we are most excited about.

# data[].attributes.attachment_link.readable_text
It's always thrilling to look back on your life and see how often it's the smallest of interactions that completely change your life's direction and set you on a new path.

For us, it was a conversation over a beer, where, barely knowing each other, we nervously started dreaming of ways to collaborate. This small interaction was the impetus for a project that consumed the next two years of our lives as well as became the foundation for a working relationship that, while not without its struggles, has developed into a collaboration with unexpected longevity.

We are two data visualization designers living on either side of the Atlantic ( Giorgia in New York, and Stefanie in London) who live lives seemingly in parallel. We met, and - struck by how many personal and work similarities we shared - we wanted to get to know each other better. So we challenged ourselves, we would do this this using the material we were most familiar with: data.

This was first and foremost a personal project, created in the evenings and on the weekends as our immensely-patient partners watched as we made space on our kitchen tables to draw, crumple up, throw away, tear our hair out in frustration, then draw some more until we finally had a postcard we felt was good enough to be shared with our newfound collaborator.

Giorgia posting from New York, Stefanie posting from London

As this project unfolded, we began to understand each others' personal and professional foibles both in the postcards we sent each other and the messages we would furiously text back and forth as the project developed. We shared the most revealing and vulnerable aspects of ourselves: our flaws, our bad habits, and even shameful revelations from our pasts and our thoughts in the form of drawn data to be interpreted by the other person. It's as though we were keeping a shared diary within a weekly rhythm, where we used this tool - this material - to compose a portrait of the other person through these weekly fragments of her nature.

Over a year we connected at a deep level, learning to love each other's imperfections, but we could do this - though data - only because we've been working in a very manual and personal way. In fact, as we gathered our weekly data, the process was more labour-intensive than just deriving standard metrics from technological devices: we conceived Dear Data from the beginning as a "personal documentary" more than a "quantified self project" where we didn't only quantify numbers but we were also adding contextual details to our logs, thus making them truly personal, about us and us alone.

And so Dear Data - a project that started with an almost casual conversation - led to outcomes we never anticipated: being exhibited at Somerset House and London's Science Museum, winning 'Most Beautiful' award (the highest accolade) at the Information is Beautiful Awards 2015, being nominated for the Design Museum's illustrious ' 2016 Designs of the Year' awards, received incredible attention and kind words from the press and so, so many more incredible opportunities, all culminating in a self-titled book published by Penguin and Princeton Architectural Press.

As a matter of fact, our 104 original postcards and the many sketchbooks we filled with intermediate data drawings every week have found the best possible home for years to come. They will live in the archives and catalogue of one of the world's most prestigious institution (well, we think so, at least!), humbled by being in the presence of the dazzling company of the great masters of art of the past two centuries.

The Museum of Modern Art acquired its first artworks in 1929, the year it was established. Today, MoMA's evolving collection contains almost 200,000 works from around the world spanning the last 150 years. The collection includes an ever-expanding range of visual expression, including painting, sculpture, printmaking, drawing, photography, architecture, design, film, and media and performance art.

In an era where everyone speaks about virtual reality and big data, our laborious year of small, analog and slow data encoding evidently touched some interesting chords, as Paola Antonelli - senior curator of the Department of Architecture and Design - beautifully states:

"There is deep beauty and poetry in this exchange between two very contemporary women and colleagues, who about two years ago decided to entrust their nascent friendship to old-school snail mail. 52 weeks of postcards with hand-drawn visualizations based on intimate data sets, gathered with pen and notepad from everyday occurrences: this engaged conversation describes the future of our relationship with information."

This acquisition and these cherished words from Paola Antonelli would seem a natural happy ending to the Dear Data 'story', and we would expect you to close the book and stop reading now, safe in the knowledge that Giorgia and Stefanie lived happily after.

The most unexpected and heartwarming reward for us is how our project resonated with an incredibly wide and diverse range of people: with Dear Data we have been able for the first time in our professional lives to truly speak beyond the closed rooms of designers or data visualization practitioners. Since the project was made public, we have seen hundreds of postcards made by people who after hearing our story wanted to try the process for themselves, many seeking out their own data 'penpals' and also setting their own yearly challenges to draw their data.

We are also touched at how many young children are reading their parents' Dear Data book and are excitedly-drawing their own data (though we would have written about less adult themes in our postcards had we realised we'd have such a youthful audience!)

And more incredibly, teachers from grade school to university and beyond are using the Dear Data format to teach their students the world of data: an amazing (and humbling) result from what started as a side project.

This analog, manual and even imperfect way we approached data has opened the idea of data to a wider audience and made it more accessible, more understandable and more easily relatable to their daily lives. We've both found with previous projects that it's often difficult to get people to understand (for example) what big data is, or what open data is, if they don't know and fully understand what data is in the first place.

So isn't this what constitutes true success?

The most important achievement for a designer or even an artist is to have an impact on people's lives and also, to explore further how we - as designers who work with data - can use our daily tools and materials to speak beyond our closed circles and truly engage unexpected audiences.

We are part of the data visualization community, and as in any community, there is healthy debate on a discipline's function and best practice. There have been debates that seek to define what is and isn't data visualization, what is data art and and what is data design, or whether if to "enjoy" a visualization you should clearly know if it was meant to be an artistic exploration or a design project. When we hold Dear Data up to these definitions, it doesn't quite fit: it might be art, it might be communication design, it might be data visualization, it is probably all three: but its interdisciplinary qualities are an asset, not a failing.

We need to embrace the in-between as practitioners to move forward: by following the 'rules' and not taking risks, we ourselves risk treading water in one place, never extending the boundaries of our community.

Would a data-driven project that firmly adhered to specific disciplinary rules inspire a child to draw their own data and see the world through the eyes of a data collector? Or would defining a project only as an artwork offer an opportunity for a data visualization practitioner to become interested in ways they could make their practices more human-scaled, smaller, and more memorable? By working in purposely-blurred spaces, we we were able ultimately reach a broader audience and help our practice to progress.

If we set out to create something that neatly fit within the boundaries of our discipline, our project would been indistinguishable from all the other projects that conformed to these boundaries. However, by placing an emphasis on working in the fuzziness and the in-between spaces, in revelling in being truly interdisciplinary, we made a project that stood out, was noticed, and made an impact outside of our community through its very nature of not quite fitting.

And so, we see our MoMA acquisition as validation that Dear Data - for all its difficulty to be defined - has extended what is possible both for data visualization and for visual communication. This outreach and extension is what we are truly proud of.*

* And what about the goal of the project, you ask? Did you get to know each other better? Needless to say, that the premise of the collaboration couldn't have been personally more successful for us: through our data we truly became friends, deeply close friends, as we would never have imagined.

You can find more information on us, the project, the book, and how to get in touch on our website, thank you!

# data[].attributes.attachment_link.title
Dear Data has been acquired by MoMA, but this isn't what we are most excited about.

# data[].attributes.attachment_link.description
It's always thrilling to look back on your life and see how often it's the smallest of interactions that completely change your life's direction and set you on a new path. For us, it was a conversation over a beer, where, barely knowing each other, we nervously started dreaming of ways to collaborate.