The DVS introduces itself

It was impossibly exciting to see the flood of introductions fly by in the Data Visualization Society slack last week. Thousands of practitioners told each other about their cities, degrees, roles, toolchains, portfolios and lives.

It was impressive. It was inspiring. It was a call to ethnography. How do we signal to each other?

Over a few late weekend nights, I combed through a little over two thousand introductions. After a couple hundred, an archetypal introduction emerged:

Hi! I’m [name] from [city]. I graduated from [university] with a degree in [major]. I was a [previous role] before moving into [visualization related role], and now I [action verbs] [domains] [tools] [skills] [projects].

Here’s mine:

I’m Colin Megill (@colinmegill on Twitter / I’m an information designer, frontend engineer & founder, and I design and build web based data exploration products in machine learning contexts. I’ve spent the bulk of my visualization career designing and building three OSS products:, & a visualizer for the Human Cell Atlas. I’ve mentored and taught through a variety of venues (multi-month bootcamps, online & in person trainings, & within orgs).

In creating structured data from the introductions, I focused on what we say we ‘do’. Sometimes these were verbs like “crafting visual narratives” or “research”. Sometimes they were nouns like “defense” or “physical geography”.

For my bio, the final ‘features’ (nouns and verbs) extracted, after tweaking the tenses, might be: information design, frontend engineering, design, build, build web applications, build data exploration interfaces, product design, machine learning, mentor, teach.

Here’s another, from Annapaola Vacanti:

Hi DVS! I’m a graphic designer based in Genova, Italy and I just started my PhD studies in data visualization! I am especially interested in visualizing complex collaborative networks such as open source communities. I look forward to share knowledge and learn from all of you!

Another, from Claire Birnie:

I’m Claire, I’m a GIS Analyst in an Engineering firm in the North of the UK trying to implement change and educate.

And another, from Chris Ganowski:

Hi there, I’m a team lead for the analytics unit at the Ontario Ministry of Training, Colleges and Universities. Our team answers complex policy and operations questions about our employment and training programs with insights derived from data analysis. I have a strong personal passion for data visualization that conveys insights accurately, effectively and beautifully.

To mix and match features from everyone’s introductions randomly:

  • I write books and do graphic design in the mining industry
  • I reach different audiences in new and compelling ways in insect behavior
  • I lead workshops for paleobiologists building visualization suites
  • I do devops helping build aeroplane wings
  • I use excel for program evaluation in K12

We all do a lot of different things. We’re polymaths. We’re interdisciplinary. We have very diverse skillsets and backgrounds. We love that, and it is part of what brings us together.

Things are a bit scattered if we look primarily at domains: we’re in public health, academia, hard sciences, mining, law enforcement, K12 schools, hospitals, ambient computing, veterinary, defense, agriculture, digital humanities, cultural institutions, media, and all kinds of enterprises are all represented, amongst many others.

We talk a lot about the tools we use as a proxy for other skills, domains, and communities. Those doing development and design talk about languages and libraries, and it’s almost exclusively Python, R/ggplot2, JS/D3/Vega, SQL. Many use Excel, Tableau and PowerBI, Matlab, or Mathematica in science and enterprise. Those doing static visualizations seem to gravitate towards Adobe Illustrator.

Some of us build new applications, while others focus on analyzing and reporting, and there’s plenty of overlap. We talk about the projects we are building and creating, from marketing technology and weather data explorers, to navigational applications and internal tools, to dashboards and reporting infrastructure.

Some of us, a smaller minority, talk about advancing the practice of visualization itself. We create novel visualizations, and publish papers on them. We research the effectiveness of various kinds of visualization. We talk in depth about human cognition and information processing.

We are quite particular when we tell each other whether or not, how, and how much we’re in cartography and mapping. It is clear that among practitioners, it is widely recognized as the root of the field, and those who practice it are respected.

So what really unites us?

We are, fundamentally, visual communicators.

We sit at critical intersections in our organizations where information turns into decision making — whether that is via the media in democracies or on analytics teams inside large companies.

We craft, we write, we teach.

We are leaders, some of us intentionally and some of us because we’ve been tasked with inserting data into decision making processes which aren’t accustomed to them — be that city councils or corporate boards.

We want people to be able to navigate complex decision with minimal cognitive effort and maximal clarity. We don’t want people to see incorrect information, or be confused, or be negatively affected by decision made with bad information or presentation. We care.

Finding insights and communicating them effectively to educate binds us all together. The words which rise to the top, after 2000 introductions:

Visual communication.

Without further ado, here are the features I extracted. I rearranged them to try to tell a story and make it easier to digest, but it’s certainly only one story and others will tell different ones (especially unsupervised analysis of our respective scorecards on an expanded version of the below).

You can find the datasets — both as they were extracted sequentially and as I rearranged them — here:

The Data

These words and phrases are exclusively extracted from the first 2000 or so introductions.

They are not meant to be an exhaustive list of industries and domains where data visualization is practiced or tools used. It’s just a snapshot.

The words and phrases were broken apart, deduplicated and the tense of some of the words were changed to be more uniform. Other than that, this comes from our words.

Join the conversation:


### The practice & process of visual communication
* find something interesting in everything
* learn
* research
* find insights
* develop insights
* use human intuition
* analyze
* report
* visual communication
* information design
* visualization design
* create narratives with data
* data visualization research
* make little visualization experiments
* tinker with code
* make personal projects
* R&D
* communicating insights effectively
* trying to figure out how to best communicate uncertainty to lay audiences
* combat misinterpretations and overconfidence
* uncertainty visualization
* design more effective approaches towards promoting evidence based decisions
* find insights and communicate them effectively
* crafting visual narratives
* explore different chart types
* graphic design
* build
* data visualization
* static print visualization
* make data visualization for publications
* data comics
* interactive visualizations
* make data stories
* build platforms
* create tools
* build tools for data scientists
* build web based tools for data exploration
* build internal tools
* building web based mapping and analysis tools
* working with weather data
* maintaining data portals
* marketing technology
* custom data driven applications
* designing and developing data-driven web-based interfaces
* creating network visualizations or diagrams
* creating process flows
* navigation applications
* integrating genetics with health care data
* translate the work of statisticians into explorable graphical interfaces
* build proof of concepts for new data products
* create applications that are suites of data visualization
* automatize visualization pipelines
* augmented reality prototypting
* translate the work of statisticians or other quantitative types (economists, bioinformaticians, physicists, etc.) into explorable, graphical forms / interfaces
* try to explain complicated information in clear ways
* a variety of jobs
* produce commercial libraries for visualization
* enhance visualization techniques
* animation
* 3d visualization
* optimize a big, complex dataviz
* merge data science and graphic design
* test the effectiveness of visualizations
* promote design and visualization best practices
* communicate complex things simply to the general public
* reach different audiences in new and compelling ways
* build up my portfolio
* empower people

### Leadership, communication, teaching, training
* teach
* teach dataviz
* teach data storytelling
* podcasting
* lead
* train
* consulting
* independent blogging
* use explanatory visualizations to communicate with customers
* content management
* write academic articles
* address inadequate data literacy skills
* facilitate dataviz training
* run a meetup
* recruiting & talent acquisition
* manage teams
* write
* write articles
* write books
* lead workshops
* communication
* customer support
* leading a team focused on analytics around helping students succeed
* communicate clearly to high-level stakeholders
* ensure high standards in data visualization and communication across an organization
* providing customized and scale-able reports for people who generally do not have analytic backgrounds
* help people advocate for themselves
* help large institutions bring data into the policy and process improvement conversation

### Sectors, domains, contexts
* civic service design
* digital humanities
* K12 schools
* higher education
* nonprofit
* program evaluation
* organizational planning
* policy analysis
* public policy research
* social policy research
* gather, process and present statistics about the economy and society of a country
* make public information public knowledge
* run a census
* use visualization for empathy and better understanding of abstract human experiences
* content design
* agency work
* defense
* law enforcement
* transportation safety
* help build aeroplane wings
* finding data pertaining to global news
* collaborate with cultural institutions such as museums or archives
* showcasing interesting datasets
* make viral content as a hobby
* use environmental sensors
* help athletes become better players
* sports visualization
* ambient computing
* supply chain management

###### Maps & geospatial
* cartography
* cartographic design
* create projections
* interactive maps
* spatial data analysis

###### Health
* public health
* digital health
* veterinary diagnostics
* data visualization for global health (reproductive, maternal, newborn, child and nutrition)
* aid clinical trials
* health statistics
* interactive online patient education content

###### Arts
* art
* generative art
* data art
* drawing with ink
* design
* interactive sculpture art
* special effects
* trippy algo art
* take pictures
* film compositing

###### Finance & economics
* financial technology
* economics
* finance
* study the market
* financial risk management
* accounting
* build financial models

###### Journalism
* visual journalism
* explain to journlists how to create better charts
* data journalism
* visual journalism
* create custom interactive visualization for articles
* graphics editorial work
* editorial work at a print magazine
* tell data and visually driven stories about topics debated in culture

###### The sciences & engineering
* statistics
* bioinformatics
* epidemiology
* aerospace
* mechanical engineering
* geophysics
* energy and minerals
* climate science
* ocean biochemistry
* water quality monitoring
* ecology
* agriculture
* complex systems research
* urban planning
* insect behavior
* computational neuroscience
* microbiology
* conservation biology
* paleobiology
* biomedical communication
* geographic visualization
* scientific data visualization
* physical geography
* environmental engineering
* geo visualization
* physics
* chemistry
* open science

###### Business intelligence & enterprise practices
* e-commerce
* marketing analytics
* work with Tableau for exploration
* analytics
* people analytics
* sports analytics
* analytics infrastructure
* business intelligence
* procurement
* product analysis
* business dashboards
* recruit participants
* analyze operational data
* move coworkers off excel
* making open data easier to work with
* juggle corporate guidelines for documents and graphs
* metrics and reporting
* fraud detection
* presentation / visualization of business data
* customer insight and experience
* support business decisions
* trying to make waterfall charts not awful
* comparative / time series work
* corporate forecasting models
* enterprise application support
* lead organizational efforts around accessibility
* produce custom made dashboards

### Analyze & Build

###### Data science practices
* data science
* data scraping
* processing data
* refining data
* data things
* data exploration
* work with high dimensional data
* backend machine learning stuff
* applied statistics
* data modeling
* data sharing
* data analysis
* data health
* clean data
* data engineering
* tweak data in R or python
* exploratory work
* work with data containing ambiguity, gaps, uncertainties and interpetive aspects

###### Data science methods
* predictive analytics
* pattern recognition
* bayesian statistics
* large scale network analysis
* machine learning
* artificial intelligence
* estimation statistics
* semiotic analysis
* sentiment analysis
* natural language processing
* graph visualization
* draw semantic word clouds
* snowball sampling

###### Software development
* engineering
* product design
* Data+Design+Code
* ui design
* ux design
* ux engineer
* user research
* usability testing
* accessibility
* web development
* software engineer
* product engineering
* full stack engineering
* front end engineering
* ops
* devops
* integration architecture
* quality evaluation / assurance
* software testing

### Tools
* R
* Python
* JavaScript
* C#
* Matlab
* Mathematica
* D3
* PowerBI
* Tableau
* Excel
* Illustrator
* Sketch
* Figma