What do data sculptures look like? The wonderful team at dataphys.org has been cataloging, thinking through, and writing about this for years. I could do no better background than they do in their paper about Opportunities and Challenges for Data Physicalization, so just start by reading that.

Ok, welcome back. So what is a data sculpture to me? It is a representation of data created using physical objects in the real world. While charts and graphs in 2D map data onto classical visual variables (size, color, shape, position, etc), data sculptures map data on to an additional set of things — smell, texture, 3D shape, taste, scale, etc. This media gives you a new toolbox with which to create data representations, and requires a new set of skills for creating with.

What I want to do is share some lessons and ideas from my ten years of helping people design and make data sculptures, in a variety of educational settings. Warning: I’m going to put on my Professor hat and share some of my strong opinions about what I think works and what I think doesn’t. I look forward to your constructive disagreements.

This posts teases out three themes I’ve seen with concrete examples from my teaching and beyond. These themes are:

1. Making charts in 3D just scratches the surface;
2. Choosing your materials wisely is critical to your physical data mappings;
3. Moving beyond gimmicks lets you flesh out how to support multiple levels of reading.

Making Charts in 3D

Most folks approach the idea of data sculptures with their existing vocabulary of data visualizations; they simply render an existing chart type in 3D using some physical material. This is all well and good, but I think making 3D charts misses the potential for data sculptures to attract and interact with audiences in memorable and provocative ways. Here are a few examples that went, or could have gone, a little further along that path.

Parallel Coordinates

One of the sparks for this post was a wonderful piece from two folks at Gramener, who wrote about their experience creating a physical data visualization in support of hackathons they run (thanks to Allen Downey for pointing me at this). They created a parallel coordinates chart with bamboo sticks and string to show metadata about the participants at these events.

Parallel coordinates are hard to read, but are powerful because they can show both trends and individual data points (see the great writeup on datavizcatalog.com for more details). Rendering them as a physical spectacle is a wonderful idea to both attract attention and do get to know the data. However, I’m left wondering about missed opportunities in the creation based on the physicality of the sculpture itself.

This project immediately brought to mind earlier work by the Domestic Data Streamers, who prompted attendees of a 2014 arts event to create a similar chart (they called it “Data Strings”). They key difference here is that they asked the participants themselves to create their data points. I think that addresses the main criticism I’d have of the Gramener example — it helped the authors understand and represent the data, while the Data Strings example took advantage of the idea to engage participants more fully.

If you’re going to make a chart in 3D, make sure it is in 3D for a reason. The Domestic Data Streamers participatory invitation is a strong reason.

Fireworks: Fun & Dangerous

Lets tackle another example of a very chart-like data sculpture. A team of students in my 2016 Data Storytelling Studio course decided to analyze fireworks-related injuries in the US. As they thought about how to best represent this in a quick data sculpture prototype, they landed on the idea of painting a mannequin to show where injuries occurred most.

This repurposing of a heat map in 3d form was a clever idea, especially since it used the human body itself in a very relatable way; you couldn’t help but feel the impact of the dark red hands (good color choice). It was a simple comparison story rendered in an emotionally evocative way, clearly intended to caution the audience about being reckless with fireworks! Yes, it is an old technique rendered in 3D, but the physical scale of the body standing in front of you fundamentally changed how you read it — you related to it.

It’s a Mysterbee

Let’s move on to the classic bar chart. A team of students from my 2018 class were digging into data about bee colony collapse across the US. Thinking of how to get people interested in a topic they might not otherwise be engaged with, the students decided to tell a high-level comparison story in honey itself by filling two cups with honey. Each represented a different year, and the amount of honey was based on the total production in each year. They invited the hypothetical audience to dip a cracker in each cup and compare — essentially creating drippy and delicious bar charts.

When was the last time you ate your bar chart? This invitation used a familiar method of reading that would make sense to people, but playfully used the subject of the data itself (the honey) to represent the data in a simple way. They had follow-up material that could support a longer conversation for folks that did stop and try the experiment, so it wasn’t just a one-trick show that ended with questions. The bar chart in 3D supported a comparison, and the cracker lent itself to being a barchart. Their use of the bar chart had a reasoning to it.

Closing Thoughts on Charts in 3D

I’ve been thinking about this theme for a while, because I see it so often. In fact in class, and in our Data Sculptures activity, I explicitly caution against doing this. I don’t mean to say it is never appropriate. In fact a 2013 study from Jansen dug into 3D bar charts to explore if they supported investigation and inquiry more. I took away the lesson that when people physically touched the 3d objects representing the data they did a better job understanding the data. A more recent paper they wrote, from 2016, investigated different approaches to mapping size as a physical variable and how people perceived it. It has a range of interesting findings, such as how spherical surface area was more accurately perceived than volume, but mostly point out that we don’t understand yet how physical variables are perceived.

Take Advantage of Your Material

The second theme I want to flesh out is how much the material matters. Cardboard is light and folds easily; balloons grow and shrink, float and pop; water flows, drips, quenches your thirst and gets you wet. If you’re using any of those materials, take advantage of how people use the material and what it can do. The creators of the Gramener graph acknowledge this for themselves, noting the power of how “feeling every data point was an experience in itself”. Choose your material intentionally to design the look, feel, smell, and taste of your data sculpture. Here are a few examples that flesh out what I mean.

Where is Your Water From

Water is used in a variety of ways across the globe — agriculture, industrial operations, human consumption. A group in my 2019 course used data about this breakdown to create an interactive piece about the future water available to us. They called it Where is Your Water From?. Their interaction was built around people’s perceptions of what water is used for and comparing that to the data.

They key point I want to emphasize with this example is how the invitations they crafted for participants centered around the “wateriness” of water. When asking people to guess how much water is used for different categories, they ask participants to pour the water from one container to another. When looking at data about how much water might be available in the future for us to consume directly, participants were invited to drink the tiny amounts. These two actions are strong examples of using the properties of water to help support the narrative they are trying to tell. They used the physical affordances and human behaviors around water to represent the data story.

Tasting Air Pollution
Taste is a wonderful sense to explore, and playful thing too map data to! Data Cuisine has been leading workshops doing this since around 2014 — their gallery has some wonderful examples of data rendered in food. Many are visual representations, but others alter flavor based on the data. A group of students in my 2017 course were inspired by this idea to sketch out an idea that mapped air quality data onto flavor in a project called Tasting Air Pollution.

Air quality is hard to experience; we don’t see subtle changes and don’t have a good sense of what an abstract air quality number means in terms of our daily experience. Stephanie Posavec and Mariam Quick’s “Air Transformed” piece gets at this in a concreate way. They literally created a set of glasses that obscured what you could see more based on how much pollution was in the air.

This group of students in my class decided to experiment with flavor as a way to represent air quality data. They were particularly curious about how we perceive intensity of flavor, and how gagging or couching on surprising or bad flavors feels like the response you have to polluted air.

To surprise the audience they invite participants to taste different brownies but didn’t telling them that the amount of salt had been increased based on how much pollution in the air there is in different cities. The “goal” brownie has the right amount of salt to make it delicious, while the Beijing brownie tastes horrible. Trust me, I was the test subject when they presented it in class!

Closing Thoughts on Materials

Data sculptures are more than just ink and paper or pixels on a screen. Data sculptures are made of something, and you have to think about what that smoething should be. Be conscious and intentional in your choice of the material you make your data sculpture out of. Consider the affordances, limitations, common uses, and interaction patterns of your material. Choose your material wisely.

Support Deeper Investigation

In my workshops and classes I encourage participants to support many “layers of reading” in their pieces. What dose that mean? Viewers should be able to quickly scan the piece and understand the main story, but should also be able to dig deeper to see more nuance and detail associated with the narrative.

Here’s the thing — most data sculptures I see don’t have many layers of reading. They use some kind of clever gimmick or tongue in cheek pun to make their point. I want to encourage you to move beyond these simple tricks and flesh out a multi-layered story that can be told with multiple uses of your physical mappings. There is a richness in your material and form that you should take advantage of.

Monopoly and Elections
One of the few examples in print that I showcase often is an article published in the New York Times in 2016, entitled The Families Funding the 2016 Election. The narrative focuses on the small number of obscenely wealthy families that were responsible for most of the campaign donations. To tell this story they use the visual metaphor of houses and hotels from from the board game Monopoly; a symbol instantly recognizable to any American kid.

The article opens with a visual pun. They show a mocked up photo of a huge pile of green Monopoly houses blocking the White House, then quickly zoom in to a tiny pile of red Monopoly hotels on top (as the reader scrolls). The whole pile literally obscures the White House and the contrast between the number of red and green pieces instantly reveals the story arc (along with the text superimposed on top). This is playful, effective, and a good example of a data sculpture presented in 2D.

Keep scrolling down the piece the reader discovers why this is even more powerfully used. First off, they continue to represent data with these same physical symbols to compare things like party affiliation of the donors.

Continuing event further down the piece one fnids that they bridge from house satellite imagery to maps showing their locations, and real photos of the houses themselves. This progression of representations is a wonderful example of really pulling all the power out of the physical symbol that you can. They support digging deeper and deeper into the data and the narrative, utilizing this physical representation in different ways throughout.

The Hidden Weight of Food

The water used in food production is becoming a larger topic of discussion as droughts become longer and more frequent. Another group of students in my 2019 course used the data about water cost of foods to create a series of sculptures — the hidden weight of food. They describe the interaction like this:

The hook is a long table with plates of food. Each plate has a fork with a bite-sized piece of food on it, such as a slice of apple. When you lift the fork, you realize it’s much heavier than a slice of apple should be. Upon being surprised and interested to learn more from the exhibit, you read the sign and realize that the weight you are lifting is the weight of the water used to produce that bite of food. For a slice of apple, that’s a full 27 pounds.

I can tell you from experience that it is a very surprising and effective trick, even in the rough prototype form that they build. They took advantage of the fact that we eat food, and that water is heavy. This comparison, between the expected weight of a bite of food vs. the far larger weight of the water used to produce that bite, is a super compelling and surprising story. It tries to capture that surprise and turn it into interest. They considered the subjects of the data (water and food), and used their affordances to design a delightful and evocative data sculpture.

They expanded on this simple and surprising interaction by adding another sculpture that provides more detail. After lifting the forks and becoming engaged with the topic, viewers can walk to the next sculpture, which breaks down the types of water used in the production of an orange, to complement the total volume of water presented in the first sculpture. They use the familiar shape of 2-liter bottles to make a pyramid with colored water representing different types of water. This constructs another physical invitation, digging into the story of water along a different dimension.

Closing Thoughts on Layers of

The lesson? Don’t stop with your initial idea; tease out how you can support your longer narrative using spark that you’ve got. Thee power in these examples is that they used the data sculptures approach to present multiple dimensions of the data story.

They expanded on this simple and surprising interaction by adding another sculpture that provides more detail. After lifting the forks and becoming engaged with the topic, viewers can walk to the next sculpture, which breaks down the types of water used in the production of an orange, to complement the total volume of water presented in the first sculpture. They use the familiar shape of 2-liter bottles to make a pyramid with colored water representing different types of water. This constructs another physical invitation, digging into the story of water along a different dimension.
They power here is that they used the data sculptures approach to present multiple dominions of the data story.

Conclusion

Curious to hear more about my approach to data scultpures? Check out the lecture slides, with notes, from the data sculptures session in my Data Storytelling Studio class on MIT’s Open Courseware site. My thoughts have evolved more since then, but it is a good set of sparks, prompts, and reflections.

Curious to hear more about my approach to data scultpures? Check out the lecture slides, with notes, from the data sculptures session in my Data Storytelling Studio class on MIT’s Open Courseware site. My thoughts have evolved more since then, but it is a good set of sparks, prompts, and reflections.

Catherine D’Ignazio and I put these thoughts together into a talk for OpenVisConf this year.  This is a super-dorky conference for data viz professionals… just the place to find more tool builders to talk to!  We put together an argument that data visualization tool as informal learning spaces.  Watch the video below:

Recently at the Responsible Visualization event put on the by the Responsible Data Forum I had a wonderful chance to sit down with the amazing Patrick Ball from the Human Rights Data Group and talk through how we help groups learn about working with incomplete data.

With my focus on capacity building, I’m trying to find fun ways for NGOs to learn about accuracy and data at a very basic level. Patrick agues that in fact you need rigorous statistical analysis to do this well, from his background in human rights data. I pushed a bit, asking him is there was a 80/20 shortcut. His response was to paint a great distinction between homogenous and heterogenous observability of data. For instance, there are many examples of questions that don’t require quantitative rigor – case existence, case history, etc.  This sparked a fun conversation about visual techniques for conveying uncertainty.

Watch the video to see the short conversation, or just catch the audio below.

When we talk about data science for good, collaborating with organizations that work for the social good, we are immediately entered into a conversation about empowerment.  How can data science help these organizations empower their constituencies and create change in the world?  Catherine and I are educators, and strongly believe learning is about empowerment, so this area naturally appeals to us!  That’s why we wrote this paper for the Bloomberg Data for Good Exchange.

Data Literacy

We’ve been thinking and working a lot on data literacy, and how to help folks build their capacity to work with information to create social change.  We define “data literacy” as the ability to read, work with, analyze and argue with data.  So how do we help build data literacy in creative and fun ways?  One example is the activity we do around text analysis.  We introduce folks to a simple word-couting website and give them lyrics of popular musicians to analyze.  Over the course of half and hour folks poke at the data, looking for stories comparing word usage between artists.  Then they sketch a visual to share a story.

Another example are my Data Murals – where we help a community group find a story in their data, collaboratively design a visual to tell that story, and paint it as a community mural.

This stuff is fun, and makes learning to work with data accessible.  We focus on working with technical and non-technical audiences.  The technical folks have a lot to learn about how to use data to effect change, while the non-technical folks want to build their skills to use data in support of their mission.

Empowerment

However this work has been focused on small data sets… when we think about “big data literacy” we see some gaps in our definition and our work.  Here are four problems related to empowerment that we see in big data, related to our definition of data literacy:

• lack of transparency: you can’t read the data if you don’t even know it exists
• extractive collection: you can’t work with data if it isn’t available
• technological complexity: you can’t analyze data unless you can overcome the technical challenges of big data
• control of impact: you can’t argue for change with data unless you can effect that change

With these problems in mind, we decided we needed an expanded definition of “big data literacy”. This includes:

• identifying when and where data is being collected
• understanding the algorithmic manipulations
• weighing the real and potential ethical impacts

So how do we work on building this type of big data literacy?  First off we look to Freire for inspiration.  We could go on for hours about his approach to building literacy in Brazil, but want to focus on his “Population Education”.  That approach was about using literacy to do education and emancipation.  This second piece matters when you are doing data for good; it isn’t just about acquiring technical skills!

Ideas

We want to work with you on how to address this empowerment problem, and have a few ideas of our own that we want to try out.  The paper has seven of these sketched out, but here are three examples.

Idea #1: Participatory Algorithmic Simulations

We want to create examples of participatory simulations for how algorithms function.  Imagine a linear search being demonstrated by lining people up and going from left to right searching for someone named “Anita”.  This would build on the rich tradition of moving your body to mimic and understand how a system functions (called “body syntonicity“).  Participatory algorithmic simulations would focus on understanding algorithmic manipulations.

Ideas #2: Data Journals

Data can bee seen as the traces of the interactions between you and the world around you.  With this definition in mind, in our classes we ask students to keep a journal of every piece of data they create during a 24 hour period (see some examples).  This activity targets identifying when and where data is being collected.  We facilitate a discussion about these journals, asking students which ones creep them out the most, which leads to a great chance to weigh the real and potential ethical implications.

Ideas #3: Reverse Engineering Algorithms:

We’ve seen a bunch of great work recently on reverse engineering algorithms, trying to understand why Amazon suggests certain products to you, or why you only see certain information on your Facebook.  We think there are ways to bring this research to the personal level by designing experiments individuals can run to speculate about how these algorithms work.  Building on Henry Jenkin’s idea of “Civic Imagination”, we could ask people to design how they would want the algorithms to work, and perhaps develop descriptive visual explanations of their own ideas.

Get Involved!

We think each of these three can help build big data literacy and try to address big data’s empowerment problem.  Read the paper for some other ideas.  Do you have other ideas or experiences we can learn from?  We’ll be working on some of these and look forward to collaborating!

The Problem

Governments large and small are speaking of open-data platforms and data-informed decision making.  They share with us a vision of responding to citizen concerns more accurately and efficiently based on data.  These governments are using the language of data.  Data is a language governments are speaking, but most people don’t understand.  This is the core problem that I address with my Data Therapy project.

Can Popular Education Help?

If you don’t speak the language used by your government to make decisions, then you can’t participate in those decisions.  This disempowers people, and popular education is an approach for rectifying disempowering situations.  The city I live in, Somerville, MA, has a a program called “ResiStat” that is intended to 

bring data-driven discussions and decision-making to residents and promote civic engagement via the internet and regular community meetings

This data-centered effort can only engage those that already understand the charts, graphs, and terms they use.  Don’t get me wrong – they don’t deliver a dry academic lecture at their community meetings.  However, they do rapidly run through reams of data analysis with an expectation that most in the audience can handle the information-centered explanation.  This leaves out the many residents who don’t speak data at all.

What is Popular Education?

Philosophical definitions are always debated, but here are a few guiding principles most practitioners of popular education would adhere to:

• participation from all parties
• learner guided explorations
• facilitation over teaching
• accessibility to a diverse set of learners
• focus on real problems in the community

If you consider this list a litmus test for governmental data programs, few (if any) would pass.  So how do we change this?

Popular Data?

Now that you’re (hopefully) on board with my problem statement, and the idea that popular education can help, lets play out how. Popular data is my name for engaging, participatory approaches to data-driven presentation and decision-making.  Not a great name, but from an academic point of view it puts my work in the right family tree so I’ll use it for now. How do you structure data programs to practice popular data? Lets run through each of the tenants listed above and look at some examples.

Participation from All Parties

Popular Data suggests a “big tent” approach; you should get everyone at the table.  For instance, far too many open-data initiatives end at the release of the data.  The smart ones realize they are the scaffolding for larger efforts, and make a strong effort to convene non-profits, constituents, and the data makers to the table in order to encourage activity around the data.  Sometimes this looks like a hackathon that makes sure to invite lots of segments of society (ie White House hackathon). Sometimes this looks like a presentation of results back to the people the data is about (ie. Somerville’s ResiStat meetings).  There are lots of ways to involve those in power positions and those outside of them.

Learner Guided Explorations

Most data presentations are about as engaging as a conversation with your dentist! You kind of have to do it, but it’s booooring. Flipping the model invites your audience to find their own stories in the data. My Data Murals work does just that – our initial “story-finding” workshop shares a small portion of the data about a topic and then lets teams of participants find stories they want to tell.  Participants own these stories and advocate for them.  That is an empowerment story – our evaluations show people come away feeling more capable of finding stories in data, and are less intimidated by data in general.

Facilitation Over Teaching

In my Data Therapy workshops I use a number of activities for building visual literacy. All of these are ways to facilitate a discussion of data presentation, and build a shared language for describing data.  When data scientists introduce ideas they too often fall back on big words.  These words alienate those who haven’t studied data.  My first step is to use language a normal person would use.  Then I help the group construct their own language for describing data, which they fully understand.

Accessibility to a Diverse Set of Learners

I spent years designing interactive museum exhibits. Museums are the hardest setting I’ve ever designed for.  At a museum you know nothing about your audience; your object has to support 30 second interactions with a single person, but also 1 hour interactions facilitated by a knowledgeable docent.  This is hard.  Really, really hard.  Data presentations and activities need to be designed the same way. I address this by starting simple, and building to complexity.  In data presentations I do break into small groups and seed each with one person that does speak data to help the other folks understand technical issues.

Focus on Real Problems in the Community

This one is easy! Make the data you are working with or presenting relevant to the communities you are working with. In the workshops I lead in the Boston area, I use the Somerville happiness survey as my silly example data set.  I wouldn’t do that for a group of public health wonks (I’d use something from the WHO).  People are naturally inclined to be engaged about the community they live in – no need to introduce data from some far off community they have no relation to.

Is this Useful?

Ok, so I’ve made my argument – I see every dataset as an opportunity for engagement.  Engagement with the public, the people the data is about, the people whole collected it, everyone. If you’re reading this, it’s up to you to use a Popular Data approach to seize the opportunity for engagement a dataset gives you.  I find this framework useful for structuring my data presentations and workshops.  Let me know what you think!  Am I just naming something obvious? Am I being too academic?

crossposted to my Civic Media blog

Some Examples

What do I mean by “informal visualization”?  To start, toss out your computer, printer and graph paper. Pull our your crayons, big paper, tape, and your imagination.

From top-left, clockwise:

• One of Jose Duarte’s physical visualizations
• Willow Brugh’s illustration for my Food Resuce blog post
• Sebastion Errazuriz’s “American Kills” mural
• An illustration from my Data Therapy blog

Another example is the Data Mural pilots I’ve been doing with artist, facilitator (and my wife) Emily Bhargava.  We’re leading groups through finding a story in their data, creating a collaborative visual design for a mural, and then painting it! (read more on my Data Therapy blog and Emily’s Connection Lab blog).

Stuff Academics Say

I work at a university, so I have to mention some of the research in this area.  First up – there is a great paper out of the City University of London, called “Sketchy Rendering for Information Visualization“.  Basically, they get a computer to draw graphs as if they had been drawn by hand.  My main takeaway was that their “sketchy” graphs engaged people more than the more “official” looking ones with straight lines.

Secondly, the Data Stories podcast had a recent episode called “Data Sculpture” in which they spoke with people investigating physical data presentations.  If you listen to it, be prepared for a lot of academic jargon – their audience is not the general public.  My main takeaway from the paper referenced (“Evaluating the Efficiency of Physical Visualizations“) was that when people physically touched the 3d objects representing the data they did a better job understanding the data.

It’s Arts & Crafts Time

Beyond these examples, and academic rationale, making informal visualizations is just flat out more fun.  As with most things, I think there is a cultural issue involved here.  Western culture has an inexplicable (to me) emphasis on professionalism and looking like an expert. When I’ve worked in Central America, South America, and India I’ve found the professions more welcoming to informal data presentations like those I show above.  Perhaps this was due to resource constraints, but it almost always led to better sessions.

Whie doing my master’s in the Lifelong Kindergarten group here at the MIT Media Lab, I fully joined the tribe that talks about how making physical things is the best way to communicate your ideas. This “constructionism” approach has feuled all my work since then, and I see this call for informal visualization as a way to bring it to the dataviz world.

So what does this mean in practice?  For me, I’ve taken to doing less on the projector and more on paper.  I encourage community groups I work with in Data Therapy sessions to partner with local artists and schools. I push businesses and organizations to thing about their audience and goals harder before jumping into making data presentation.  (PLUG: come to my “Fight the Bar Chart” meetup here in Boston to learn more about that)

I’m using the word in more than it’s typical “I can read well” kind of way.  Here are some questions you should ask yourself:

• How literate is my audience about the issue I’m presenting?
• What pictures or graphs are most appropriate for their visual literacy level?
• How literate is the audience about me and where I’m coming from as a presenter?

The answers to these questions should inform what data presentation technique you pick.  When talking about creative data presentation options, a common comment I hear is that some parts of the audience want to see the “real” data – where real means “numbers in a table.”  To that I say fine, all well and good.  Supply a handout or an appendix that includes the data in tabular form.  That lets you please the traditional numbers people, but doesn’t stop you from engaging the rest of us that get bored by long lists of numbers.

What You Should Do:

Flesh out the definition of your audience(s) by thinking about their literacy. Use different and/or multiple techniques based on their background and knowledge.  Remember that their literacy will increase as you present, so don’t be complexity-phobic.

This photo shows a soda bottle filled up with just the amount of sugar in that drink.  This is a bit of a classic public health example; most people are surprised at the amount of sugar in a soda.  Representing this physically brings home the idea that when you drink the bottle, you’re consuming that amount of sugar.  A bar chart would be far less compelling, and you wouldn’t be able to relate to it.  This is a simple example, but the underlying concept is clear.

What You Should Do:

Consider whether your data can be brought off the page (or screen).  We live in an interactive, three-dimensional, world so you should be creative about bringing your data presentation into it. Surprising your audience with a novel display can engage them long enough for you to tell the rest of your story.

Background Information:

Here’s my standard breakdown of this data presentation:

• Who – group advocating for healthy eating decisions
• Goal – inform the audience about the amount of sugar they consume when drinking a bottle (and possible change their behavior)
• Audience – general public
• Data – photos of things they would like to change, quotes from patients about their experiences
• Technique – “physicalize” the data
• Tools – soda bottle and sugar

OkCupid, seeing their data as an asset, used to publish an insightful and entertaining blog called OkTrends. They were trying to come up with dating / relationship advice for people based on their warehouse of dating data. My goal in sharing this example isn’t to help you take more attractive pictures of yourself – but rather to talk about the way they share their complex data. These are very nerdy statistics people, but they present their data in entertaining and informative way.  After reading their blog for a while it became clear to me that they serve as a great example of some of the presentation strategies I like best.  Here are two examples that showcase how they start with something simple and build to something complex.

In a post about lies people tell online they start off with a cartoon-based joke about pretending to be someone you’re not.  Through their explanation they move to a complex, uncommon visualization showing how often men get contacted base on their age and income.

In another post, about what white people actually like, they start with a tag cloud of what people have said they are interested in.  Over the course of a single post they move to a complicated, multi-dimensional graph that correlates religious beliefs to writing proficiency.  Crazy.

What You Should Do:

Don’t worry about having an overly-complex data story.  Start with something simple and fun to get your audience interested, then they’ll be ready for your more complex data presentation once you get to it.