A few years ago I went to the first UN World Data Forum and made some amazing connections with non-profits large and small (read more about that here). A common theme at that event was how to help organizations and governments get the data they needed to start work on the Sustainable Development Goals.
I just returned from the 2018 event, and found a new message repeated over and over – how can we help those who have data communicate about its potential and its impact? I’ll write more about that later. For now I want to share a bit about the session I ran with my collaborator Maryna Taran from the World Food Program (WFP). It was a pleasure to return to the event where we first met and speak to the impact we’ve had at WFP, and how the Data Culture Project has grown to a suite of 7 hands-on activities you can use for free right now.
Empowering Those That Don’t “Speak” Data
Our session was designed to focus on bringing the non-data literate into the data-centered conversation. The idea is that we can help these folks learn to “speak” data with playful activities that try to meet them where they are, rather than with technical trainings that focus on specific tools.
We introduced our arts-centric approach to creating participatory invitations through the data cycle – from data collection, to story-finding, to story-telling. Specifically, we ran our Paper Spreadsheets activity and our Data Sculptures activity. Maryna also shared how the WFP has rolled out a data program globally, where the Data Culture Proect activities fit into it, and some of the impacts they’ve seen already.
The Paper Spreadsheet activity led to a wonderful discussion of data types, survey question create, and security concerns. The Data Sculptures folks created were a great mix of different types of stories, so I highlighted some of the scaffolding we’ve created for finding stories in data.
One of the most rewarding comments at the end was from a woman who worked on the data analysis side creating charts and such for her team. She noted that she often will share a chart with others on the team and they’ll say “tell me the story”, much to her frustration – she just didn’t understand what they meant. What more did they want than the chart showing them the evidence of the claim or pattern? She was pleased to share that after this session, she finally had a way to think about the difference between the charts she was making and the story that her colleagues might be looking for! Such a wonderful comment that resonated with a lot of the points Maryna was making about how and why WFP is rolling out the Data Culture Project activities in parallel with their more technical data trainings.
I’ve been connecting with more and more educators that want to take a creative approach to building data literacy with their students. Schools traditionally introduce data with in-class surveys and charting. This approach to generating their own data can be a wonderful way to empower learners to collect and represent data themselves. A more recent movement has centered around the STEAM movement – including the Arts along with the Science, Technology, Engineering, and Math curricular focus. I’m seeing a pattern at the intersection of these two approaches – educators are seeing strong engagement and results when they introduce their students to working with data through arts-based activities. Here’s a case study from a collaboration with the MIT Museum to flesh out how this can work.
Environmental Data Mosaics at the MIT Museum
This case-study was contributed by Brian Mernoff, one of my collaborators at the MIT Museum.
Each February, during Massachusetts school break, the MIT Museum runs a week of hands-on activities and workshops called Feb Fest. This year, the event was themed around our temporary exhibit, Big Bang Data, which explored how the increasing use of data affects technology, culture, and society. The purpose of the workshop was to let students view data sets of interest, understand these data sets, and share what they have learned with others in a creative and accessible way; all pieces of building their data literacy.
Data Sculptures as a Quick Introduction
As soon as students entered the classroom, they were asked to create a data sculpture based upon one of the sets of data placed at on their table. This is an activity the MIT Museum Idea Hub has already been running regularly. These data sheets contained relatively straightforward data sets to analyze, such as happiness in Somerville, and the cost of college over time. Art supplies were on the table, and the students worked with each other to create these sculptures while getting to know one another. After about half an hour each team presented how they decided to represent their data to the class. This activity was a great way to get them to get used to talking about data with each other and representing it in a novel way.
Building a Collaborative Data Mosaic
After presenting the data sculptures, we began the main activity for the day. Students were given a list of websites (see below) that they could visit containing environmental data in either graphical or numeric form (see the Environmental Data Search worksheet). Once they had explored the websites, they discussed these websites with a second group of two at their table and determined which one of the links was most interesting to them to explore for the remainder of the project. Once the website was chosen, they again worked in their original group of two to find a story in one of the data sets on the website using the “Finding a Data Story” worksheet. After doing so, the two smaller groups recombined and chose which of the two stories they would like to tell in the final project.
In their story, students needed to explain the problem the data connects to, what the data is and shows, why the data is important, what the audience of the story should do about it, and what would happen in the long run if the reader did what was suggested (see the Data Story Mosaic Layout worksheet).
Beyond these physical artifacts, the students’ discussions about data were particularly impressive. One group brought up a very interesting question about rare bird sightings and proceeded to debate it for about 15 minutes. They noticed that certain areas of the United States had more overall sightings of rare birds. At the same time, they looked at another data set on the same website showing the number of reporting bird observers across the country. Combining these graphs, they noticed that more rare birds are spotted where there are more reporters. This brought up the question of whether or not rare birds are actually as rare as shown by the data if there is such a close relationship between the two data sets. Both sides of the debate made good arguments and they eventually settled on the idea that the data was still valid, but incomplete. They would need more experiments in order to say anything conclusively. This demonstrates that the learners were in the “data headspace”, thinking about standard questions of representation, outliers, and normalization.
A second group, studying data on arable land, was trying to combine their data set with information on organic farming. This brought up good questions about what the terms “organic” and “GMO” actually meant, as well as whether or not it is related to the ability to reuse land over time. To their surprise, the students did some more research and realized that genetically modified foods and some types of “non-organic” farming actually increase what land can be farmed. Again, the activity pulled the learners into a space where they were curious and driven to understand the real-world approaches and impacts the data might be representing; making sure they understood what they had in front of them before finding a story to tell with it.
Overall, these projects allowed students not just to analyze data to find trends, but to think about why data is important and it can be used to find solutions to problems. Through their mosaics, students explored and discussed different potential solutions to determine which one they wanted to communicate with a larger audience.
The Opportunity of STEAM
Brian’s workshop is a wonderful example of how a creative arts-based approach to working with data can engage and proboke students in novel ways. It matches results we’ve seen in previous work on creating data murals with youth in Brazil, and working with a network of school on data challenges. These workshops are starting to help us build an evidence base for using the arts as an introduction to working with data. This can meet a larger set of students where they are. The physical artifacts and conversations around them are assets we use for evaluation and assessment. Are you an educator? We’d enjoy hearing how you are approaching this.
We’ve officially launched the Data Culture Project and are excited to introduce you all to it! Our collaborators at Stanford’s Digital Impact program are hosting a virtual roundtable for us on April 12th. Join it to learn more about creative approaches to building a data culture within your organization!
As part of it, we’ll be trying a hands-on activity online, and feature real stories from staff at two of our pilot partners – the World Food Program and El Radioperiódico Clarín.
The Data Culture Project: Building Data Capacity with Confidence
Most data trainings are focused on computer-based tools. Excel tutorials, Tableau trainings, database intros – these all talk about working with data as a question of learning the right technology. I’m here to argue against that. Building your capacity to work with data can be done without becoming a “magician” in some software tool.
Data literacy is not the same as computer literacy. This is an important distinction, because there are lots of people that are intimidated by computer technologies; but many of them are otherwise ready and excited to work with data. In my workshops with non-profits I find that this technological focus excludes far too many people. Defining data literacy in technological terms doesn’t welcome those people to learn.
To support this argument, let me start by describing what I mean by the skills needed to work with data. In my workshops we focuses on:
Asking good questions
Acquiring the right data to work with
Finding the data story you want to tell
Picking the right technique to tell that story
Trying it out to see if your audience understands your story
With Catherine D’Ignazio, I’ve been creating hands-on, participatory, arts-based activities to support each of these. Some involve simple web-based tools, but none are about mastering those tools as the skill to learn. They treat the technology as a one-button means to an end. The activity is designed to work the muscle.
Curious about how those work? If you want to learn how to start working with a set of data to ask good questions, use our WTFcsv activity. Struggling to learn about the types of stories you can find in data? Try our data sculptures activity to quickly build some mental scaffolding you can use.
Those are two quick examples. Here’s a sketch of all the activities we are building out and how they fit into the process I just described:
Some of these are old, and well documented on DataBasic.io; others are new and lightly sketched out on my Data Therapy Activities page; the rest are still nascent. We’re trying to build a road for many more people to learn to “speak” data, before they even touch tools like Excel or Tableau. These activities support this alternate entry point to data literacy; one that is fun and engaging to everyone!
Don’t get me wrong – there is certainly a place for learning how to use these amazing software tools. My point is that technology isn’t the only way to build data literacy.
You don’t need to be a computer whiz to work with data; you can exercise the muscles required with hands-on arts-based activities. We’re trying to build and document an evidence base demonstrating how the muscles you develop for working with data outside of computers easily transfer to computer based tools. Stay tuned for future blog posts that summarize that evidence…
I’m excited to be joining my friends at School of Data for a live online chat tomorrow on their new “Data is a Team Sport” show. Read the details and RSVP!
Lucy Chambers (of Tech to Human) and I will be talking about the ecosystem of tools, approaches, and activities for building data literacy. If you are someone that tries to train of introduce data to others then you should tune in!
I just hosted a workshop today at the Stanford Do Good Data / Data on Purpose “from Possibilities to Responsibilities” event. My workshop, called “Telling Your Story Well”, focused on how to flesh out your audience and goals well so that you can pick a presentation technique that is effective. We did some hands-on exercises to practice using those as criteria for telling your story well.
I just ran a workshop for attendees at the 2017 UN World Data Forum in Cape Town, called Empowering People with Data: tips and tricks for creative data literacy”. This was a great chance to connect my activities, and my work with Catherine D’Ignazio on DataBasic.io, to the non-profits and government statistical bureaus. We’ll be doing more of this, as NGOs are coming to me more often to talk about helping them build their capacity to tell strong stories with their information.
Many in the audience came up afterwards and were excited to bring the activities and approaches back to their organizations! Our fun activities were definitely new and novel for their world, and they immediately saw the value for many of the stakeholders they work with.
This week I’m at the Data Literacy Conference in France. One of the reasons I’m super excited about this because it is a gathering of people I’ve been wanting to talk to for years! Although there are tons of conferences about data, they are few conferences focused on the literacy aspect, so I thank Fing for putting this together. Catherine D’Ignazio and I both presented a talk and workshop. You see can see our slides for our talk about Bridging the Gap Between Data Haves and Data Haven-Nots. It focused on describing how to help two audiences:
We want to help those in power, the “Data-Haves”, learn how to present their data in more appropriate ways.
We want to help those that don’t usually have power, the “Data Have-Nots”, build their capacity to use data to create change in the world around them.
Too often we focus on just the second goal, ignoring the needs of those that have the data.
It was so fun to be able to have his conversation with a random set of curious folks. As we built things we chatted about loads of topics related to data literacy. Some people dig into how you could find simple or complex stories in such small datasets. Others explored how to present the impact of the data, not the data itself. Some decided to use totally different data, related to their lives. This variety created a great set of evocative examples that made discussions later in the afternoon even richer.
I used to do a lot more museum works, so it was a pleasure to be back in that setting. Museums prime people’s brains to be curious, so it’s wonderful to offer an invitation i that space to discuss and explore a topic more deeply. Actually when I was a student here at MIT i volunteered at the museum, helping run robotics workshops for kids and adults with my good friend Stephanie Hunt. It felt great to be back!
I look forward to dropping in when the museum staff runs this on their own. Can’t wait to see how they make it even better.
Here is a list of some of the data sculptures people made: