You don’t need complicated software to learn how to work with data

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:

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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…

You Don’t Need a Data Scientist, You Need a Data Culture

Most of the larger non-profit organizations we work with are scrambling to figure out how to deploy complex technologies like machine learning and “AI” in service of the social good. These include inspiring examples that range from poverty alleviation, to home fire prevention, to self-harm risk reduction.  These stories have spread widely and have come to define what a data-centric organization should be doing – namely complicated data science.  However, if you’re an organization thinking about how to use data better, this is not where you should start.  You don’t need a data scientist, you need a data culture.

Catherine D’Ignazio and I have built the DataBasic.io tools to focus on helping people creatively build their data literacy.  As more and more organizations have started using them, we’ve been pushed to think more deeply about what it means to take this approach to building a data culture.  This post lays out our latest thinking abut the building a data culture, and how to overcome barriers you’re likely to run into.

The key problem we see is that organizations working for the social good don’t feel empowered to work with data in a variety of ways. This is a rank-and-file staff problem, not a data scientist problem. We’ve come to work on this in three ways:WFP_DMC_building_a_data_culture.png

Organizations don’t feel confident that they can work with data at all, so to build a data culture we prioritize building confidence through small, focused activities. The technology that they think they need to work with data is daunting, expensive, and requires technical expertise, so our approach focuses on approaches that don’t rely on complex technology.  Organizations don’t have a good process for starting to work with data, so we introduce a step-by-step approach with hands-on activities.

We’re trying to help here by creating the “Data Culture Project” – you can expect to hear more about that early next year.  This gives organizations a lightweight, self-service curriculum or video-facilitated activities.  We’re piloting that with 30 organizations right now, to learn from how they approach running these over three months within their organizations.

What is a “Data Culture”?

This phrase is becoming a bit of a buzz-word right now. So what does it mean? After lots of conversations, with organizations big and small, we’ve narrowed down to this list:

  • Leadership prioritizes and invests in data collection, management and analysis/knowledge production.
  • Leadership prioritizes creative data literacy for the whole organization, not just IT and Evaluation.
  • Staff are encouraged and supported to access, combine and derive insight from the organization’s data.
  • Staff recognize data when they see it. They offer creative ways to use the organization’s data to solve problems, make decisions and tell stories.

This four-part definition focuses on leadership and staff responsibility very intentionally.  You need buy in across the organization to really make this work. We also focus on making sure data doesn’t get siloed into one department or another. Working with data is a core skill that can be valuable across an organization.

Why Build a Data Culture?

Why bother with building a data culture?  Over the last 10 years we’ve seen a lot of data projects in our workshops and partners. These tend to cluster around three purposes.

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Data is most often used to improve operations;  doing things like measuring delivery performance, changing how it works, and them measuring it again to see if it improved.  One the last years we see more and more uses of data to spread a message, giving rise to infographics and other formats where data is used to show impact of programs.  Data is less-often used to bring people together, which is the focus of my work on arts-based hands-on activities, data murals, and more.  We think this third purpose is central to building a strong data culture across your organization.

 

Barriers to Building a Data Culture

Of course, like any organizational change, there are barriers. We’ve listed 6 that we think are useful to have in mind while thinking about any efforts you are taking to build a data culture.

Barrier #1: Confusion

Most introductions to data are confusing and overly technical.

Complicated words can alienate people that are just entering the field of working with data.  Pick your words carefully to welcome them.  For instance, you could introduce the idea of “correlation” by talking about “connections” between pieces of data that move together.

Piaget, the great educational psychologist, introduced us to the idea that people will absorb new information by “assimilating” it into their existing mindset, or change their mental model to “accommodate” it.  If you know people’s background you can make your outreach more effective. You have to understand their existing mental models if you want to introduce new information. Your goal is not to turn everyone in the organization into data scientists. A data culture means people recognize data and are able to pinpoint new opportunities for deriving knowledge and insight from it.

Tips:

  • Avoid technical jargon
  • Meet people where they are

Barrier #2: Not Knowing Your Data

Sometimes you don’t even know the data you have.

At a recent workshop we were talking with a medium-sized environmental advocacy group and they lamented not having any data about participation at recent public events.  I mentioned that I had seen photos on facebook, and how that was data they could use. They were surprised and had ignored this set of data, yet it contained exactly the data they wanted.

Remember that data can be qualitative or quantitative.  If your development director shares photos and a headcount from your last fundraiser, that’s all data. Be creative about recognizing the data you have already.

It is hard to keep track of datasets within your organization that might be related to each other.  Identify a person and a technology that can be a central clearinghouse for data.  This could be as simple as a Word document with a bulleted list, or as complex as a internal data portal.

Tips:

  • Keep your eyes and ears open
  • Build a data catalogue, or library

Barrier #3: Organizational Silos

People will fight efforts to work across silos.

We were working with a large nonprofit to build a data culture across their organization, but they were stymied by people that thought they owned the data, and were hoarding it from others as a form of job security.  The only way we found to work on it was risky – to sneakily use it and then credit its successful use to the owner retroactively.  It helped, but we can do better than that.

Most organizations suffer from these silos – independent functional units that take pains to control a slice of the overall work. You have to acknowledge these walls in order to break them down.

When you have an example of a data-centric project that cuts across existing silos, hold it up as an example to success.  This is an opportunity to have leadership show buy-in and backing for a cross-sectional approach to data.

Tips:

  • Acknowledge your weaknesses
  • Highlight successes

Barrier #4: IT-Centric Thinking

Data gets locked away in the IT department.

Over and over we hear from organizations where IT is running Tableau trainings regularly and they just can’t understand why people aren’t adopting the tool and approach.  I’m like a broken record telling them that you need to separate the tool and the process – the tool training can be owned by IT, but the process training doesn’t need to be.

You need to make sure people don’t have to go to IT to pull out the latest numbers they need. Building a data culture means making sure every part of your organization can use data, for a variety of reasons.

Just because IT owns the data technology, it doesn’t mean they should own the process of creating a data culture.  Building this capacity is better housed across multiple departments, or within the office of a Chief Data Scientist.  That can lead to invitations to build data capacity that are more fun that just boring spreadsheet trainings.

Tips:

  • Data is for everyone
  • Create more invitations to work with data

Barrier #5: Irrelevance

Staff don’t connect to many high-level data dashboards.

High-level data summaries are great for leadership, but staff can’t always connect to them.  You need to integrate data into their day-to-day operations.  You can try ideas like mainstreaming quarterly data-reports from each department, or attaching data outcomes to program reviews. If staff don’t understand and the utility and use of data they are collecting, it just becomes boring homework they have to do. This hurts not only your data culture, but also the data quality!

Showing a number of summary of some data is great, but is just the start.  Asking “so what?” is when the real culture starts to emerge.  Actionable data can help you drive your organizational goal.  If people can’t answer the “so what” question, then they don’t have the right data. Engage staff in figuring out why the data they collect is useful; they are best positioned to answer the “so what?” question.

Tips:

  • KPIs aren’t for everyone
  • Remember to ask “So What?”

Barrier #6: Boredom

Data is seen as a boring chore.

Spreadsheet-driven activities are boring to the majority of people.  Use more fun activities, in novel settings, to bring a more creative approach to data. Make data sculptures in the lobby, or paint a data mural at your next retreat.  These approaches create multiple pathways into learning how to work with data.

Communicating in charts and graphs is the default for presentations.  However, these don’t tell a story.  Encourage your organization to put the data in context, and talk about impact, but focusing on how to tell a story with your data rather than just introducing how to do Pivot Tables. People like telling stories, and get interested and engaged in hearing them.

Tips:

  • Use creative data-centric activities
  • Tell stories with your data

Building Your Data Culture

Each organization is different.  Hopefully this high-level summary of some of our latest thinking helps inspire ideas what might work for you.  In future posts we’ll dig into more concrete ways to build a data culture, the motivations behind them, and how they are working for various partner organizations we work with.

This post is based on a presentation Catherine D’Ignazio and I gave to non-profit leaders convened by the Stanford Social Innovation Review. Thanks to Catherine D’Ignazio and Ethan Zuckerman for feedback and edits.

Fight the Quick Chart Buttons

I despise the “quick chart” buttons. This post explains why, and tries to help you go from making charts to telling stories.

Here’s an example of the quick chart buttons in Excel:

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Excel’s list of chart buttons doesn’t help you pick the right chart to show your data.  Caveat: newer versions try to help with a “Recommended Charts” option.

Most of our chart-making tools don’t help us pick the best chart to tell our data story, and this is a big problem for chart makers. They just offer up a set of options to let you quickly make a chart. That doesn’t help you put together a data story! We just end up with lots of bar charts and line charts 😦

I love chart picker guides like the PolicyViz’s Graphic Continuum, Abela’s Chart Suggestions, and the FT’s Visual Vocabulary.  These guides reframe the question of picking a chart as a question of identifying your story. That is a crucial distinction.

The visual depiction of information in a chart is an editorial process, not some objective representation of the data. The visual mapping of the data onto shape, color, position, and size are all subjective choices you should be making make. These should be conscious decisions, not at the mercy of some tytranical default button. The result of all these decisions should be a chart that is closer to a story then simple raw data.

Look at the difference between these two charts for an example:

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Same data; different story.

The chart on the left might tell a story about Dragon Fruit underselling as compared to other fruits.  The chart on the right might tell a story about apples being a dominant player in the market that needs to be fought.  These are two very different stories; and all I did was change the color of one bar!

The key question is: what is your story? what chart can help you tell that story?

Anyway, back to the quick chart buttons. They don’t help you pick which chart to make! Bar charts are good for showing comparisons between a few categories within a dataset. What about when you want to show changes over time (line chart)? Or a distribution of two variables (scatter plot)?  Or the promotional share of one category compared to the total (pie chart)?

Different stories demand different charts.  So next time you’re putting a chart together, start by thinking about the type of data story you’re trying to tell. Then use a guide to find the right chart to show it. Don’t be seduced by the promised simplicity of the “quick chart” button!

Approaches to Teaching Data for Non-Profits

Recently The National Neighborhood Indicators Partnership and Microsoft Civic Technology Engagement Group launched a project to expand training on data and technology to improve communities.  I’m pleased they’ve included Data Therapy as one of the resources they highlight to help you think about building your data culture.  Check out their training guide and their catalog of resources!

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On a related note, if you are someone that does a lot of training and capacity building, or an organization that wants to be doing that, checkout the podcast and recording of a conversation about enabling learning with School of Data.

Making Tools More Learner-Friendly

I often advise learners to be careful with what tools they choose to spend time learning.  Some powerful ones have steep learning curves, full of jargon and technical hurdles.  Others are simple and self-explanatory, but can’t do more than one thing.  I’ve been trying to find better ways to connect with tool builders and talk to them about how they need to build learner-centered tools.

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:

New DataBasic Tool Lets You “Connect the Dots” in Data

Catherine and I have launched a new DataBasic tool and activity, Connect the Dots, aimed at helping students and educators see how their data is connected with a visual network diagram.

By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach to understanding it.

The new tool gives users a network diagram to reveal links as well as a high level report about what the network looks like. Using network analysis helped Google revolutionize search technology and was used by journalists who investigated the connections between people and banks during the Panama Papers Leak.

Connect the Dots is the fourth and most recent addition to DataBasic, a growing suite of easy-to-use web tools designed to make data analysis and storytelling more accessible to a general and non-technical audience launched last year.

As with the previous three tools released in the DataBasic suite, Connect the Dots was designed so that its lessons can be easily planned to help students learn how to use data to tell a story. Connect the Dots comes with a learning guide and introductory video made for classes and workshops for participants from middle school through higher education. The learning guide has a 45-minute activity that walks people through an exercise in naming their favorite local restaurants and seeking patterns in the networks that result. To get started using the tool, sample data sets such as Donald Trump’s inside connections and characters from the play Les Miserables have also been included to help introduce users to vocabulary terms and the algorithms at work behind the scenes. Like the other DataBasic tools, Connect the Dots is available in English, Portuguese, and Spanish.

Learn more about Connect the Dots and all the DataBasic tools here.

Have you used DataBasic tools in your classroom, organization, or personal projects? If so, we’d love to hear your story! Write to help@databasic.io and tell us about your experience.

Telling Your Story Well

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.

One key takeway is the reminder to know your audience and your goals before deciding how to tell your data-driven story.

Folks dove into the activity we did – remixing an infographic to target a specific audience and an achievable change.

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For example, here’s a sketch of one group’s idea of an interactive data sculpture that dumps stuff on you based on how much water your purchases at a grocery store took to generate!

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Creating Ethical Algorithms – Data on Purpose Live Blog

This is a live-blog from the Stanford Data on Purpose / Do Good Data “From Possibilities to Responsibilities” event. This is a summary of what the speakers at the talked about, captured by Rahul Bhargava and Catherine D’Ignazio. Any omissions or errors are likely my fault.

Human-Centered Data Science for Good: Creating Ethical Algorithms

Zara Rahman works at both Data & Society and the Engine Room, where she helps co-ordinate the Responsible Data Forum series of events. Jake Porway founded and runs DataKind.

Jake notes this is the buzzkill session about algorithms. He wants us all to walk away being able to critically assess algorithms.

How do Algorithms Touch our Lives?

They invite the audience to sketch out their interactions with digital technologies over the last 24 hours on a piece of paper. Stick figures and word totally ok. One participant drew a clock, noting happy and sad moments with little faces. Uber and AirBnb got happy faces next to them. Trying to connect to the internet in the venue got a sad face.  Here’s my drawing.

Next they ask where people were influenced by algorithms. One participant shares the flood warning we all received on our phones. Another mentioned a bot in their Slack channel that queued up a task. Someone else mentions how news that happened yesterday filtered down to him; for instance Hans Rosling’s death made it to him via social channels much more quickly than via technology channels. Someone else mentioned how their heating had turned on automatically based on the temperature.

What is an Algorithm?

Jake shares that the wikipedia-esque definition is pretty boring. “A set of rules that precisely deinfes a sequence of operations”. These examples we just heard demonstrate the reality of this. These are automated and do things on their own, like Netflix’s recommendation algorithm. The goal is to break down how these operate, and figure out how to intervene in what drives these thinking machines. Zara reminds us that even if you see the source code, that doesn’t help really understand it. We usually just see the output.

Algorithms have some kind of goal they are trying to get to. It takes actions to get there. For Netflix, the algorithm is trying to get you to watch more movies; while the actions are about showing you movies you are likely to want to watch. It tries to show you movies you might like; there is no incentive to show you a movie that might challenge you.

Algorithms use data to inform their decisions. In Netflix, the data input is what you have watched before, and what other people have been watching. There is also a feedback loop, based on how it is doing. It needs some way to figure out it is doing a good thing – did you click the movie, how much of it did you watch, how many star did you give it. We can speculate about what those measurements are, but we have no way of knowing their metrics.

A participant asks about how Netflix is probably also nudging her towards content they have produced, since that is cheaper for them. The underlying business model can drive these algorithms. Zara responds that this idea that the algorithm operates “for your benefit” is very subjective. Jake notes that we can be very critical about their goal state.

Another participant notes that there are civic benefits; in how Facebook can influence how many people are voting.

The definition is tricky, notes someone else, because anything that runs automatically could be called an algorithm. Jake and Zara are focused in on data-driven algorithms. They use information about you and learning to correct themselves. The purest definition and how the word is used in media are very different. Data science, machine learning, artificial intelligence – these are all squishy terms that are evolving.

Critiquing Algorithms

They suggest looking at Twitter’s “Who to follow” feature. Participants break into small groups for 10 minutes to ask questions about this algorithm. Here are the questions and some responses that groups shared after chatting:

  • What is the algorithm trying to get you to do?
    • They want to grow their user base, and then shifted to growing ad dollars
    • Showing global coverage, to show they are the network to be in
    • People name some unintended consequences like political polarization
  • What activities does it use to do that?
  • What data drives these decisions?
    • Can you pay for these positions? There could be an agreement based on what you are looking at and what Twitter recommends
  • What data does it use to evaluate if it is successful?
    • It can track your hovers, clicks, etc. both on the recommendation and adds later on
    • If you don’t click to follow somewhere that could be just as much signal
    • They might track the life of your relationship with this person (who you follow later because you followed their recommendation, etc)
  • Who has the power to influence these answers?

A participant notes that there were lots of secondary outcomes, which affected other people’s products based on their data. Folks note that the API opens up possibilities for democratic use and use for social good. Others note that Twitter data is highly expensive and not accessible to non-profits. Jake notes problems with doing research with Twitter data obtained through strange and mutant methods. Another participant notes they talked about discovering books to read and other things via Twitter. These reinforced their world views. Zara notes that these algorithms reinforce the voices that we hear (by gender, etc). Jake notes that Filter Bubble argument, that these algorithms reinforce our views. Most of the features they bake in are positive ones, not negative.

But who has the power the change these things? Not just on twitter, but health-care recommendations, Google, etc. One participant notes that in human interactions they are honest and open, but online he lies constantly. He doesn’t trust the medium, so he feeds it garbage on purpose. This matches his experiences in impoverished communities, where destruction is a key/only power. Someone else notes that the user can take action.

A participant asks what the legal or ethical standards should be. Someone responds that in non-profits the regulation comes from self-regulation and collective pressure. Zara notes that Twitter is worth nothing without it’s users.

Conclusion

Jake notes that we didn’t talk about it directly, but the ethical issues come up in relation to all these questions. These systems aren’t neutral.