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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Data, What is it Good For?

I recently led a short session at the inspiring Southern Poverty Law Center called “Using Data to Create Change: Real World Examples”.  Here is a short write-up of some of the examples I shared.

The hype around data has reached such heights that it is in danger of going into low-earth orbit! Being drenched in stories about the potentials for data to change your organization and your work, it is sometimes hard to pick apart the motivations and reasons to using data.  Unlike my blog title suggests, I’m not here to argue that data is good for “absolutely nothing”. I like to look at data as an asset for your organization, but focus in and talk about how it can help you in three concrete ways:

  • You can use data to improve internal operations
  • You can use data to spread the message
  • You can use data to bring people together

Here are four short stories to help pick these apart.  I live and work here in the US, so these case studies are all American.

Designing a Mural

Groundwork Somerville is a organization that works in my hometown here in Somerville, Massachusetts in the US.  One of their big projects involves reclaiming unused urban lots and helping youth build and maintain raised beds to grow vegetables.  They then sell these vegetables at cheap prices from a mobile market that visits multiple local sites weekly. For those of you in other countries, this is a big problem here in the US, where unhealthy food is generally far cheaper than healthy fresh food.

Created by Growndwork Somerville (August 2013)
Created by Growndwork Somerville (August 2013)

To build skills in their youth programs, share their work, argue for more support, and have fun, we worked with local youth to design and paint a Data Mural.  They looked at the urban landscape, quotes from youth in the program, public health data, and participation in the mobile market to craft a story and mural speak to the internal and externals impacts the program has.

We used this kind playful engagement of data to bring people together and spread the message.

Using Metrics to Drive Engagement 

Here I’m going to retell a story that is often pointed to, most succinctly in Beth Kanter’s Measuring the Networked Nonprofit.  This is the story of how online news site Grist.com uses social media metrics and other data to move people up their ladder of engagement.  Grist tries to bring a light, playful, and new framing to issues that are important to folks who care about the environment. Folks that might not self-identify as “environmentalists” per say.

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The Grist.org ladder of engagement

Grist does deep dives into their web and social metrics to understand what is important to their readers from a short-tem and long-term point of view.  They try to respond to these interests with editorial decision-making and sometimes in near-realtime content generation. Grist uses a strong ladder of engagement to prompt people to engage and own the narratives of stories about environmental issues, knowing that that will make them more likely to act to solve problems.

This attention to metrics and constant checks of their ladder of engagement is a great example of using data to improve internal operations and spread the message.  Read more about this in the book Measuring the Networked Nonprofit (by Kanter and Paine).

Creating Insights and Action

Their third story I want to share is about a small company in Detroit called LoveLand Technologies.  Over the last few years Detroit has been a city in crisis, recording record foreclosure rates, stuck with barely functioning public utilities, and having to file for bankruptcy protection.  In this context LoveLand stared making some simple maps of property in tax-related distress and foreclosure.  These were maps of people losing their homes.

The LoveLand map of foreclosures in Detroit (circa 2014)
The LoveLand map of foreclosures in Detroit (circa 2014)

Before they knew it, their maps were being used in a variety of unforeseen ways. Government officials were relying on them as the data source of record.  Churches were using them to raise funds for their neighbors in need.  Folks with deep pockets were ready to give them money to do even more work around urban blight in the city.

Their data was being used to improve internal operations, spread the message, and bring people together!  If you want to learn more read Ethan Zuckermen’s liveblog of a talk Mike Evans did recently at the MIT Center for Civic Media.

Guiding Program Decisions

My last story is the most high tech. It comes from DataKind, and organization that pairs data scientists with nonprofits to think through and implement projects focused on data analysis.  GiveDirectly started working with DataKind to get help targeting their unconditional cash transfers to those the money could help the most.  They’re a very data-centric organization already, so working with DataKind volunteers on some advanced topics just made sense!

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A screenshot of their UI identifying roof types from satellite images (from the DataKind blog)

Data scientists Kush Varshney and Brain Abelson worked with GiveDirectly to understand how satellite imagery could be analyzed by computers to identify areas where aid funds would best be directed.  Based on the existing research showed a strong correlation between a villages wealth and the number of iron (vs. thatch) roofs, they created an algorithm that attempts to count iron and thatch roofs in satellite imagery. It is important to note that it doesn’t quite work yet, but it is important to think about novel applications for data mining that can create new types of data to help your work. Hopefully they can continue to tune the algorithm to improve their results and turn into a useful tool.

This analysis and tool building is trying to improve internal operations so GiveDirectly can do their work better.  Watch their technical talk to learn more.

Wrapping Up

There are just a handful of my favorite stories to illustrate the variety of ways you can use data to help you make change in the world.  Are their counter-examples illustrating the perils and pitfalls of using data in any of these ways.  Of course. I strive to highlight those stories just as often… but that’s a list for a different blog post!  I hope these four help you start to think about creative and new ways your organization might be able to turn all the data hype into something useful.

For reference, here’s a link to the presentation that went along with this talk:

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Architectures for Building a Data Culture

This is a summary of one section of my workshop on Data Architectures at the SSIR Data on Purpose workshop.

Organizations all around the world are asking themselves how to build a data culture within their walls.  Of course, this means something different for each of them.  However, I want to introduce you to my process for answering that question.  I rely heavily on Beth Kanter’s amazing work in this space, specifically her book Measuring the Networked Nonprofit (co-written with KD Paine).

There are three guiding questions you can use to lead you through this process. I’ll go into each one in detail in this blog post.

  • What is a data culture?
  • What is our existing data culture?
  • How do we build a data culture?

What is a Data Culture?

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First off, it is important to define what a data culture means to you.  We toss around a lot of phrases to tease that out, so I find these little comics illustrative of the differences between some of these labels.

  • We you’re data-centric, you bring people together around data as the central driver to help make decisions
  • When you’re data-informed, you take the data and it’s context as inputs to your conversation and decision make process
  • When you’re data-driven, you look at the data to find out what to do or how to approach something

Sure, these are kind of caricatures of those terms, but they’re helpful.  As with most things, I like Beth Kanter’s description of some of these differences.  Not surprisingly, I agree with her and advocate that organizations take a data-informed approach.

What is Our Existing Data Culture?

Before coming up with a plan for building the data culture you want to see in our organization, you have to understand the culture that is already there.  Looking internally at your organization structures and practices can feel tiring, but it is a necessary time to put on your anthropologist hat.  Here are some questions that might help:

  • Are there data champions already using data in good ways that you can celebrate as models to duplicate?
  • Are the roles in your organization aligned with your data needs?
  • Is there a central person setting policies and best practices when it comes to your data-related work?
  • Do you have a data group? A Chief Data Officer? A Data Scientist?  Or are those labels too much for your small organization?
  • Who owns the data being collected, and do they have incentives to share it across the organization?

How do we Build a Data Culture?

Changing the internal culture of any organization is slow work.  Beth’s crawl-walk-run-fly model (borrowing from the MLK quote) is a fantastic approach to this.

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slide from Beth Kanter, used here with her permission

She is, of course, focused on internal processes and measurement for social media (that’s what she does), but the approach is valid for various types of data work.  There are a multitude of strategies she suggests for building this kind of culture:

  • look for internal advocates / experts
  • look for key exemplars
  • build external relationships
  • lead from the top and from below
  • baby steps are ok

Seriously, just go buy and read the book already.

Pitfalls

Of course, there are dangers and barriers you will have to overcome.  First off, remember that people tend to measure what is easy to measure, not necessarily what is important to measure.  The way to overcome this is to create a critical data culture that constantly asks questions like “what does this data help us do?” and “what is missing from this data?”.  Another common barrier is organizational fiefdoms that don’t want to share their data with other.  You can respond to this by incentivizing sharing of data and highlighting examples that do.

There will be other challenges on your path to building a data culture, but remember your goal.  Data-informed decision making and communication has already emerged as a key skill you need to have to help you create the change you want to make.  You need to build a data culture within your organization to advance your work. I hope these tips help!