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  • Writer's pictureflynnkristina

Tech is Terrifying. And Other Lessons I Learned at Data on Purpose, 2019.

The Stanford Social Innovation Review gave me a scholarship ticket to attend the Data on Purpose conference. In appreciation, I thought the very least I could do is share some of my takeaways. These are my top three lessons:

  1. Technology is HARD, particularly for social sector organizations. But that’s okay.

  2. Data is a process, not a result.Understanding data security makes it WAY less scary.

  3. Let’s get a little deeper and I’ll share what encourages me.

1. Dear Social Sector, You Are Not Idiots.

Technology is hard for many, especially for nonprofit organizations. They often lack the capacity and resources for even basic administrative functions. So it’s not surprising that they may also lack the capacity to quickly adapt to tech advances.

Their focus is on the mission. As it should be. So if tech organizations want to be helpful, we should start by acting as interpreters. Help explain the complex. Because maximized understanding leads to maximized impact.

@alixtrot described being without technological capacity in today’s society as being without power. It means exclusion from fundamental decision making. How can you request and demand change for things you don’t understand?

What Encourages Me: most tech implementation can be learned within a few months. But it often takes a lifetime of experience to understand the civic sector. Advancement is inevitable if we create safe spaces for people to ask and learn.

2. Beauty is in the Process.

Missions come first. They’re what make nonprofit organizations so amazing. We need to create a data culture that aligns with those same missions, not the other way around.

“Excel doesn’t help you ask the right questions. Tableau won’t help you figure out which story to tell. R won’t tell you which chart to choose to speak to your audience. You need to work on your data culture, not just your technical tool tricking.”- @rahulbot

The process is just as important as the information you’re collecting. Good solutions need to respect and understand the culture of those we engage.

What Encourages Me: Rahul Bhargava's simple framework to apply to your process

  • Determine the outcome(s) you want to showcase

  • Get to know the community you’d like to engage

  • Gather only the data that supports the intended outcome(s)

  • Build an understanding of that dataWork with the community to express it

  • Tell a story that honours them

Simple, right?

3. Understanding Data Makes It Much Less Scary.

The most eye-opening discussion of the conference was from @mroytman. He taught me that “50% of data security compliance is in the way the data flows”.

Now, doesn’t that already make you feel better?

Disparate solutions are one of the greatest security threats to social sector organizations. Every step of the process presents a risk for data loss or exposure. But with limited capacity and resources, many nonprofits turn to free solutions like Excel, Google Forms, MailChimp, and Survey Monkey.

This added to the other steps of data flow — staff, computers, phones, etc. — and security can present a real problem.

Here are some steps to start reducing risk:

  • Build a culture of awareness, ownership, and responsibility around organizational and beneficiary data

  • Clean up the process and limit the number of steps

  • Document the revised process and train your team on how to reduce the risks

What Encourages Me: we can be in control of a large majority of this scary stuff. And with education and awareness, positive progression for the sector is entirely possible.

— -

Over two days of content, I couldn’t help but get excited about the benefits of data and technology to the sector. But I remind myself that a barrier to this excitement is the limited ability of nonprofits to collect reliable data.

You’ve heard the term before “Crap In, Crap Out”. The barrier is real. Organizations doing incredible things are collecting data on paper. Others are using a myriad of free tools to “make it happen”. Without solving this problem, predictive analytics is useless.

Technology is one way to help. But unlocking this potential requires resources, investments and permission by funders.

This problem gets us out of bed in the morning. We work extremely hard to think in the now and meet nonprofits where they are. To not let data’s potential force us to lose sight of the fact that first, we must take baby steps.

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