Advocacy That Works: 4 Ways To Turn Data Into Impact

Sun, 23 Mar 2025 17:00:00 +0000

“60% of the time it works every time.” -Brian Fantana (Played by Paul Rudd), Anchorman

What I learned about data advocacy from Ron Burgundy.

It was around Christmas when I sat with my brother in the living room watching Anchorman and laughed over the adventures of Ron Burgundy, the protagonist of the film (played by Will Ferrell).

The beauty of Anchorman is the satire. It portrays the childishness of misogyny in the workplace with a rare comedic elegance. Ron Burgundy and his posse, the epitome of the toxic “boys club” in the newsroom, are threatened by the presence of even just one female anchor on the channel, Veronica Corningstone (played by Christina Applegate).

However, what I want to talk to you about today is my favorite quote from the film, which comes from Paul Rudd’s character, Brian Fantana. It goes like this:

“60% of the time it works every time.”

Of course, Burgundy replies with what we already know: “That doesn’t make sense.”

But this got me thinking.

Oftentimes, the statistics we hear and the data we use to make decisions make similarly ludicrous claims.

The Birth Of A Statistic

All statistics have an origin story.

It’s easy to forget this.

Imagine you are conducting a study on coronary heart disease.

In one version of your study, your participants are all women aged 30-60. In another, they are men aged 20-50. In another study, your participants are all college students. In another, they are only vegetarians.

Within each of these, the simple choice of who gets to be a participant in your study drastically influences what kind of results you get, regardless of whether you are studying the influence of medication, lifestyle choices (e.g. diet or exercise), or anything else.

The same principle applies to whether your study is interventional or observational, how you measure the study outcomes, and even what rules your statistical analysis uses to deem a correlation “significant” or not.

It boils down to this: How you observe determines what you observe.

Thus, statistics are born from the lens and tools we are using to observe the world.

So when Brian Fantana claims “60% of the time it works every time”, we laugh, because we know it’s nonsensical to say something “works every time” if you are only looking at 60% of the attempts.

What we fail to acknowledge is that a lot of statistical claims work this way.

For example, it’s commonplace to extrapolate medical results from predominantly white, predominantly male samples to the entire population.

It’s common to extrapolate psychological findings from industrialized, Western populations to non-industrialized, Eastern populations.

We know these so-called “data-based” approaches are based on flawed assumptions, but we use them anyway.

We accept the error.

Science is objective, right?

Torture The Data!!!

Well… not entirely.

How we observe determines what we observe.

Data collection and analysis are both rife with bias, and this bias is only perpetuated by the belief that something so “technical” must by its very nature be objective.

So what do you do when you want to dive deeper into the story behind the data?

Torture it.

How To Torture Data (…In the name of advocacy)

Torturing the data is a lot like analyzing a poem.

First, you have to understand grammar. You must know the sounds each letter is making and the way letters come together to make words, and the way words come together to make each line.

Then, you have to understand connotation. The color black isn’t just a color; it’s a symbol. It communicates a sense of evil sometimes, but at others it was signal elegance or mystery. Sometimes, red symbolizes passion. At others, it is a sign of good luck.

But if you want to really dive into a poem, you don’t stop here.

If you dive deeper, you also ask about the context.

Who was the poet? What time period was the poem written in? What culture was this written within? Who was the poem written for? What are all the angles (if you can even access them all)?

Data is like this – telling countless different stories depending on who collected it, why, how, when, and where.

Data analysis is our way of dissecting what the story is, and maybe even more importantly, our way of deciding how to respond.

It’s an art almost as much as it is a science.

For now though, here are 4 frameworks you can use to leverage the data you have, turning it into impact.

Impact to Advocacy Tip #1: Think beyond numbers.

Numbers tell a story, but they don’t tell the whole story.

Data often has blind spots—missing contexts, underrepresented groups, or nuances that numbers alone can’t capture.

That’s why combining data with real conversations and lived experiences helps fill the gaps.

How to Apply This Approach For Advocacy:

Start with the Data – Identify key insights and trends from your dataset. What patterns stand out? What gaps or anomalies do you notice?
Go to the Source – Engage with the people behind the numbers. Talk to individuals, attend community meetings, or conduct informal interviews.
Ask “Why?” and “How?” – Use open-ended questions to understand the lived experiences behind the data. Example: If data shows a low participation rate in a program, ask why people aren’t joining. Is it awareness? Accessibility? Cultural barriers?
Compare Insights – Look for mismatches between the data and real-world experiences. If the numbers suggest one thing but people say another, dig deeper.
Refine Your Narrative – Adjust your analysis based on what you’ve learned, ensuring your conclusions reflect both statistical evidence and human reality.

Impact to Advocacy Tip #2: Use multiple metrics to measure impact.

Data isn’t just about defining the problem you’re dealing with.

It’s also about measuring your success at developing a solution.

Many people make the mistake of tracking only one metric, but real impact is multidimensional.

A single number rarely tells the full story, which is why looking at multiple data points ensures a more complete picture of success.

How to Apply This Approach To Advocacy:

Empathize – Understand the people affected by the problem. What challenges do they face? What does success look like to them?
Define – Use data to clearly articulate the problem. Instead of just saying “food insecurity is rising,” define who is affected, where, and why.
Ideate – Brainstorm potential solutions while considering how success should be measured beyond just one statistic.
Prototype – Implement a small-scale version of your solution while tracking multiple indicators.
Test & Measure – Instead of just tracking the number of meals distributed, also look at long-term impact (e.g., changes in income, school performance, or community health)..

Impact to Advocacy Tip #3: Ask “So what?”

Numbers might tell you what’s happening, but they don’t explain why it matters.

Too often, people stop at presenting statistics without considering their human significance.

For example, let’s say 30% of elderly people live alone.

So what?

Instead of responding with another number, consider: What does isolation feel like? How does it affect mental health? What support systems are needed?

By asking “So what?”, you move beyond data points to uncover the real-life impact behind them.

Then, instead of answering with more numbers, use empathy to connect the data to people’s lived experiences.

How to Apply This Approach In Advocacy:

Start with a Statistic – Identify a key data point related to your work. Example: “40% of low-income students drop out of college.”
Ask “So What?” – Why does this number matter? What’s the consequence of ignoring it?
Find the Human Story – Instead of citing another statistic, answer with a personal or collective experience. What does dropping out mean for a student’s future, mental health, or family?
Use This to Guide Action – Now that you understand the real-world impact, how does this shape your next steps? What kind of solution would actually help?

Impact to Advocacy Tip #4: Apply the Four Levels of Data Impact (DIKW Pyramid)

Not all data is created equal.

A statistic alone doesn’t lead to meaningful action—it has to be processed, interpreted, and applied.

The DIKW Pyramid (Data → Information → Knowledge → Wisdom) helps you move beyond just collecting numbers and toward making strategic, high-impact decisions.

How to Apply This Approach To Advocacy:

Start with Data – Collect raw numbers, facts, or statistics. (Example: 60% of startups fail.)
Turn Data into Information – Analyze patterns and trends to understand why something is happening. (Most failures occur due to funding gaps.)
Convert Information into Knowledge – Identify the deeper implications. (Minority-owned businesses are disproportionately affected.)
Apply Wisdom – Use insights to make informed, actionable decisions. (We need policy incentives to close the funding gap.)

Data Is Your Friend.

Data is all around us, and it always has been.

The only difference between data now and at other points in history is that now we collect more of it than ever before.

In many ways, this is scary, but I invite you to think about it differently.

I invite you to be empowered by this.

We don’t have to play a guessing game anymore when it comes to making an impact.

We don’t have to jump so many hurdles to access the vast sea of databases and scientific journals.

With a wifi connection and a laptop, anyone can use open-source datasets and scientific articles from online to build their own evidence-based solution to the world’s problems.

If we want a starting point for where to make an impact, we need not look any further than our computer screens.

So why not start using these tools today to dive into the data, to think critically about advocacy?

I promise it’s easier than you think, and the impact will surprise you.

Thought to Action

Self-Educate: Immerse yourself in the world of data literacy. Utilize free online courses and resources to understand data collection, analysis, and interpretation. This foundational knowledge will empower you to harness data effectively in your advocacy efforts.
Engage with Communities: Combine quantitative data with qualitative insights by engaging directly with the communities you aim to support. Attend local events, conduct interviews, or organize focus groups to gather personal stories that add depth to your data.
Diversify Metrics: When measuring the impact of your initiatives, go beyond surface-level statistics. Consider multiple metrics that reflect both immediate outcomes and long-term effects, ensuring a comprehensive evaluation of your efforts.
Ask “So What?”: For every data point you encounter, challenge yourself to understand its real-world implications. Reflect on how the numbers translate to human experiences and adjust your strategies to address these underlying issues effectively.
Apply the DIKW Pyramid: Transform raw data into actionable wisdom by progressing through the stages of Data, Information, Knowledge, and Wisdom. This approach ensures that your advocacy is informed by deep insights, leading to more impactful outcomes.