# Introduction
Every organization likes to call itself “data driven.” It has become the gold standard for honesty, what you say to shut down objections in a meeting. But here's something to sit with for a moment: the phrase “data analysis” can come from two very different places.
Another genuine curiosity. Another is a person who already knows what he wants and went to look for a number to look at.
And the weird part? Both of those people ended up pushing the same decision, using the same language, sitting on the same side of the table. That association is more common than you might think, and it has a name.
# Bootleggers and Baptists
Back in 1983, regulatory economist Bruce Yandle introduced a concept he called “Bootleggers and Baptists.” This idea came from awareness of Sunday liquor laws in the American South. Baptists push those laws on moral grounds. They believe that banning the sale of alcohol on Sundays is the right thing to do. Bootleggers, on the other hand, liked the exact same rules because they ended their legal competition for one day.
Both groups wanted the same result, but for completely different reasons. Baptists provided a moral cover, an obvious justification that politicians could point to. Bootleg sellers work behind the scenes, quietly profiting from the result. Yandle's understanding was that these unexpected minerals often produced more successful control results than any group could achieve alone.
It is a powerful framework. And it maps the world of data and statistics with uncomfortable precision.
In any data-savvy organization, you'll find people who genuinely try to let evidence guide their decisions. These are your Baptists. They want clean data pipelines, better dashboards, rigorous A/B testing. They stress the importance of statistics not because they serve their own agenda, but because they believe that better data leads to better results.
These people are easy to spot. They are the ones who change their minds when the data contradicts their hypothesis. They are free to say “I was wrong” or “we need more information before we move.” They treat data like a flashlight in a dark room – something that helps everyone see clearly, even if what's being shown is distracting.
Data Baptists truly believe in the principle, regardless of how the data is constructed. And that belief is exactly what makes them useful to bootleggers.
Now meet on the other side. These are people who already have a conclusion and are backing up the data story to support it. They are fluent in the language of evidence. They can crunch numbers, reference dashboards, and present findings on polished slide decks. But the analysis process they followed was not really open. The destination is fixed before the journey begins.
Data bootleggers do things like cherry pick time ranges that support the trend they like. They will choose metrics that flatter their implementation while silently ignoring those that don't. They will rely on the link if it suits them and wave you off if not. And they rarely, if ever, present data that contradicts their position.
Say someone wants to create AI-generated ads. They will add up the click rates from the two week trial and call it a win. What they won't say is that bounce rates have doubled, time on page has decreased, and campaign costs per acquisition have actually increased. AI ads got clicks, sure. But so are misleading icons. The full picture tells a very different story, and that's why they don't show the full picture.
What makes them successful is that they sound exactly like Baptists. We have the same vocabulary. Same emphasis on “what the data shows.” On the outside, it's almost impossible to tell the two apart in a meeting.
# Why Partnerships Work So Well
This is where the Yandle framework really clicks. Baptists provide authenticity. If someone who is truly committed to evidence-based reasoning supports a decision, it lowers the political costs for everyone to go along with it. The smugglers rode that wave, using the Baptist's credibility as a shield for the result they wanted all along.
And here's the kicker: Baptists often don't see themselves as part of a coalition. They think the decision was made right because, in their view, the data really points that way. They looked at the numbers honestly and came to a conclusion. The bootlegger has just made sure that the right numbers are the ones on the table.
# Learning to Separate Them
So what can you really do? Start by looking at what happens when the data contradicts someone's preferred outcome. Baptists will join them. They will ask follow-up questions, revisit assumptions, and perhaps even change direction. Bootleggers will circulate. They will redo the query, remove a metric, or decide that the data “doesn't paint the full picture.”
Similarly, pay attention to who presents the data versus who chooses what data is presented. There is a significant difference between someone who analyzes all available evidence and someone who selects a subset of it.
You also have to ask yourself if the analysis process was a real experiment or if the conclusion was drawn before the data was released. You won't always be able to tell them apart.
The whole point of this alliance is that it is difficult to distinguish between the two. But being flexible is already a huge advantage, because many people in many organizations never consider that their “data-driven” culture may be running on two very different engines at the same time.
# Final thoughts
Yandle's framework is built on regulatory economics, but the pattern it describes is universal. Wherever decisions have moral or intellectual authority, there will be people who believe in the principle and people who use the cover it provides. The data-driven culture is also different.
The best defense you have is simple: always want to know who benefits from the decision, not just what the numbers say. Because the numbers can be real, the analysis can be heard, and everything can still be a bootlegger's dream. Data optimization means asking “why this data?” every time you ask “what is this data?”
Here is Davies is a software developer and technical writer. Before devoting his career full-time to technical writing, he managed—among other interesting things—to work as a lead programmer at Inc. 5,000 branding whose clients include Samsung, Time Warner, Netflix, and Sony.
