# Introduction
Every organization likes to call itself “data-driven.” It’s become the gold standard of credibility, the thing you say to shut down disagreements in a meeting. But here’s something worth sitting with for a moment: the phrase “According to data analytics“Can come from two completely different places.
One is genuine curiosity. The second is someone who already knows what they want and is looking for a number to support it.
And the weird part? Both of them are sitting on the same side of the table, using the same language, and pushing for the same decision. That alliance is more common than you think, and it has a name.
# Bootleggers and Baptists
In 1983, regulatory economist Bruce Yandle He introduced a concept called “Bootleggers and Baptists”. The idea came from an observation about Sunday liquor laws in the American South. Baptists insisted on those laws on moral grounds. He believed that restricting the sale of liquor on Sundays was the right move. Meanwhile, bootleggers loved the exact same laws because they eliminated their legal competition for a day.
both groups I wanted the same results, but for completely different reasons. Baptists provided a moral cover, a public-facing justification to which politicians could point. Bootleggers worked behind the scenes and quietly profited from the outcome. Yandle’s insight was that these unlikely alliances produce more successful regulatory outcomes than any group could achieve alone.
This is a powerful framework. And it maps onto the world of data and analytics with uncomfortable precision.
In any data-literate organization, you will find people who are actually trying to let the evidence guide their decisions. These are your Baptists. They want cleaner data pipelines, better dashboards, more rigorous A/B testing. They emphasize statistical significance not because it serves their agenda, but because they believe that better data leads to better results.
It is easy to recognize these people. It is only when the data contradicts their hypothesis that they change their minds. They are comfortable saying “I was wrong” or “We need more information before we proceed.” They treat data like a flashlight in a dark room – something that helps everyone see more clearly, even if what it reveals is uncomfortable.
baptist of data really believe in the theory, It doesn’t matter how the data is structured. And this belief makes them useful to liquor smugglers.
Now meet the other side. These are people who already have a conclusion and are reverse-engineering the data story to support it. He is well versed in the language of evidence. They can cite numbers, reference dashboards, and present findings in a polished slide deck. But the analytical process he employed was never really open. The destination was decided even before the journey started.
data smugglers Do things like cherry-pick time ranges that support their favorite trend. They will choose metrics that promote their initiatives while quietly ignoring those that don’t. They will rely on correlation when it suits them and throw it away when it does not suit them. And they rarely, if ever, present data that argues against their position.
Let’s say someone is pushing AI-generated ad creative. They’ll take the click-through rates from a two-week test and call it a win. What they won’t mention is that bounce rates doubled, time on page decreased and the campaign’s cost per acquisition actually increased. AI ads definitely got clicks. But so do misleading thumbnails. The whole picture tells a very different story, and that’s why they don’t show the whole picture.
What makes them effective is that they sound exactly like Baptists. Same terminology. The same emphasis is placed on “what the data shows.” From the outside, it is almost impossible to tell the two apart in a meeting.
# Why alliances work so well
This is where Yandle’s framework really comes into play. Baptists provide validity. When someone with a genuine commitment to evidence-based thinking supports a decision, This reduces the political cost of going along with everyone else.. The bootleggers ride the wave, using Baptists’ credibility as a guise to get the result they always wanted.
And here’s the point: Baptists often don’t realize they’re part of a coalition. He believes the decision was made on the merits, because from his vantage point, that’s exactly what the data pointed towards. He looked at the numbers in good faith and came to a conclusion. The bootlegger simply made sure the correct numbers were on the table.
# learn to tell them apart
So what can you actually do? start by looking What happens when data contradicts someone’s preferred outcome. Baptists will engage in this. They’ll ask follow-up questions, revisit assumptions, maybe even change direction. Bootleggers will roam. They will reframe the question, change the metric, or suddenly decide that the data “doesn’t paint the whole picture.”
same way, Pay attention to who submits the data Versus who chooses what data will be presented. There is a meaningful difference between a person analyzing all the available evidence and a person producing a subset of it.
You should also ask yourself whether the analytical process was truly exploratory or whether the conclusions were already being drawn before the data were extracted. You won’t always be able to tell them apart.
The whole point of the alliance is that it is difficult to differentiate between the two. But being aware of dynamics is already a significant advantage, because most people in most organizations have never considered that their “data-driven” culture can run on two very different engines at the same time.
# final thoughts
Yandle’s framework was created for regulatory economics, but the patterns it describes are universal. Wherever decisions have moral or intellectual validity, there will be those who believe in the principle and those who take advantage of the cover it provides. Data-driven culture is no exception.
The best defense you have is simple: Be curious about who benefits from a decision, not just what the numbers say. Because the numbers may be real, the analysis may be solid, and the whole thing may still be a bootlegger’s dream. Good data practice means asking “Why this data?” How often do you ask “What does this data say?”
Nahla Davis Is a software developer and technical writer. Before devoting his work full-time to technical writing, he worked for Inc., among other interesting things. Managed to work as a lead programmer at a 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.
