# Entry
Every organization loves to call itself “data-driven.” It has become the gold standard of credibility, something you say to silence dissent in a meeting. But there is something worth dwelling on for a moment: the phrase “according to data analytics” may come from two very different places.
One of them is true curiosity. The second is someone who already knows what they want and has started looking for a number to back it up.
And the weird part? Both of these people end up pushing for the same decision, using the same language and sitting on the same side of the table. This coalition is more common than you might think, and it has a name.
# Traders and Baptists
In 1983, regulatory economist Bruce Yandle introduced a concept he called “Bootleggers and Baptists”. The idea came from observations about Sunday alcohol laws in the American South. Baptists pushed for these laws on moral grounds. They believed that restricting Sunday alcohol sales was right. Meanwhile, smugglers loved the exact same regulations because they eliminated legal competition for one day.
Both groups he wanted the same result, but for completely different reasons. Baptists provided moral cover, a justification to the public that politicians could point to. Criminals worked behind the scenes, quietly profiting from the results. Yandle noted that these unlikely coalitions tend to produce better regulatory outcomes than either group could achieve on its own.
It’s a powerful framework. It maps the world of data and analytics with uncomfortable precision.
In every data-savvy organization, you will find people who really try to make their decisions influence the evidence. These are your Baptists. They want cleaner data pipelines, better dashboards, and more stringent A/B testing. They push for statistical significance not because it serves their purposes, but because they believe that better data leads to better results.
These people are basic to recognize. They are the ones who change their minds when the data contradicts their hypotheses. They have no problem saying, “I was wrong” or “we need more information before we move.” They treat data like a flashlight in a gloomy room—something that helps everyone see more clearly, even if what it reveals is uncomfortable.
Data Baptists I really believe in the principle regardless of the data structure. And it is this belief that makes them useful to smugglers.
Now get to know the other side. These are people who have already reached their conclusions and reverse engineer the data history to support it. They are fluent in the language of evidence. They can quote numbers, refer to dashboards and present findings in the form of polished slides. However, the analytical process they used was never truly open. The destination was decided before the journey began.
Data bootleggers do things like choosing timeframes that support their preferred trend. They will choose indicators that flatter their initiative, silently ignoring those that do not. They will rely on correlation when it suits them and reject it when it does not. And they rarely, if ever, present data that contradicts their position.
Let’s say someone insists on AI-generated ads. They will check the click-through rates from the two-week test and call it a win. What they fail to mention is that bounce rates have doubled, time on site has dropped, and the cost of acquiring campaigns has actually increased. Sure, AI ads bring clicks. But the same goes for misleading thumbnails. The full picture tells a completely different story and that’s why they don’t show the full picture.
What makes them effective is that they sound exactly like Baptists. Same vocabulary. Same emphasis on “what the data shows.” From the outside, it’s almost impossible to tell the two apart when they meet.
# Why the coalition works so well
This is where the Yandle framework really impresses. Baptists provide legitimacy. When someone truly committed to evidence-based thinking supports a decision, this lowers the political costs for everyone else. Illegal traffickers are riding this wave, using the Baptist’s credibility as a cover to achieve the result they wanted all along.
And here’s the problem: Baptists often don’t realize they’re part of a coalition. They believe that the decision was made on a substantive basis because, from their point of view, the data actually indicated this. In good faith, they looked at the numbers and came to a conclusion. Bootlegger simply made sure the right numbers were on the table.
# Learn to tell them apart
So what can you actually do? Start by watching what happens when the data contradicts someone’s preferred outcome. The Baptists will take care of it. They will ask follow-up questions, revisit assumptions, and maybe even change direction. Bootleggers will rotate. They rephrase the question, change the metric, or suddenly decide that the data “does not paint the full picture.”
Similarly, pay attention to who presents the data compared to who chooses which data is presented. There is a significant difference between someone who analyzes all available evidence and someone who deals with a subset of it.
You also need to ask yourself whether the analytical process was truly exploratory in nature, or whether conclusions were reached before the data was even extracted. You won’t always be able to tell them apart.
The whole point of coalitions is that they are demanding to tell apart. However, being aware of this vigorous is already a significant advantage, because most people in most organizations haven’t even considered that their “data-driven” culture can run on two very different engines at the same time.
# Final thoughts
The Yandle framework was built with regulatory economics in mind, but the pattern it describes is universal. Wherever decisions have moral or intellectual legitimacy, there will be people who believe in this principle and those who take advantage of the cover it provides. Data-driven culture is no exception.
The best defense you have is uncomplicated: be curious about who will benefit from the decision, not just what the numbers say. Because the numbers can be real, the analysis can be solid, and the whole thing can still be a smuggler’s dream. Good data practice means asking “why this data?” just as often as you ask “what does this data say?”
Nahla Davies is a programmer and technical writer. Before devoting herself full-time to technical writing, she managed, among other intriguing things, to serve as lead programmer for a 5,000-person experiential branding organization whose clients include: Samsung, Time Warner, Netflix and Sony.