I immediately had a pretty good idea what had happened. They were probably preparing the poached eggs ahead of time, refrigerating them, and then reheating them to handle brunch volume more efficiently. And I get that. Brunch gets busy. Eggs Benedict is labor-intensive. Prepping components ahead of time is a perfectly reasonable way to improve throughput. But somebody still has to make sure the customer gets a warm egg.
Nobody did. And that is where efficiency and quality assurance collide.
The process was probably designed to move food through the kitchen faster. It worked. Right up until it didn’t. And that made me think about data and analytics, because I guess far too many things make me think of data and analytics.
We talk constantly about QA in software. We test applications, validate releases, check bugs, verify functionality. But data and analytics QA often gets much less attention.
Too often, the test is basically: Did the query run? Did the dashboard load? Did the numbers reconcile?
Those things matter. But they are not enough. An analytics process can be technically correct, efficient, automated, repeatable, and still fail completely at the thing that matters most: Did it actually answer the question the business needs answering? That's the first level of analytics QA.
The eggs Benedict failed that test. The kitchen process may have been efficient. The plate may have arrived quickly. The ingredients may all have been present. But the eggs were refrigerator cold.
Data teams can do exactly the same thing. They can automate transformations, standardize reports, reuse templates, follow design rules, and process enormous amounts of information very efficiently. Then they hand the business something that does not actually answer the question. That is not success. That is a fast, efficient failure.
There is a tendency among data propeller-heads to believe there is one correct way to model something, one correct way to present something, one correct visualization, one correct architecture. And usually the “correct” way happens to be the way they already know how to do it or “the way we have always done it.” Neither is QA.
QA requires checking the result against the actual need. And in analytics, that means knowing your audience. A CFO may interpret information differently from a regional sales manager. A service director may need completely different detail from an executive. Someone who has lived with a process for twenty years may understand a metric very differently from someone looking at it for the first time.
So you have to talk to the people who are actually going to use what you build. Not just at the beginning. Not just when you think you are finished. Throughout the process. Show them things. Ask what they think the numbers mean. Watch what they focus on. Watch what they ignore. Find out whether the thing you are building actually helps them make a better decision. That is QA too.
Then there is a second question: Does the result go beyond the literal request and solve the real problem? This does not mean piling on features nobody asked for. It means understanding why the person asked the question in the first place. The best analytics work does not merely satisfy the specification. It understands the intent behind it.
Back to brunch. I told the server the eggs were cold. At first she was pretty casual about it, but I asked her to touch the edge of an egg white. Her eyes went wide. I was completely calm about it. I knew what had probably happened and just asked for another plate. She was great about it and said she would have it remade. She also said she would send the manager over. I told her not to bother. Really. It was fine. She said the manager would come anyway.
The replacement came out quickly. Maybe too quickly. One egg was basically smashed. The other looked strange. The plating was a mess. But the eggs were warm. Then I noticed the potatoes were completely different.
The first plate had plain potatoes. The second had onions and peppers all over them. Same order. Different result. That's another QA failure. (The dish was pretty good regardless, so I picked out the onions and peppers and kept eating. This was not worth a tableside uprising, especially with bottomless mimosas in play.) The immediate defect had been corrected, but the process still had no control over the final result.
And the manager? I think the person who brought the second plate may have been the manager. But if she was, she never introduced herself, never acknowledged the problem, and never asked whether the replacement was okay. Which brings us to the third level of QA: If you miss, do you merely fix it — or do you make it right? Those are not the same thing.
Replacing cold eggs with warm eggs fixes the immediate defect. Making it right means checking the replacement before it leaves the kitchen, making sure the whole order is correct, and confirming with the customer that the problem is actually resolved, not merely patched.
The same thing applies in analytics. If a calculation is wrong, fixing the formula may not be enough. Who saw the bad result? Who acted on it? Did someone make a decision based on it? Management’s job does not end when the bug is closed. It ends when the problem is actually made right. That requires QA. It also requires humility.
Efficiency is valuable.
Automation is valuable.
Standardization and best practices are valuable.
But none of them matter if the output does not serve the person on the other end. The best process in the world still needs somebody to ask: Did this actually work?
Because whether you are serving brunch or building an executive analytics platform, the real test is not whether the process completed successfully. It is whether the person on the other end got what they actually needed — or whether, in the name of efficiency, they got cold eggs.