The Cost of Perfection
Ever been in a college dorm or a fraternity house on a Sunday morning where no one’s complaining about how it looks? However scary it might be to you and I … it’s clean enough for them.
Yes, the same can be said for your data. It depends on how you wish to use it, and what the outcome you're looking for is. Even the fraternity house looks perfect and smells like fresh lemons the day that the parents (and their checkbooks) come to visit.
Accuracy vs. Precision
Instead of clean vs. dirty data, marketers do well when they consider how accurate the data is for a specific purpose, vs. how much precision it could produce.
Big Data being “big,” we simply don’t need to hit the bulls-eye every single time; which is critical, because that’s not likely.
If the collection methods are logical and reasonable, even if only 90 percent right … that’s 10 fails out of 100 tries … we can still have precision for a given purpose.
This example from Jim De Novo’s "Drilling Down" makes a great example of why accuracy (AKA, “clean” data ) isn’t the only thing that matters — precision is what matters.
In the bulls-eye on the left, we keep aiming for the perfect bulls-eye. We keep missing, however, and how much we miss by, or where the next shot will land is hard to say.
In the bulls-eye on the right, the attempts are precise. That is, they do not hit the bulls-eye consistently — but they are also consistently near the bulls-eye. We can realistically expect to know where the next dart will land.
Perfect Is the Enemy of Good
Similarly with data, we don’t need some theoretical "perfection" to be practical. When we have a large data-set (and in the digital age, they are usually sufficiently large) with some level of random error in it, we have precision, and we can predict the customer will buy more bath soap than perfume.